Merge branch 'ralph/distributed-gguf-runtime' of https://git.d-popov.com/popov/neuron-tai into ralph/distributed-gguf-runtime

.opencode/opencode.json

@@ -0,0 +1,5 @@
+{
+  "plugin": [
+    ".opencode/plugins/graphify.js"
+  ]
+}

.opencode/plugins/graphify.js

@@ -0,0 +1,30 @@
+// graphify OpenCode plugin
+// Injects a knowledge graph reminder before bash tool calls when the graph exists.
+//
+// IMPORTANT: keep the reminder string free of backticks and $(...) constructs.
+// The hook prepends `echo "<reminder>" && <cmd>` to the user's bash command;
+// backticks inside the double-quoted echo trigger bash command substitution,
+// which both corrupts tool output and silently executes the very graphify
+// command we are only suggesting. Plain words render fine in opencode's TUI.
+import { existsSync } from "fs";
+import { join } from "path";
+
+export const GraphifyPlugin = async ({ directory }) => {
+  let reminded = false;
+
+  return {
+    "tool.execute.before": async (input, output) => {
+      if (reminded) return;
+      if (!existsSync(join(directory, "graphify-out", "graph.json"))) return;
+
+      if (input.tool === "bash") {
+        // ';' not '&&' — Windows PowerShell 5.1 rejects '&&' as a statement
+        // separator, breaking the first bash command of the session (#1646).
+        output.args.command =
+          'echo "[graphify] knowledge graph at graphify-out/. For focused questions, run graphify query with your question (scoped subgraph, usually much smaller than GRAPH_REPORT.md) instead of grepping raw files. Read GRAPH_REPORT.md only for broad architecture context." ; ' +
+          output.args.command;
+        reminded = true;
+      }
+    },
+  };
+};

.opencode/skills/graphify/.graphify_version

@@ -0,0 +1 @@
+0.9.29

.opencode/skills/graphify/SKILL.md

@@ -0,0 +1,694 @@
+---
+name: graphify
+description: "Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools."
+---
+
+# /graphify
+
+Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.
+
+## Usage
+
+```
+/graphify                                             # full pipeline on current directory (HTML viz; add --obsidian for a vault)
+/graphify <path>                                      # full pipeline on specific path
+/graphify https://github.com/<owner>/<repo>           # clone repo then run full pipeline on it
+/graphify https://github.com/<owner>/<repo> --branch <branch>  # clone a specific branch
+/graphify <url1> <url2> ...                           # clone multiple repos, build each, merge into one cross-repo graph
+/graphify <path> --mode deep                          # thorough extraction, richer INFERRED edges
+/graphify <path> --update                             # incremental - re-extract only new/changed files
+/graphify <path> --directed                            # build directed graph (preserves edge direction: source→target)
+/graphify <path> --whisper-model medium                # use a larger Whisper model for better transcription accuracy
+/graphify <path> --cluster-only                       # rerun clustering on existing graph
+/graphify <path> --no-viz                             # skip visualization, just report + JSON
+/graphify <path> --html                               # (HTML is generated by default - this flag is a no-op)
+/graphify <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
+/graphify <path> --graphml                            # export graph.graphml (Gephi, yEd)
+/graphify <path> --neo4j                              # generate graphify-out/cypher.txt for Neo4j
+/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
+/graphify <path> --falkordb                           # generate graphify-out/cypher.txt for FalkorDB
+/graphify <path> --falkordb-push falkordb://localhost:6379   # push directly to FalkorDB
+/graphify <path> --mcp                                # start MCP stdio server for agent access
+/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
+/graphify <path> --wiki                               # build agent-crawlable wiki (index.md + one article per community)
+/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project  # write vault to custom path (e.g. existing vault)
+/graphify add <url>                                   # fetch URL, save to ./raw, update graph
+/graphify add <url> --author "Name"                   # tag who wrote it
+/graphify add <url> --contributor "Name"              # tag who added it to the corpus
+/graphify query "<question>"                          # BFS traversal - broad context
+/graphify query "<question>" --dfs                    # DFS - trace a specific path
+/graphify query "<question>" --budget 1500            # cap answer at N tokens
+/graphify path "AuthModule" "Database"                # shortest path between two concepts
+/graphify explain "SwinTransformer"                   # plain-language explanation of a node
+```
+
+## What graphify is for
+
+Drop any folder of code, docs, papers, images, or video into graphify and get a queryable knowledge graph. Persistent across sessions, honest audit trail (EXTRACTED/INFERRED/AMBIGUOUS), community detection surfaces cross-document connections you wouldn't think to ask about.
+
+## What You Must Do When Invoked
+
+If the user invoked `/graphify --help` or `/graphify -h` (with no other arguments), print the contents of the `## Usage` section above verbatim and stop. Do not run any commands, do not detect files, do not default the path to `.`. Just print the Usage block and return.
+
+**Fast path — existing graph:** Before doing anything else, check whether `graphify-out/graph.json` exists. The expected location is `graphify-out/graph.json` relative to the **current working directory** (i.e. the project root where you are running commands). If it exists AND the user's request is a natural-language question about the codebase (e.g. "How does X work?", "What calls Y?", "Trace the data flow through Z") and NOT an explicit rebuild command (`--update`, `--cluster-only`, or a bare path/URL that implies fresh extraction): **skip Steps 1–5 entirely and jump straight to `## For /graphify query`.** Run `graphify query "<question>"` immediately. Do not run detect. Do not check corpus size. Do not ask the user to narrow. The graph is already built — use it.
+
+If no path was given, use `.` (current directory). Do not ask the user for a path.
+
+If the path argument starts with `https://github.com/` or `http://github.com/`, treat it as a GitHub URL - run Step 0 before anything else, then continue with the resolved local path.
+
+Follow these steps in order. Do not skip steps.
+
+### Step 0 - GitHub repos and multi-path merge (only if a URL or several paths)
+
+Only when the path is one or more `https://github.com/...` URLs, or several local subfolders to merge. See `references/github-and-merge.md` for the clone, cross-repo merge, and monorepo flow, then continue with the resolved local path. A plain local path skips this step.
+
+### Step 1 - Ensure graphify is installed
+
+```bash
+# Detect the correct Python interpreter (handles uv tool, pipx, venv, system installs)
+PYTHON=""
+GRAPHIFY_BIN=$(which graphify 2>/dev/null)
+# 1. uv tool installs — most reliable on modern Mac/Linux
+if [ -z "$PYTHON" ] && command -v uv >/dev/null 2>&1; then
+    _UV_PY=$(uv tool run --from graphifyy python -c "import sys; print(sys.executable)" 2>/dev/null)
+    if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
+fi
+# 2. Read shebang from graphify binary (pipx and direct pip installs)
+if [ -z "$PYTHON" ] && [ -n "$GRAPHIFY_BIN" ]; then
+    _SHEBANG=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
+    case "$_SHEBANG" in
+        *[!a-zA-Z0-9/_.@-]*) ;;
+        *) "$_SHEBANG" -c "import graphify" 2>/dev/null && PYTHON="$_SHEBANG" ;;
+    esac
+fi
+# 3. Fall back to python3
+if [ -z "$PYTHON" ]; then PYTHON="python3"; fi
+if ! "$PYTHON" -c "import graphify" 2>/dev/null; then
+    if command -v uv >/dev/null 2>&1; then
+        uv tool install --upgrade graphifyy -q 2>&1 | tail -3
+        _UV_PY=$(uv tool run --from graphifyy python -c "import sys; print(sys.executable)" 2>/dev/null)
+        if [ -n "$_UV_PY" ]; then PYTHON="$_UV_PY"; fi
+    else
+        "$PYTHON" -m pip install graphifyy -q 2>/dev/null \
+          || "$PYTHON" -m pip install graphifyy -q --break-system-packages 2>&1 | tail -3
+    fi
+fi
+# Write interpreter path for all subsequent steps (persists across invocations)
+mkdir -p graphify-out
+"$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w', encoding='utf-8').write(sys.executable)"
+# Save scan root so `graphify update` (no args) knows where to look next time
+echo "$(cd INPUT_PATH && pwd)" > graphify-out/.graphify_root
+```
+
+If the import succeeds, print nothing and move straight to Step 2.
+
+**In every subsequent bash block, replace `python3` with `$(cat graphify-out/.graphify_python)` to use the correct interpreter.**
+
+### Step 2 - Detect files
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from graphify.detect import detect
+from pathlib import Path
+result = detect(Path('INPUT_PATH'))
+print(json.dumps(result, ensure_ascii=False))
+" > graphify-out/.graphify_detect.json
+```
+
+Replace INPUT_PATH with the actual path the user provided. Do NOT cat or print the JSON - read it silently and present a clean summary instead:
+
+```
+Corpus: X files · ~Y words
+  code:     N files (.py .ts .go ...)
+  docs:     N files (.md .txt ...)
+  papers:   N files (.pdf ...)
+  images:   N files
+  video:    N files (.mp4 .mp3 ...)
+```
+
+Omit any category with 0 files from the summary.
+
+Then act on it:
+- If `total_files` is 0: stop with "No supported files found in [path]."
+- If `skipped_sensitive` is non-empty: report the count and list the skipped file names, so a wrongly-flagged source or doc is visible and can be renamed or moved (#2106).
+- If `total_words` > 2,000,000 OR `total_files` > 500: show the warning. Then compute the top 5 first-level subdirectories by file count:
+  - Read `scan_root` from the detect JSON (always an absolute path to the resolved INPUT_PATH).
+  - Concatenate all file lists across all types (`code`, `document`, `paper`, `image`, `video`).
+  - Filter out any path that starts with `scan_root + "/graphify-out/"` to exclude converted sidecars.
+  - For each file, strip the `scan_root` prefix and take the first path component. Files directly in `scan_root` with no subdirectory count as `(root)`.
+  - If all files are in `(root)` with no subdirectories, do not ask to narrow — no subfolders exist. Instead suggest `--no-cluster` to skip the expensive clustering step and proceed.
+  - Otherwise rank by count, show the top 5 with file counts, then ask which subfolder to run on. Wait for the user's answer before proceeding.
+- Otherwise: proceed directly to Step 2.5 if video files were detected, or Step 3 if not.
+
+### Step 2.5 - Video and audio (only if video files detected)
+
+Skip this step entirely if `detect` returned zero `video` files. When the corpus has video or audio, see `references/transcribe.md` to transcribe them to text first, then treat the transcripts as doc files in Step 3.
+
+### Step 3 - Extract entities and relationships
+
+**Before starting:** note whether `--mode deep` was given. You must pass `DEEP_MODE=true` to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.
+
+This step has two parts: **structural extraction** (deterministic, free) and **semantic extraction** (LLM, costs tokens).
+
+> **graphify needs no API key. Never ask the user for one, and never block on one.** Code is extracted structurally (AST) with no LLM and no key at all — a code-only corpus (the common `/graphify .` on a repo) skips semantic extraction entirely, so it needs nothing here: go straight to Part A and skip Part B. Semantic extraction (only for docs, papers, and images) uses Gemini **only if** `GEMINI_API_KEY`/`GOOGLE_API_KEY` is already set; otherwise the host agent itself is the LLM. graphify does **not** read `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or any other provider key. If you catch yourself about to prompt for, wait on, or stop because of a missing API key, that is a misread of this skill — proceed without one.
+
+**Before semantic extraction:** check whether `GEMINI_API_KEY` or `GOOGLE_API_KEY` is set. If neither is set, print this one-liner to the user:
+> Tip: set `GEMINI_API_KEY` or `GOOGLE_API_KEY` to use Gemini for semantic extraction (`pip install 'graphifyy[gemini]'`).
+
+Print it once, then continue — do not wait for the user to supply a key. If `GEMINI_API_KEY` or `GOOGLE_API_KEY` IS set, use `graphify.llm.extract_corpus_parallel(files, backend="gemini")` for semantic extraction instead of dispatching subagents. The default Gemini model is `gemini-3-flash-preview`; set `GRAPHIFY_GEMINI_MODEL` or pass `--model` in headless CLI flows to override it.
+
+> **No other API keys are read.** When `GEMINI_API_KEY`/`GOOGLE_API_KEY` are unset, semantic extraction falls to the host agent itself — the running session is the LLM. On a host that dispatches subagents (e.g. Claude Code), dispatch them as written in Part B. On a host that runs the CLI directly in a terminal and cannot dispatch subagents, do not stall: a code-only corpus has no semantic work, so write the empty semantic file (Part B "Fast path") and continue to Part C; for a corpus with docs/papers/images, either set a Gemini key or extract those inline yourself, but in no case prompt for `ANTHROPIC_API_KEY` — that prompt is a misread of this skill.
+
+**Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.**
+
+Note: Parallelizing AST + semantic saves 5-15s on large corpora. AST is deterministic and fast; start it while subagents are processing docs/papers.
+
+#### Part A - Structural extraction for code files
+
+For any code files detected, run AST extraction in parallel with Part B subagents:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from graphify.extract import collect_files, extract
+from pathlib import Path
+import json
+
+code_files = []
+detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+for f in detect.get('files', {}).get('code', []):
+    code_files.extend(collect_files(Path(f)) if Path(f).is_dir() else [Path(f)])
+
+if code_files:
+    result = extract(code_files, cache_root=Path('INPUT_PATH'))
+    Path('graphify-out/.graphify_ast.json').write_text(json.dumps(result, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+    print(f'AST: {len(result[\"nodes\"])} nodes, {len(result[\"edges\"])} edges')
+else:
+    Path('graphify-out/.graphify_ast.json').write_text(json.dumps({'nodes':[],'edges':[],'input_tokens':0,'output_tokens':0}, ensure_ascii=False), encoding=\"utf-8\")
+    print('No code files - skipping AST extraction')
+"
+```
+
+#### Part B - Semantic extraction (parallel subagents)
+
+**Fast path:** If detection found zero docs, papers, and images (code-only corpus), skip Part B entirely and go straight to Part C. AST handles code - there is nothing for semantic subagents to do. **First write an empty semantic file** so Part C's merge has its input (it reads `.graphify_semantic.json` unconditionally; without this a code-only run hits `FileNotFoundError`):
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+Path('graphify-out/.graphify_semantic.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
+"
+```
+
+**MANDATORY: You MUST use the Agent tool here. Reading files yourself one-by-one is forbidden - it is 5-10x slower. If you do not use the Agent tool you are doing this wrong.**
+
+Before dispatching subagents, print a timing estimate:
+- Load `total_words` and file counts from `graphify-out/.graphify_detect.json`
+- Estimate agents needed: `ceil(uncached_non_code_files / 22)` (chunk size is 20-25)
+- Estimate time: ~45s per agent batch (they run in parallel, so total ≈ 45s × ceil(agents/parallel_limit))
+- Print: "Semantic extraction: ~N files → X agents, estimated ~Ys"
+
+**Step B0 - Check extraction cache first**
+
+Before dispatching any subagents, check which files already have cached extraction results:
+
+SPEC_PATH below is the **absolute** path of the `references/extraction-spec.md` that ships beside this SKILL.md — the same file Step B2 loads and hands to every subagent. It is the extraction prompt, so cache entries are attributed to it: when a graphify upgrade changes the prompt, entries produced by the old one are re-extracted instead of replayed, and unchanged prompts keep their entries (#1939). Substitute the real path in both Step B0 and Step B3 — pass the same one to each, and do not drop the argument.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from graphify.cache import check_semantic_cache
+from pathlib import Path
+
+detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+# Only content files go to semantic extraction. Code is already covered structurally
+# by the AST pass (Part A); flattening every category here makes subagents re-read
+# every source file (#1392). Video is transcribed to a document in Step 2.5 first.
+all_files = [f for cat in ('document', 'paper', 'image') for f in detect['files'].get(cat, [])]
+
+cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files, root='INPUT_PATH', prompt_file='SPEC_PATH')
+
+# Always (re)write the cache file: write hits, else DELETE any leftover from a prior
+# run so Part C never merges a stale .graphify_cached.json (#1392).
+if cached_nodes or cached_edges or cached_hyperedges:
+    Path('graphify-out/.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}, ensure_ascii=False), encoding=\"utf-8\")
+else:
+    Path('graphify-out/.graphify_cached.json').unlink(missing_ok=True)
+Path('graphify-out/.graphify_uncached.txt').write_text('\n'.join(uncached), encoding=\"utf-8\")
+print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
+"
+```
+
+Only dispatch subagents for files listed in `graphify-out/.graphify_uncached.txt`. If all files are cached, skip to Part C directly.
+
+**Step B1 - Split into chunks**
+
+Load files from `graphify-out/.graphify_uncached.txt`. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). When splitting, group files from the same directory together so related artifacts land in the same chunk and cross-file relationships are more likely to be extracted.
+
+**Step B2 - Dispatch ALL subagents in a single message (OpenCode)**
+
+> **OpenCode platform:** Uses `@mention` dispatch instead of the Agent tool. All mentions in a single message run in parallel.
+
+Dispatch one `@mention` per chunk — ALL in the same response:
+
+```
+@agent Chunk CHUNK_NUM of TOTAL_CHUNKS: [extraction prompt with FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE substituted]
+
+@agent Chunk 2 of TOTAL_CHUNKS: [next chunk]
+```
+
+Wait for all agents to return. Parse each response as JSON. Accumulate nodes/edges/hyperedges across all results and write to `graphify-out/.graphify_semantic_new.json`. If the `@agent` path cannot write chunk files, fall back to the serial path that writes each `graphify-out/.graphify_chunk_NN.json` before merge.
+
+Subagent prompt template:
+
+See `references/extraction-spec.md` for the exact subagent prompt (JSON schema, node-ID rules, confidence rubric, hyperedge, and vision rules). Load it only here, only when at least one chunk holds a doc, paper, or image; a pure-code corpus has skipped Part B and never reads it. Pass each agent that prompt verbatim with FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, and DEEP_MODE substituted.
+
+**Step B3 - Collect, cache, and merge**
+
+Wait for all subagents. For each result:
+- Check that `graphify-out/.graphify_chunk_NN.json` exists on disk — this is the success signal
+- If the file exists and contains valid JSON with `nodes` and `edges`, include it and save to cache
+- If the file is missing, the subagent was likely dispatched as read-only (Explore type) — print a warning: "chunk N missing from disk — subagent may have been read-only. Re-run with general-purpose agent." Do not silently skip.
+- If a subagent failed or returned invalid JSON, print a warning and skip that chunk - do not abort
+
+If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure `subagent_type="general-purpose"` is used.
+
+Merge all chunk files into `.graphify_semantic_new.json`. **After each Agent call completes, read the real token counts from the Agent tool result's `usage` field and write them back into the chunk JSON before merging** — the chunk JSON itself always has placeholder zeros. Then run:
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json, glob
+from pathlib import Path
+
+chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json'))
+all_nodes, all_edges, all_hyperedges = [], [], []
+total_in, total_out = 0, 0
+for c in chunks:
+    d = json.loads(Path(c).read_text(encoding=\"utf-8\"))
+    all_nodes += d.get('nodes', [])
+    all_edges += d.get('edges', [])
+    all_hyperedges += d.get('hyperedges', [])
+    total_in += d.get('input_tokens', 0)
+    total_out += d.get('output_tokens', 0)
+Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({
+    'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges,
+    'input_tokens': total_in, 'output_tokens': total_out,
+}, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens')
+"
+```
+
+Save new results to cache. Pass the same SPEC_PATH as Step B0 — it stamps each entry with the prompt that produced it, and a write under a different prompt than the read lands where the next run won't look (#1939):
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from graphify.cache import save_semantic_cache
+from pathlib import Path
+
+new = json.loads(Path('graphify-out/.graphify_semantic_new.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
+uncached = [line for line in Path('graphify-out/.graphify_uncached.txt').read_text(encoding=\"utf-8\").splitlines() if line]
+saved = save_semantic_cache(new.get('nodes', []), new.get('edges', []), new.get('hyperedges', []), root='INPUT_PATH', allowed_source_files=uncached, prompt_file='SPEC_PATH')
+print(f'Cached {saved} files')
+"
+```
+
+Merge cached + new results into `graphify-out/.graphify_semantic.json`:
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+
+cached = json.loads(Path('graphify-out/.graphify_cached.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_cached.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
+new = json.loads(Path('graphify-out/.graphify_semantic_new.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
+
+all_nodes = cached['nodes'] + new.get('nodes', [])
+all_edges = cached['edges'] + new.get('edges', [])
+all_hyperedges = cached.get('hyperedges', []) + new.get('hyperedges', [])
+seen = set()
+deduped = []
+for n in all_nodes:
+    if n['id'] not in seen:
+        seen.add(n['id'])
+        deduped.append(n)
+
+merged = {
+    'nodes': deduped,
+    'edges': all_edges,
+    'hyperedges': all_hyperedges,
+    'input_tokens': new.get('input_tokens', 0),
+    'output_tokens': new.get('output_tokens', 0),
+}
+Path('graphify-out/.graphify_semantic.json').write_text(json.dumps(merged, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+print(f'Extraction complete - {len(deduped)} nodes, {len(all_edges)} edges ({len(cached[\"nodes\"])} from cache, {len(new.get(\"nodes\",[]))} new)')
+"
+```
+Clean up temp files: `rm -f graphify-out/.graphify_cached.json graphify-out/.graphify_uncached.txt graphify-out/.graphify_semantic_new.json`
+
+#### Part C - Merge AST + semantic into final extraction
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from pathlib import Path
+
+ast = json.loads(Path('graphify-out/.graphify_ast.json').read_text(encoding=\"utf-8\"))
+sem = json.loads(Path('graphify-out/.graphify_semantic.json').read_text(encoding=\"utf-8\"))
+
+# Merge: AST nodes first, semantic nodes deduplicated by id
+seen = {n['id'] for n in ast['nodes']}
+merged_nodes = list(ast['nodes'])
+for n in sem['nodes']:
+    if n['id'] not in seen:
+        merged_nodes.append(n)
+        seen.add(n['id'])
+
+merged_edges = ast['edges'] + sem['edges']
+merged_hyperedges = sem.get('hyperedges', [])
+merged = {
+    'nodes': merged_nodes,
+    'edges': merged_edges,
+    'hyperedges': merged_hyperedges,
+    'input_tokens': sem.get('input_tokens', 0),
+    'output_tokens': sem.get('output_tokens', 0),
+}
+Path('graphify-out/.graphify_extract.json').write_text(json.dumps(merged, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+total = len(merged_nodes)
+edges = len(merged_edges)
+print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(sem[\"nodes\"])} semantic)')
+"
+```
+
+### Step 4 - Build graph, cluster, analyze, generate outputs
+
+**Before starting:** the code blocks below pass `directed=IS_DIRECTED` to `build_from_json()`. Replace `IS_DIRECTED` with `True` if `--directed` was given (builds a `DiGraph` preserving edge direction source→target), otherwise `False` (the default undirected `Graph`). Substitute it the same way you substitute `INPUT_PATH` — do not leave the literal `IS_DIRECTED` in the code.
+
+```bash
+mkdir -p graphify-out
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from graphify.build import build_from_json
+from graphify.cluster import cluster, score_all
+from graphify.analyze import god_nodes, surprising_connections, suggest_questions
+from graphify.report import generate
+from graphify.export import to_json
+from pathlib import Path
+
+extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+
+# root= mirrors the --update runbook (#1361): relativize source_file to the same
+# base so the full build and incremental --update never drift apart on re-extract.
+G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
+# Guard BEFORE any write: an empty extraction must not clobber a good graph.json /
+# GRAPH_REPORT.md / analysis sidecar. Check immediately after build (#1392).
+if G.number_of_nodes() == 0:
+    print('ERROR: Graph is empty - extraction produced no nodes.')
+    print('Possible causes: all files were skipped, binary-only corpus, or extraction failed.')
+    raise SystemExit(1)
+communities = cluster(G)
+cohesion = score_all(G, communities)
+tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
+gods = god_nodes(G)
+surprises = surprising_connections(G, communities)
+labels = {cid: 'Community ' + str(cid) for cid in communities}
+# Placeholder questions - regenerated with real labels in Step 5
+questions = suggest_questions(G, communities, labels)
+
+# Export FIRST and honor the #479 shrink-guard: to_json returns False (writing
+# nothing) when the new graph is smaller than the existing graph.json. Only write
+# GRAPH_REPORT.md + the analysis sidecar when the graph was actually written, so
+# they never describe a graph that graph.json doesn't contain (#1392).
+wrote = to_json(G, communities, 'graphify-out/graph.json')
+if not wrote:
+    print('ERROR: refused to shrink graphify-out/graph.json (existing graph has more nodes; #479).')
+    print('If this shrink is intentional (you deleted files), re-run a full build with --force.')
+    raise SystemExit(1)
+report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, 'INPUT_PATH', suggested_questions=questions)
+Path('graphify-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
+analysis = {
+    'communities': {str(k): v for k, v in communities.items()},
+    'cohesion': {str(k): v for k, v in cohesion.items()},
+    'gods': gods,
+    'surprises': surprises,
+    'questions': questions,
+}
+Path('graphify-out/.graphify_analysis.json').write_text(json.dumps(analysis, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+print(f'Graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges, {len(communities)} communities')
+"
+```
+
+If this step prints `ERROR: Graph is empty`, stop and tell the user what happened - do not proceed to labeling or visualization.
+
+Replace INPUT_PATH with the actual path.
+
+### Step 4.5 - Graph health check (read-only integrity gate)
+
+A non-destructive diagnostic on the extraction, before labeling. It surfaces edge collapse, dangling/missing endpoints, and self-loops — the silent-corruption modes of incremental updates and AST/LLM id mismatches. Read-only; never aborts.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+from graphify.diagnostics import diagnose_extraction, format_diagnostic_report
+
+extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+summary = diagnose_extraction(extraction, directed=IS_DIRECTED, root='INPUT_PATH')
+print(format_diagnostic_report(summary))
+flags = [f'{summary[k]} {label}' for k, label in (
+    ('dangling_endpoint_edges', 'dangling-endpoint edges'),
+    ('missing_endpoint_edges', 'missing-endpoint edges'),
+    ('self_loop_edges', 'self-loop edges'),
+    ('directed_same_endpoint_collapsed_edges', 'collapsed (directed) edges'),
+    ('undirected_same_endpoint_collapsed_edges', 'collapsed (undirected) edges'),
+) if summary.get(k, 0)]
+print('GRAPH HEALTH WARNING: ' + '; '.join(flags) + ' - graph may be incomplete/corrupt.' if flags else 'Graph health: OK (no dangling/missing/collapsed edges).')
+"
+```
+
+Substitute `IS_DIRECTED` and `INPUT_PATH` as in Step 4. If a `GRAPH HEALTH WARNING` prints, surface it in the final summary (do not abort — the graph is still usable, but the integrity issue must be visible, per the Honesty Rules).
+
+### Step 5 - Label communities
+
+Read `graphify-out/.graphify_analysis.json`. For each community key, look at its node labels and write a 2-5 word plain-language name (e.g. "Attention Mechanism", "Training Pipeline", "Data Loading").
+
+Then regenerate the report and save the labels for the visualizer:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from graphify.build import build_from_json
+from graphify.cluster import score_all
+from graphify.analyze import god_nodes, surprising_connections, suggest_questions
+from graphify.report import generate
+from pathlib import Path
+
+extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+detection  = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+analysis   = json.loads(Path('graphify-out/.graphify_analysis.json').read_text(encoding=\"utf-8\"))
+
+# root= as in Step 4 / the --update runbook (#1361) — same base for node-key parity.
+G = build_from_json(extraction, root='INPUT_PATH', directed=IS_DIRECTED)
+communities = {int(k): v for k, v in analysis['communities'].items()}
+cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
+tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
+
+# LABELS - replace these with the names you chose above
+labels = LABELS_DICT
+
+# Regenerate questions with real community labels (labels affect question phrasing)
+questions = suggest_questions(G, communities, labels)
+
+report = generate(G, communities, cohesion, labels, analysis['gods'], analysis['surprises'], detection, tokens, 'INPUT_PATH', suggested_questions=questions)
+Path('graphify-out/GRAPH_REPORT.md').write_text(report, encoding=\"utf-8\")
+Path('graphify-out/.graphify_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()}, ensure_ascii=False), encoding=\"utf-8\")
+print('Report updated with community labels')
+"
+```
+
+Replace `LABELS_DICT` with the actual dict you constructed (e.g. `{0: "Attention Mechanism", 1: "Training Pipeline"}`).
+Replace INPUT_PATH with the actual path.
+
+### Step 6 - Generate Obsidian vault (opt-in) + HTML
+
+**Generate HTML always** (unless `--no-viz`). **Obsidian vault only if `--obsidian` was explicitly given** — skip it otherwise, it generates one file per node.
+
+If `--obsidian` was given:
+
+- If `--obsidian-dir <path>` was also given, pass it via `--dir`. Otherwise defaults to `graphify-out/obsidian`.
+
+```bash
+graphify export obsidian
+# or with custom dir: graphify export obsidian --dir ~/vaults/my-project
+```
+
+Generate the HTML graph (always, unless `--no-viz`):
+
+```bash
+graphify export html  # auto-aggregates to community view if graph > 5000 nodes
+# or: graphify export html --no-viz
+```
+
+### Steps 6b-8 - Wiki, Neo4j, FalkorDB, SVG, GraphML, MCP, benchmark (only on their flags)
+
+These run only when their flag is present (`--wiki`, `--neo4j`/`--neo4j-push`, `--falkordb`/`--falkordb-push`, `--svg`, `--graphml`, `--mcp`) or, for the token-reduction benchmark, when `total_words` exceeds 5,000. A default run with no export flags skips all of them. See `references/exports.md` for each one. Run any `--wiki` export before Step 9 cleanup so `.graphify_labels.json` is still available.
+
+---
+
+### Step 9 - Save manifest, update cost tracker, clean up, and report
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+from datetime import datetime, timezone
+from graphify.detect import save_manifest
+
+# Save manifest for --update
+detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+# In --update mode, 'all_files' carries the full corpus; 'files' is the changed
+# subset. Full-rebuild mode populates only 'files', so the fallback handles that.
+# root= relativizes the manifest keys to the scan root (same base as the build),
+# so the on-disk manifest is portable across clones/machines and a later --update
+# matches cached files instead of missing every one (#1417).
+#
+# Only stamp semantic files (docs/papers/images) that ACTUALLY produced output:
+# a detected file whose chunk failed or was omitted must stay unstamped so the
+# next --update re-queues it, otherwise it is marked done and its content is lost
+# forever (#2015). This mirrors the library extract path exactly
+# (cli._stamped_manifest_files + clear_semantic + scan_corpus); do not stamp the
+# raw corpus. Code files are always stamped (AST is deterministic); only semantic
+# types are gated on output.
+from graphify.cli import _stamped_manifest_files
+_corpus = detect.get('all_files') or detect['files']
+_manifest_files = _stamped_manifest_files(_corpus, extract, Path('INPUT_PATH'))
+# Files dispatched this run (the changed subset) but NOT stamped above still carry
+# a stale semantic_hash from a prior run; clear it so detect_incremental re-queues
+# them instead of reading them as unchanged (#1948).
+_sem_types = ('document', 'paper', 'image')
+_dispatched = {f for t, fl in detect['files'].items() if t in _sem_types for f in fl}
+_stamped = {f for fl in _manifest_files.values() for f in fl}
+_cleared = _dispatched - _stamped
+# scan_corpus = the RAW full corpus (not the stamp-filtered subset) so in-root
+# files newly excluded since last run are dropped rather than masquerading as
+# deletions; untouched files' prior rows are still preserved (#1908).
+_scan = {f for fl in _corpus.values() for f in fl}
+save_manifest(_manifest_files, root='INPUT_PATH', scan_corpus=_scan, clear_semantic=_cleared or None)
+
+# Update cumulative cost tracker
+input_tok = extract.get('input_tokens', 0)
+output_tok = extract.get('output_tokens', 0)
+
+cost_path = Path('graphify-out/cost.json')
+if cost_path.exists():
+    cost = json.loads(cost_path.read_text(encoding=\"utf-8\"))
+else:
+    cost = {'runs': [], 'total_input_tokens': 0, 'total_output_tokens': 0}
+
+cost['runs'].append({
+    'date': datetime.now(timezone.utc).isoformat(),
+    'input_tokens': input_tok,
+    'output_tokens': output_tok,
+    'files': detect.get('total_files', 0),
+})
+cost['total_input_tokens'] += input_tok
+cost['total_output_tokens'] += output_tok
+cost_path.write_text(json.dumps(cost, indent=2, ensure_ascii=False), encoding=\"utf-8\")
+
+print(f'This run: {input_tok:,} input tokens, {output_tok:,} output tokens')
+print(f'All time: {cost[\"total_input_tokens\"]:,} input, {cost[\"total_output_tokens\"]:,} output ({len(cost[\"runs\"])} runs)')
+"
+rm -f graphify-out/.graphify_detect.json graphify-out/.graphify_extract.json graphify-out/.graphify_ast.json graphify-out/.graphify_semantic.json graphify-out/.graphify_analysis.json
+find graphify-out -maxdepth 1 -name '.graphify_chunk_*.json' -delete 2>/dev/null
+rm -f graphify-out/.needs_update 2>/dev/null || true
+```
+
+Replace INPUT_PATH with the actual path (same value used in Steps 4-5) so the manifest is relativized to the scan root.
+
+Tell the user (omit the obsidian line unless --obsidian was given):
+```
+Graph complete. Outputs in PATH_TO_DIR/graphify-out/
+
+  graph.html            - interactive graph, open in browser
+  GRAPH_REPORT.md       - audit report
+  graph.json            - raw graph data
+  obsidian/             - Obsidian vault (only if --obsidian was given)
+```
+
+If graphify saved you time, consider supporting it: https://github.com/sponsors/safishamsi
+
+Replace PATH_TO_DIR with the actual absolute path of the directory that was processed.
+
+Then paste these sections from GRAPH_REPORT.md directly into the chat:
+- God Nodes
+- Surprising Connections
+- Suggested Questions
+
+Do NOT paste the full report - just those three sections. Keep it concise.
+
+Then immediately offer to explore. Pick the single most interesting suggested question from the report - the one that crosses the most community boundaries or has the most surprising bridge node - and ask:
+
+> "The most interesting question this graph can answer: **[question]**. Want me to trace it?"
+
+If the user says yes, run `/graphify query "[question]"` on the graph and walk them through the answer using the graph structure - which nodes connect, which community boundaries get crossed, what the path reveals. Keep going as long as they want to explore. Each answer should end with a natural follow-up ("this connects to X - want to go deeper?") so the session feels like navigation, not a one-shot report.
+
+The graph is the map. Your job after the pipeline is to be the guide.
+
+---
+
+## Interpreter guard for subcommands
+
+Before running any subcommand below (`--update`, `--cluster-only`, `query`, `path`, `explain`, `add`), check that `.graphify_python` exists. If it's missing (e.g. user deleted `graphify-out/`), re-resolve the interpreter first:
+
+```bash
+if [ ! -f graphify-out/.graphify_python ]; then
+    GRAPHIFY_BIN=$(which graphify 2>/dev/null)
+    if [ -n "$GRAPHIFY_BIN" ]; then
+        PYTHON=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
+        case "$PYTHON" in *[!a-zA-Z0-9/_.@-]*) PYTHON="python3" ;; esac
+    else
+        PYTHON="python3"
+    fi
+    mkdir -p graphify-out
+    "$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w', encoding='utf-8').write(sys.executable)"
+fi
+```
+
+## For --update and --cluster-only
+
+Both are non-default subcommands. `--update` re-extracts only new or changed files; `--cluster-only` reruns clustering on the existing graph. See `references/update.md` for both flows.
+
+---
+
+## For /graphify query
+
+When `graphify-out/graph.json` already exists and the user asks a question about the corpus, answer from the graph rather than rebuilding it:
+
+```bash
+graphify query "<question>"
+```
+
+Before traversal, expand the question against the graph's own vocabulary so a wording mismatch does not collapse the answer to noise. If the `graphify query` CLI is unavailable, fall back to an inline NetworkX traversal of `graphify-out/graph.json`. Answer using only what the graph output contains, and quote `source_location` when citing a specific fact. For that vocab-expansion step, the BFS/DFS traversal modes, the `--budget` cap, the NetworkX fallback, `save-result` feedback, and the `/graphify path` and `/graphify explain` flows, see `references/query.md`.
+
+---
+
+## For /graphify add and --watch
+
+Neither is part of the default build. When the user runs `/graphify add <url>` to fetch a URL into the corpus, or passes `--watch` to auto-rebuild on file changes, see `references/add-watch.md`.
+
+---
+
+## For the commit hook and native CLAUDE.md integration
+
+When the user asks to install the post-commit auto-rebuild hook or wire graphify into a project's CLAUDE.md, see `references/hooks.md`.
+
+---
+
+## Honesty Rules
+
+- Never invent an edge. If unsure, use AMBIGUOUS.
+- Never skip the corpus check warning.
+- Always show token cost in the report.
+- Never hide cohesion scores behind symbols - show the raw number.
+- Never run HTML viz on a graph with more than 5,000 nodes without warning the user.

.opencode/skills/graphify/references/add-watch.md

@@ -0,0 +1,56 @@
+# graphify reference: add a URL and watch a folder
+
+Load this when the user ran `/graphify add <url>` or passed `--watch`. Neither is part of the default build.
+
+## For /graphify add
+
+Fetch a URL and add it to the corpus, then update the graph.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys
+from graphify.ingest import ingest
+from pathlib import Path
+
+try:
+    out = ingest('URL', Path('./raw'), author='AUTHOR', contributor='CONTRIBUTOR')
+    print(f'Saved to {out}')
+except ValueError as e:
+    print(f'error: {e}', file=sys.stderr)
+    sys.exit(1)
+except RuntimeError as e:
+    print(f'error: {e}', file=sys.stderr)
+    sys.exit(1)
+"
+```
+
+Replace `URL` with the actual URL, `AUTHOR` with the user's name if provided, `CONTRIBUTOR` likewise. If the command exits with an error, tell the user what went wrong - do not silently continue. After a successful save, automatically run the `--update` pipeline on `./raw` to merge the new file into the existing graph.
+
+Supported URL types (auto-detected):
+- YouTube / any video URL → audio downloaded via yt-dlp, transcribed to `.txt` on next run (requires `pip install 'graphifyy[video]'`)
+- Twitter/X → fetched via oEmbed, saved as `.md` with tweet text and author
+- arXiv → abstract + metadata saved as `.md`
+- PDF → downloaded as `.pdf`
+- Images (.png/.jpg/.webp) → downloaded, Claude vision extracts on next run
+- Any webpage → converted to markdown via html2text
+
+---
+
+## For --watch
+
+Start a background watcher that monitors a folder and auto-updates the graph when files change.
+
+```bash
+$(cat graphify-out/.graphify_python) -m graphify.watch INPUT_PATH --debounce 3
+```
+
+Replace INPUT_PATH with the folder to watch. Behavior depends on what changed:
+
+- **Code files only (.py, .ts, .go, etc.):** re-runs AST extraction + rebuild + cluster immediately, no LLM needed. `graph.json` and `GRAPH_REPORT.md` are updated automatically.
+- **Docs, papers, or images:** writes a `graphify-out/needs_update` flag and prints a notification to run `/graphify --update` (LLM semantic re-extraction required).
+
+Debounce (default 3s): waits until file activity stops before triggering, so a wave of parallel agent writes doesn't trigger a rebuild per file.
+
+Press Ctrl+C to stop.
+
+For agentic workflows: run `--watch` in a background terminal. Code changes from agent waves are picked up automatically between waves. If agents are also writing docs or notes, you'll need a manual `/graphify --update` after those waves.

.opencode/skills/graphify/references/exports.md

@@ -0,0 +1,87 @@
+# graphify reference: extra exports and benchmark
+
+Load this when the user passed one of the export flags (`--wiki`, `--neo4j`, `--neo4j-push`, `--falkordb`, `--falkordb-push`, `--svg`, `--graphml`, `--mcp`), or when the corpus is large enough for the token-reduction benchmark. Each step runs only for its own flag.
+
+### Step 6b - Wiki (only if --wiki flag)
+
+**Only run this step if `--wiki` was explicitly given in the original command.**
+
+Run this before Step 9 (cleanup) so `.graphify_labels.json` is still available.
+
+```bash
+graphify export wiki
+```
+
+### Step 7 - Neo4j export (only if --neo4j or --neo4j-push flag)
+
+**If `--neo4j`** - generate a Cypher file for manual import:
+
+```bash
+graphify export neo4j
+```
+
+**If `--neo4j-push <uri>`** - push directly to a running Neo4j instance. Ask the user for credentials if not provided:
+
+```bash
+graphify export neo4j --push bolt://localhost:7687 --user neo4j --password PASSWORD
+```
+
+Default URI is `bolt://localhost:7687`, default user is `neo4j`. Uses MERGE - safe to re-run without creating duplicates.
+
+### Step 7a - FalkorDB export (only if --falkordb or --falkordb-push flag)
+
+**If `--falkordb`** - generate a Cypher file. The statements are OpenCypher, but FalkorDB's `GRAPH.QUERY` runs one statement at a time (no bulk script import like Neo4j's `cypher-shell`), so prefer `--falkordb-push` to load a graph. Use this only when you want the portable `cypher.txt` artifact:
+
+```bash
+graphify export falkordb
+```
+
+**If `--falkordb-push <uri>`** - push directly to a running FalkorDB instance. Credentials are optional; ask the user only if the instance requires auth:
+
+```bash
+graphify export falkordb --push falkordb://localhost:6379
+```
+
+Default URI is `falkordb://localhost:6379` (the scheme is informational - `redis://` or a bare `host:port` work too), auth is optional, and the target graph defaults to `graphify`. Uses MERGE - safe to re-run without creating duplicates.
+
+### Step 7b - SVG export (only if --svg flag)
+
+```bash
+graphify export svg
+```
+
+### Step 7c - GraphML export (only if --graphml flag)
+
+```bash
+graphify export graphml
+```
+
+### Step 7d - MCP server (only if --mcp flag)
+
+```bash
+$(cat graphify-out/.graphify_python) -m graphify.serve graphify-out/graph.json
+```
+
+This starts a stdio MCP server that exposes tools: `query_graph`, `get_node`, `get_neighbors`, `get_community`, `god_nodes`, `graph_stats`, `shortest_path`. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.
+
+To configure in Claude Desktop, add to `claude_desktop_config.json`. Claude Desktop can't run `$(...)`, and under `uv tool install` the system `python3` can't import graphify — so set `command` to the **absolute interpreter path** printed by `cat graphify-out/.graphify_python`:
+```json
+{
+  "mcpServers": {
+    "graphify": {
+      "command": "<absolute path from: cat graphify-out/.graphify_python>",
+      "args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
+    }
+  }
+}
+```
+
+### Step 8 - Token reduction benchmark (only if total_words > 5000)
+
+If `total_words` from `graphify-out/.graphify_detect.json` is greater than 5,000, run:
+
+```bash
+graphify benchmark
+```
+
+Print the output directly in chat. If `total_words <= 5000`, skip silently - the graph value is structural clarity, not token compression, for small corpora.

.opencode/skills/graphify/references/extraction-spec.md

@@ -0,0 +1,70 @@
+# graphify reference: extraction subagent prompt
+
+Load this in Step 3 Part B when the corpus has at least one doc, paper, or image chunk. A pure-code corpus skips Part B and never reads this file. Each semantic subagent receives the prompt below verbatim (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE, and CHUNK_PATH).
+
+```
+You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment.
+Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble.
+
+Files (chunk CHUNK_NUM of TOTAL_CHUNKS):
+FILE_LIST
+
+Rules:
+- EXTRACTED: relationship explicit in source (import, call, citation, "see §3.2")
+- INFERRED: reasonable inference (shared data structure, implied dependency)
+- AMBIGUOUS: uncertain - flag for review, do not omit
+
+Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns).
+  Do not re-extract imports - AST already has those.
+Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant concept node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms, design patterns). `file_type` MUST be one of exactly these six values: `code`, `document`, `paper`, `image`, `rationale`, `concept`. Any other value is invalid and will be rejected.
+Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction. `calls` edges MUST stay within one language: a Python function cannot `calls` a JS/TS/Go/Rust/Java symbol and vice versa — cross-language call edges are phantom artifacts, never emit them.
+Image files: use vision to understand what the image IS - do not just OCR.
+  UI screenshot: layout patterns, design decisions, key elements, purpose.
+  Chart: metric, trend/insight, data source.
+  Tweet/post: claim as node, author, concepts mentioned.
+  Diagram: components and connections.
+  Research figure: what it demonstrates, method, result.
+  Handwritten/whiteboard: ideas and arrows, mark uncertain readings AMBIGUOUS.
+
+DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
+  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.
+
+Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples:
+- Two functions that both validate user input but never call each other
+- A class in code and a concept in a paper that describe the same algorithm
+- Two error types that handle the same failure mode differently
+Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.
+
+Hyperedges: if 3 or more nodes clearly participate together in a shared concept, flow, or pattern that is not captured by pairwise edges alone, add a hyperedge to a top-level `hyperedges` array. Examples:
+- All classes that implement a common protocol or interface
+- All functions in an authentication flow (even if they don't all call each other)
+- All concepts from a paper section that form one coherent idea
+Use sparingly — only when the group relationship adds information beyond the pairwise edges. Maximum 3 hyperedges per chunk.
+
+If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
+  contributor onto every node from that file.
+
+confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a default:
+- EXTRACTED edges: confidence_score = 1.0 always
+- INFERRED edges: pick exactly ONE value from this set — never 0.5:
+    0.95  direct structural evidence (shared data structure, named cross-file reference).
+    0.85  strong inference (clear functional alignment, no direct symbol link).
+    0.75  reasonable inference (shared problem domain + similar shape, requires interpretation).
+    0.65  weak inference (thematically related, no shape evidence).
+    0.55  speculative but plausible (surface-level co-occurrence only).
+  Models follow discrete rubrics better than continuous ranges; the bimodal
+  distribution observed in production (>50% at 0.5, >40% at 0.85+) shows the
+  range guidance is being collapsed to a binary. If no value above fits, mark
+  the edge AMBIGUOUS rather than picking 0.4 or below.
+- AMBIGUOUS edges: 0.1-0.3
+
+Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format: `{stem}_{entity}` where stem is the **full repo-relative path with the extension dropped**, every path segment kept and joined with `_` (each segment lowercased with non-alphanumeric chars replaced by `_`), and entity is the symbol name similarly normalized. Use every directory level, not just the immediate parent — this keeps same-named files in different directories distinct. Examples: `src/auth/session.py` + `ValidateToken` → `src_auth_session_validatetoken`; `lib/utils/helpers.py` + `parse_url` → `lib_utils_helpers_parse_url`; `tests/test_foo.py` + `_helper` → `tests_test_foo_helper`; `docs/v1/api/README.md` + `getUser` → `docs_v1_api_readme_getuser`. Top-level files (no parent dir, e.g. `setup.py`) use just the filename stem: `setup_my_func`. This must match the ID the AST extractor generates — using just the filename (e.g., `session_validatetoken`) or only the immediate parent (e.g., `auth_session_validatetoken`) will create orphan ghost-duplicate nodes. If you are re-extracting a project built under the old immediate-parent format, the user should run `graphify extract --force` to rebuild cleanly. CRITICAL: never append chunk numbers, sequence numbers, or any suffix to an ID (no `_c1`, `_c2`, `_chunk2`, etc.). IDs must be deterministic from the label alone — the same entity must always produce the same ID regardless of which chunk processes it.
+
+Generate the extraction JSON matching this schema exactly:
+{"nodes":[{"id":"auth_session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"<FILE_LIST path verbatim>","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"<FILE_LIST path verbatim>","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"<FILE_LIST path verbatim>"}],"input_tokens":0,"output_tokens":0}
+
+source_file RULE (every node, edge, and hyperedge): set source_file to the path of the originating file EXACTLY as it appears in FILE_LIST — verbatim and absolute. Do NOT shorten to a basename, do NOT re-relativize, do NOT strip any directory prefix, and do NOT change separators (the engine canonicalizes separators and relativizes against the build root downstream). Copy the FILE_LIST entry character-for-character. This keeps the full build and incremental --update on the same base, so build_merge's replace-on-re-extract matches the existing node instead of accumulating a duplicate.
+
+Then write the JSON to disk using the Write tool at this exact absolute path (no relative paths — Write resolves relative paths against an undefined cwd and the file will be silently lost):
+CHUNK_PATH
+```

.opencode/skills/graphify/references/github-and-merge.md

@@ -0,0 +1,46 @@
+# graphify reference: GitHub clone and cross-repo merge
+
+Load this when the user passed one or more `https://github.com/...` URLs, or named several local subfolders to merge into one graph.
+
+### Step 0 - Clone GitHub repo(s) (only if a GitHub URL was given)
+
+**Single repo:**
+```bash
+LOCAL_PATH=$(graphify clone <github-url> [--branch <branch>])
+# Use LOCAL_PATH as the target for all subsequent steps
+```
+
+**Multiple repos (cross-repo graph):**
+```bash
+# Clone each repo, run the full pipeline on each, then merge
+graphify clone <url1>   # → ~/.graphify/repos/<owner1>/<repo1>
+graphify clone <url2>   # → ~/.graphify/repos/<owner2>/<repo2>
+# Run /graphify on each local path to produce their graph.json files
+# Then merge:
+graphify merge-graphs \
+  ~/.graphify/repos/<owner1>/<repo1>/graphify-out/graph.json \
+  ~/.graphify/repos/<owner2>/<repo2>/graphify-out/graph.json \
+  --out graphify-out/cross-repo-graph.json
+```
+
+Graphify clones into `~/.graphify/repos/<owner>/<repo>` and reuses existing clones on repeat runs. Each node in the merged graph carries a `repo` attribute so you can filter by origin.
+
+**Multiple local subfolders (monorepo or multi-service layout):**
+
+The skill pipeline writes all intermediate and final outputs to `graphify-out/` in the current working directory. Running the skill on each subfolder separately will clobber the same output dir. Instead, use the CLI directly for each subfolder — it places `graphify-out/` *inside* the scanned path:
+
+```bash
+graphify extract ./core/     # → ./core/graphify-out/graph.json
+graphify extract ./service/  # → ./service/graphify-out/graph.json
+graphify extract ./platform/ # → ./platform/graphify-out/graph.json
+# Add --backend gemini|kimi|openai|deepseek|claude-cli depending on which API key you have set
+
+# Then merge at the project root:
+graphify merge-graphs \
+  ./core/graphify-out/graph.json \
+  ./service/graphify-out/graph.json \
+  ./platform/graphify-out/graph.json \
+  --out graphify-out/graph.json
+```
+
+Once `graphify-out/graph.json` exists, the fast path above takes over: any codebase question runs `graphify query` directly on the merged graph — no re-extraction, no size gate.

.opencode/skills/graphify/references/hooks.md

@@ -0,0 +1,33 @@
+# graphify reference: commit hook and native CLAUDE.md integration
+
+Load this when the user asked to install the post-commit hook or wire graphify into a project's CLAUDE.md.
+
+## For git commit hook
+
+Install a post-commit hook that auto-rebuilds the graph after every commit. No background process needed - triggers once per commit, works with any editor.
+
+```bash
+graphify hook install    # install
+graphify hook uninstall  # remove
+graphify hook status     # check
+```
+
+After every `git commit`, the hook detects which code files changed (via `git diff HEAD~1`), re-runs AST extraction on those files, and rebuilds `graph.json` and `GRAPH_REPORT.md`. Doc/image changes are ignored by the hook - run `/graphify --update` manually for those.
+
+If a post-commit hook already exists, graphify appends to it rather than replacing it.
+
+---
+
+## For native CLAUDE.md integration
+
+Run once per project to make graphify always-on in Claude Code sessions:
+
+```bash
+graphify claude install
+```
+
+This writes a `## graphify` section to the local `CLAUDE.md` that instructs Claude to check the graph before answering codebase questions and rebuild it after code changes. No manual `/graphify` needed in future sessions.
+
+```bash
+graphify claude uninstall  # remove the section
+```

.opencode/skills/graphify/references/query.md

@@ -0,0 +1,311 @@
+# graphify reference: query, path, explain
+
+Load this when the user asks a question against an existing graph, or runs `/graphify path` or `/graphify explain`. The core's query stub points here for the full traversal flow. These flows use the `graphify query` CLI when it is available and fall back to an inline NetworkX traversal otherwise.
+
+Two traversal modes - choose based on the question:
+
+| Mode | Flag | Best for |
+|------|------|----------|
+| BFS (default) | _(none)_ | "What is X connected to?" - broad context, nearest neighbors first |
+| DFS | `--dfs` | "How does X reach Y?" - trace a specific chain or dependency path |
+
+First check the graph exists:
+```bash
+$(cat graphify-out/.graphify_python) -c "
+from pathlib import Path
+if not Path('graphify-out/graph.json').exists():
+    print('ERROR: No graph found. Run /graphify <path> first to build the graph.')
+    raise SystemExit(1)
+"
+```
+If it fails, stop and tell the user to run `/graphify <path>` first.
+
+### Step 0 — Constrained query expansion (REQUIRED before traversal)
+
+graphify's `query` CLI matches nodes via case-folded substring + IDF — there is **no stemming, no synonyms, no cross-language match** inside the binary, and the inline fallback below matches the same way. If the user's question uses different language or different domain vocabulary than the graph's labels (user says "обработчик" / graph says "handler"; user says "authentication" / graph says "Guardian"), the literal matcher returns 0 hits and the answer collapses to noise.
+
+Fix this **without inventing tokens** by expanding the query against the actual graph vocabulary first:
+
+1. Extract the token vocabulary from node labels:
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json, re
+from pathlib import Path
+data = json.loads(Path('graphify-out/graph.json').read_text(encoding='utf-8'))
+vocab = set()
+for n in data['nodes']:
+    for c in re.findall(r'[^\W\d_]+', n.get('label','') or '', re.UNICODE):
+        parts = re.findall(r'[A-Z]+(?=[A-Z][a-z])|[A-Z]?[a-z]+|[A-Z]+', c) or [c]
+        for p in parts:
+            t = p.lower()
+            if 3 <= len(t) <= 30:
+                vocab.add(t)
+Path('graphify-out/.vocab.txt').write_text('\n'.join(sorted(vocab)), encoding='utf-8')
+print(f'vocab: {len(vocab)} tokens')
+"
+```
+
+2. Read `graphify-out/.vocab.txt`. Then for the user's question, select **up to 12 tokens from this exact list** that semantically match the query intent. Hard constraints:
+   - You MUST pick only tokens present in the vocabulary file. Do NOT invent tokens.
+   - If a query concept has no plausible token in the vocab, skip it — do not substitute a near-synonym from training memory.
+   - If **no** vocab tokens match the query at all, output an empty list and tell the user the corpus has no relevant vocabulary for this question. Do not fabricate a search.
+   - Translate cross-language: Russian "аутентификация" → look for `auth`, `credential`, `token`, `security` IFF present in vocab.
+   - Morphology: "handlers" maps to `handler` IFF present; "todos" maps to `todo` IFF present.
+
+3. Print the selection explicitly to the user before running the query, so the expansion is auditable:
+```
+Query expanded to (from graph vocab, N tokens): [token1, token2, ...]
+```
+If the list is empty, say so plainly and stop — do not proceed to traversal.
+
+### Step 1 — Traversal
+
+Build the **expanded query string** by joining the selected tokens with spaces. Use this string as `QUESTION` below — NOT the original user question. (The original question is preserved only for `save-result` at the end.)
+
+Prefer the CLI when it is installed:
+```bash
+graphify query "QUESTION"
+# or: graphify query "QUESTION" --dfs --budget 3000
+```
+
+If the CLI is unavailable, load `graphify-out/graph.json` and run the traversal inline:
+
+1. Find the 1-3 nodes whose label best matches the expanded tokens.
+2. Run the appropriate traversal from each starting node.
+3. Read the subgraph - node labels, edge relations, confidence tags, source locations.
+4. Answer using **only** what the graph contains. Quote `source_location` when citing a specific fact.
+5. If the graph lacks enough information, say so - do not hallucinate edges.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from networkx.readwrite import json_graph
+import networkx as nx
+from pathlib import Path
+
+data = json.loads(Path('graphify-out/graph.json').read_text(encoding='utf-8'))
+G = json_graph.node_link_graph(data, edges='links')
+
+question = 'QUESTION'
+mode = 'MODE'  # 'bfs' or 'dfs'
+terms = [t.lower() for t in question.split() if len(t) >= 3]  # match the vocab threshold; keeps api/jwt/ios (#1392)
+
+# Find best-matching start nodes
+scored = []
+for nid, ndata in G.nodes(data=True):
+    label = ndata.get('label', '').lower()
+    score = sum(1 for t in terms if t in label)
+    if score > 0:
+        scored.append((score, nid))
+scored.sort(reverse=True)
+start_nodes = [nid for _, nid in scored[:3]]
+
+if not start_nodes:
+    print('No matching nodes found for query terms:', terms)
+    sys.exit(0)
+
+subgraph_nodes = set()
+subgraph_edges = []
+
+if mode == 'dfs':
+    # DFS: follow one path as deep as possible before backtracking.
+    # Depth-limited to 6 to avoid traversing the whole graph.
+    visited = set()
+    stack = [(n, 0) for n in reversed(start_nodes)]
+    while stack:
+        node, depth = stack.pop()
+        if node in visited or depth > 6:
+            continue
+        visited.add(node)
+        subgraph_nodes.add(node)
+        for neighbor in G.neighbors(node):
+            if neighbor not in visited:
+                stack.append((neighbor, depth + 1))
+                subgraph_edges.append((node, neighbor))
+else:
+    # BFS: explore all neighbors layer by layer up to depth 3.
+    frontier = set(start_nodes)
+    subgraph_nodes = set(start_nodes)
+    for _ in range(3):
+        next_frontier = set()
+        for n in frontier:
+            for neighbor in G.neighbors(n):
+                if neighbor not in subgraph_nodes:
+                    next_frontier.add(neighbor)
+                    subgraph_edges.append((n, neighbor))
+        subgraph_nodes.update(next_frontier)
+        frontier = next_frontier
+
+# Token-budget aware output: rank by relevance, cut at budget (~4 chars/token)
+token_budget = BUDGET  # default 2000
+char_budget = token_budget * 4
+
+# Score each node by term overlap for ranked output
+def relevance(nid):
+    label = G.nodes[nid].get('label', '').lower()
+    return sum(1 for t in terms if t in label)
+
+ranked_nodes = sorted(subgraph_nodes, key=relevance, reverse=True)
+
+lines = [f'Traversal: {mode.upper()} | Start: {[G.nodes[n].get(\"label\",n) for n in start_nodes]} | {len(subgraph_nodes)} nodes']
+for nid in ranked_nodes:
+    d = G.nodes[nid]
+    lines.append(f'  NODE {d.get(\"label\", nid)} [src={d.get(\"source_file\",\"\")} loc={d.get(\"source_location\",\"\")}]')
+for u, v in subgraph_edges:
+    if u in subgraph_nodes and v in subgraph_nodes:
+        _raw = G[u][v]; d = next(iter(_raw.values()), {}) if isinstance(G, nx.MultiGraph) else _raw
+        lines.append(f'  EDGE {G.nodes[u].get(\"label\",u)} --{d.get(\"relation\",\"\")} [{d.get(\"confidence\",\"\")}]--> {G.nodes[v].get(\"label\",v)}')
+
+output = '\n'.join(lines)
+if len(output) > char_budget:
+    output = output[:char_budget] + f'\n... (truncated at ~{token_budget} token budget - use --budget N for more)'
+print(output)
+"
+```
+
+Replace `QUESTION` with the **expanded** query string, `MODE` with `bfs` or `dfs`, and `BUDGET` with the token budget (default `2000`, or whatever `--budget N` specifies). Then answer based on the subgraph output above, using only what the graph contains.
+
+After writing the answer, save it back into the graph so it improves future queries. Include the expanded tokens inside the `--answer` text (e.g. `"Expanded from original query via vocab: [tokens]. Then traversed..."`) so the next `--update` extracts the expansion history as a graph node:
+
+```bash
+$(cat graphify-out/.graphify_python) -m graphify save-result --question "ORIGINAL_QUESTION" --answer "ANSWER" --type query --nodes NODE1 NODE2
+```
+
+Replace `ORIGINAL_QUESTION` with the user's verbatim question, `ANSWER` with your full answer text (containing the expanded-token trace), `NODE1 NODE2` with the list of node labels you cited. This closes the feedback loop: the next `--update` will extract this Q&A as a node in the graph.
+
+**Work memory (self-improving loop).** Add an `--outcome` so future sessions learn from this one — append `--outcome useful|dead_end|corrected` to the `save-result` command (and `--correction "the right answer"` when correcting):
+
+- `useful` — the cited nodes answered the question well (they become *preferred sources*).
+- `dead_end` — the question/path led nowhere; don't re-derive it next time.
+- `corrected` — the saved answer was wrong; `--correction` records what was right.
+
+At the **start** of graph work, refresh and read the lessons: run `graphify reflect --if-stale` (cheap, deterministic, no LLM; `--if-stale` makes it a no-op when `LESSONS.md` is already newer than every input, e.g. when the git hook just refreshed it), then read `graphify-out/reflections/LESSONS.md`. It lists **preferred sources** (start there), **known dead ends** (skip them), and prior **corrections**. Running `reflect` yourself keeps the lessons current even without the git hook installed; if the post-commit hook *is* installed, `--if-stale` means your session-start run costs almost nothing.
+
+---
+
+## For /graphify path
+
+Find the shortest path between two named concepts in the graph. Prefer the CLI when installed:
+
+```bash
+graphify path "NODE_A" "NODE_B"
+```
+
+If the CLI is unavailable, run it inline:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json, sys
+import networkx as nx
+from networkx.readwrite import json_graph
+from pathlib import Path
+
+data = json.loads(Path('graphify-out/graph.json').read_text(encoding='utf-8'))
+G = json_graph.node_link_graph(data, edges='links')
+
+a_term = 'NODE_A'
+b_term = 'NODE_B'
+
+def find_node(term):
+    term = term.lower()
+    scored = sorted(
+        [(sum(1 for w in term.split() if w in G.nodes[n].get('label','').lower()), n)
+         for n in G.nodes()],
+        reverse=True
+    )
+    return scored[0][1] if scored and scored[0][0] > 0 else None
+
+src = find_node(a_term)
+tgt = find_node(b_term)
+
+if not src or not tgt:
+    print(f'Could not find nodes matching: {a_term!r} or {b_term!r}')
+    sys.exit(0)
+
+try:
+    path = nx.shortest_path(G, src, tgt)
+    print(f'Shortest path ({len(path)-1} hops):')
+    for i, nid in enumerate(path):
+        label = G.nodes[nid].get('label', nid)
+        if i < len(path) - 1:
+            _raw = G[nid][path[i+1]]; edge = next(iter(_raw.values()), {}) if isinstance(G, nx.MultiGraph) else _raw
+            rel = edge.get('relation', '')
+            conf = edge.get('confidence', '')
+            print(f'  {label} --{rel}--> [{conf}]')
+        else:
+            print(f'  {label}')
+except nx.NetworkXNoPath:
+    print(f'No path found between {a_term!r} and {b_term!r}')
+except nx.NodeNotFound as e:
+    print(f'Node not found: {e}')
+"
+```
+
+Replace `NODE_A` and `NODE_B` with the actual concept names from the user. Then explain the path in plain language - what each hop means, why it's significant.
+
+After writing the explanation, save it back:
+
+```bash
+$(cat graphify-out/.graphify_python) -m graphify save-result --question "Path from NODE_A to NODE_B" --answer "ANSWER" --type path_query --nodes NODE_A NODE_B
+```
+
+---
+
+## For /graphify explain
+
+Give a plain-language explanation of a single node - everything connected to it. Prefer the CLI when installed:
+
+```bash
+graphify explain "NODE_NAME"
+```
+
+If the CLI is unavailable, run it inline:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json, sys
+import networkx as nx
+from networkx.readwrite import json_graph
+from pathlib import Path
+
+data = json.loads(Path('graphify-out/graph.json').read_text(encoding='utf-8'))
+G = json_graph.node_link_graph(data, edges='links')
+
+term = 'NODE_NAME'
+term_lower = term.lower()
+
+# Find best matching node
+scored = sorted(
+    [(sum(1 for w in term_lower.split() if w in G.nodes[n].get('label','').lower()), n)
+     for n in G.nodes()],
+    reverse=True
+)
+if not scored or scored[0][0] == 0:
+    print(f'No node matching {term!r}')
+    sys.exit(0)
+
+nid = scored[0][1]
+data_n = G.nodes[nid]
+print(f'NODE: {data_n.get(\"label\", nid)}')
+print(f'  source: {data_n.get(\"source_file\",\"unknown\")}')
+print(f'  type: {data_n.get(\"file_type\",\"unknown\")}')
+print(f'  degree: {G.degree(nid)}')
+print()
+print('CONNECTIONS:')
+for neighbor in G.neighbors(nid):
+    _raw = G[nid][neighbor]; edge = next(iter(_raw.values()), {}) if isinstance(G, nx.MultiGraph) else _raw
+    nlabel = G.nodes[neighbor].get('label', neighbor)
+    rel = edge.get('relation', '')
+    conf = edge.get('confidence', '')
+    src_file = G.nodes[neighbor].get('source_file', '')
+    print(f'  --{rel}--> {nlabel} [{conf}] ({src_file})')
+"
+```
+
+Replace `NODE_NAME` with the concept the user asked about. Then write a 3-5 sentence explanation of what this node is, what it connects to, and why those connections are significant. Use the source locations as citations.
+
+After writing the explanation, save it back:
+
+```bash
+$(cat graphify-out/.graphify_python) -m graphify save-result --question "Explain NODE_NAME" --answer "ANSWER" --type explain --nodes NODE_NAME
+```

.opencode/skills/graphify/references/transcribe.md

@@ -0,0 +1,52 @@
+# graphify reference: transcribe video and audio
+
+Load this only when `detect` reported one or more `video` files. A corpus with no video never reads this.
+
+### Step 2.5 - Transcribe video / audio files (only if video files detected)
+
+Skip this step entirely if `detect` returned zero `video` files.
+
+Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.
+
+**Strategy:** Read the god nodes from `graphify-out/.graphify_detect.json` (or the analysis file if it exists from a previous run). You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.
+
+**However**, if the corpus has *only* video files and no other docs/code, use the generic fallback prompt: `"Use proper punctuation and paragraph breaks."`
+
+**Step 1 - Write the Whisper prompt yourself.**
+
+Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:
+
+- Labels: `transformer, attention, encoder, decoder` → `"Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."`
+- Labels: `kubernetes, deployment, pod, helm` → `"DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."`
+
+**Export** it as `GRAPHIFY_WHISPER_PROMPT` (the exact name the transcriber reads — and it must be `export`ed so the child Python process sees it) for the next command.
+
+**Step 2 - Transcribe:**
+
+```bash
+export GRAPHIFY_WHISPER_MODEL=base  # or whatever --whisper-model the user passed (must be exported)
+export GRAPHIFY_WHISPER_PROMPT="<the one-sentence domain hint you composed in Step 1>"
+$(cat graphify-out/.graphify_python) -c "
+import json, os, sys
+from pathlib import Path
+from graphify.transcribe import transcribe_all
+
+detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text(encoding=\"utf-8\"))
+video_files = detect.get('files', {}).get('video', [])
+prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')
+
+transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
+# Write the JSON from Python (NOT a shell '>' redirect): transcribe_all/Whisper
+# print progress to stdout, which would otherwise corrupt the JSON file (#1392).
+Path('graphify-out/.graphify_transcripts.json').write_text(json.dumps(transcript_paths, ensure_ascii=False), encoding=\"utf-8\")
+print(f'Transcribed {len(transcript_paths)} file(s)', file=sys.stderr)
+"
+```
+
+After transcription:
+- Read the transcript paths from `graphify-out/.graphify_transcripts.json`
+- Add them to the docs list before dispatching semantic subagents in Step 3B
+- Print how many transcripts were created: `Transcribed N video file(s) -> treating as docs`
+- If transcription fails for a file, print a warning and continue with the rest
+
+**Whisper model:** Default is `base`. If the user passed `--whisper-model <name>`, `export GRAPHIFY_WHISPER_MODEL=<name>` (it must be exported, not just assigned) before running the command above.

.opencode/skills/graphify/references/update.md

@@ -0,0 +1,210 @@
+# graphify reference: incremental update and cluster-only
+
+Load this only when the user passed `--update` or `--cluster-only`. A first-time full build never reads this file.
+
+## For --update (incremental re-extraction)
+
+Use when you've added or modified files since the last run. Only re-extracts changed files - saves tokens and time.
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import sys, json
+from graphify.detect import detect_incremental, save_manifest
+from pathlib import Path
+
+result = detect_incremental(Path('INPUT_PATH'))
+new_total = result.get('new_total', 0)
+print(json.dumps(result, indent=2, ensure_ascii=False))
+Path('graphify-out/.graphify_incremental.json').write_text(json.dumps(result, ensure_ascii=False), encoding=\"utf-8\")
+deleted = list(result.get('deleted_files', []))
+if new_total == 0 and not deleted:
+    print('No files changed since last run. Nothing to update.')
+    raise SystemExit(0)
+if deleted:
+    print(f'{len(deleted)} deleted file(s) to prune.')
+if new_total > 0:
+    print(f'{new_total} new/changed file(s) to re-extract.')
+"
+```
+
+Then populate `.graphify_detect.json` so Steps 3A–6 (which read it unconditionally) see the right state for an incremental run. `files` carries the changed subset (drives Step 3A AST + Step 3B0 cache check on only what changed); `all_files` carries the full corpus for any step that needs corpus-wide context:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+r = json.loads(Path('graphify-out/.graphify_incremental.json').read_text(encoding=\"utf-8\"))
+Path('graphify-out/.graphify_detect.json').write_text(json.dumps({
+    'files': r.get('new_files', {}),
+    'all_files': r.get('files', {}),
+    'total_files': r.get('new_total', 0),
+    'total_words': r.get('total_words', 0),
+    'skipped_sensitive': r.get('skipped_sensitive', []),
+    'needs_graph': True,
+}, ensure_ascii=False), encoding=\"utf-8\")
+"
+```
+
+If new files exist, first check whether all changed files are code files:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+
+result = json.loads(open('graphify-out/.graphify_incremental.json', encoding='utf-8').read()) if Path('graphify-out/.graphify_incremental.json').exists() else {}
+code_exts = {'.py','.ts','.js','.go','.rs','.java','.cpp','.c','.rb','.swift','.kt','.cs','.scala','.php','.cc','.cxx','.hpp','.h','.kts','.lua','.toc','.f','.F','.f90','.F90','.f95','.F95','.f03','.F03','.f08','.F08'}
+new_files = result.get('new_files', {})
+all_changed = [f for files in new_files.values() for f in files]
+code_only = all(Path(f).suffix.lower() in code_exts for f in all_changed)
+print('code_only:', code_only)
+"
+```
+
+If `code_only` is True: print `[graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed)`, run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.
+
+If `code_only` is False (any changed file is a doc/paper/image/video): **first, if any changed file is in `new_files['video']`, run `references/transcribe.md` (Step 2.5) on those files, then rewrite `.graphify_detect.json` to move the resulting transcript paths into `files['document']` and drop `files['video']`** — otherwise raw `.mp4/.mp3` paths are fed to semantic subagents as unreadable media (#1392). Then run the full Steps 3A–3C pipeline as normal.
+
+
+If no new files exist (only deletions), create an empty extraction so the merge step can prune:
+
+```bash
+if [ ! -f graphify-out/.graphify_extract.json ]; then
+    echo '[graphify update] Only deletions -- creating empty extraction for merge.'
+    $(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+Path('graphify-out/.graphify_extract.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
+"
+fi
+```
+
+
+Then:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from pathlib import Path
+from graphify.build import build_merge
+from graphify.detect import save_manifest
+
+# Load new extraction and incremental state
+new_extraction = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+incremental = json.loads(Path('graphify-out/.graphify_incremental.json').read_text(encoding=\"utf-8\"))
+deleted = list(incremental.get('deleted_files', []))
+# prune_sources is ONLY for genuinely DELETED files. Changed/re-extracted files are
+# handled by build_merge's replace-on-re-extract (#1344): every source_file in
+# new_chunks is dropped from the base before merge, so old/stale nodes don't survive.
+# Do NOT add `changed` here: with root= passed, prune_set relativizes to the same base
+# as the freshly merged nodes and would DELETE the re-extracted content (#1178 is moot
+# now that replace — not the dedup pass — reconciles changed files).
+prune = list(deleted) or None
+
+# Use build_merge() — reads graph.json directly without NetworkX round-trip
+# so edge direction (calls, implements, imports) is always preserved (#801).
+# Pass root= so prune_sources (absolute paths from detect_incremental) are
+# relativized to match the graph's relative source_file values; without it
+# nothing is pruned and stale nodes accumulate on every update (#1361).
+# directed=IS_DIRECTED: replace IS_DIRECTED with True if --directed was given, else
+# False. Without it a --directed --update silently rebuilds undirected and collapses
+# reciprocal A<->B edges (#1392).
+G = build_merge(
+    [new_extraction],
+    graph_path='graphify-out/graph.json',
+    prune_sources=prune,
+    root='INPUT_PATH',
+    directed=IS_DIRECTED,
+)
+print(f'[graphify update] Merged: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges')
+
+# Write merged result back to .graphify_extract.json so Step 4 sees the full graph
+merged_out = {
+    'nodes': [{'id': n, **d} for n, d in G.nodes(data=True)],
+    'edges': [
+        # Explicit source/target last so they win over any stale attrs in d.
+        {**{k: val for k, val in d.items() if k not in ('_src', '_tgt', 'source', 'target')},
+         'source': d.get('_src', u), 'target': d.get('_tgt', v)}
+        for u, v, d in G.edges(data=True)
+    ],
+    # G.graph["hyperedges"] holds hyperedges from both existing graph.json
+    # and new_extraction (build_merge combines them). Falling back to
+    # new_extraction only would silently drop prior-run hyperedges (#801).
+    'hyperedges': list(G.graph.get('hyperedges', [])),
+    'input_tokens': new_extraction.get('input_tokens', 0),
+    'output_tokens': new_extraction.get('output_tokens', 0),
+}
+Path('graphify-out/.graphify_extract.json').write_text(json.dumps(merged_out, ensure_ascii=False), encoding=\"utf-8\")
+print(f'[graphify update] Merged extraction written ({len(merged_out[\"nodes\"])} nodes, {len(merged_out[\"edges\"])} edges)')
+
+# Save manifest so next --update diffs against today's state, not the
+# prior run's baseline (prevents ghost-node reports on subsequent updates).
+# root= matches the build_merge call above so the manifest keys stay relative to
+# the scan root — portable across clones/machines, so --update keeps matching
+# cached files instead of missing every one after a move (#1417).
+#
+# Only stamp semantic files (docs/papers/images) that ACTUALLY produced output
+# THIS run (new_extraction is this run's fresh extraction, read above before the
+# merge overwrote the file): a changed doc whose chunk failed must stay unstamped
+# so the next --update re-queues it, otherwise it is marked done and its content
+# is lost forever (#2015). Mirrors the library extract path
+# (cli._stamped_manifest_files + clear_semantic + scan_corpus).
+from graphify.cli import _stamped_manifest_files
+_manifest_files = _stamped_manifest_files(incremental['files'], new_extraction, Path('INPUT_PATH'))
+# Changed semantic files dispatched this run but NOT stamped had their chunk fail
+# or be omitted; clear any stale semantic_hash so they are re-queued (#1948).
+_sem_types = ('document', 'paper', 'image')
+_dispatched = {f for t, fl in incremental.get('new_files', {}).items() if t in _sem_types for f in fl}
+_stamped = {f for fl in _manifest_files.values() for f in fl}
+_cleared = _dispatched - _stamped
+# scan_corpus = the RAW full corpus so in-root files newly excluded since last run
+# are dropped rather than masquerading as deletions; untouched rows preserved (#1908).
+_scan = {f for fl in incremental['files'].values() for f in fl}
+save_manifest(_manifest_files, root='INPUT_PATH', scan_corpus=_scan, clear_semantic=_cleared or None)
+print('[graphify update] Manifest saved.')
+"
+```
+
+Then run Steps 4–8 on the merged graph as normal.
+
+After Step 4, show the graph diff:
+
+```bash
+$(cat graphify-out/.graphify_python) -c "
+import json
+from graphify.analyze import graph_diff
+from graphify.build import build_from_json
+from networkx.readwrite import json_graph
+import networkx as nx
+from pathlib import Path
+
+# Load old graph (before update) from backup written before merge
+old_data = json.loads(Path('graphify-out/.graphify_old.json').read_text(encoding=\"utf-8\")) if Path('graphify-out/.graphify_old.json').exists() else None
+new_extract = json.loads(Path('graphify-out/.graphify_extract.json').read_text(encoding=\"utf-8\"))
+G_new = build_from_json(new_extract, directed=IS_DIRECTED)
+
+if old_data:
+    G_old = json_graph.node_link_graph(old_data, edges='links')
+    diff = graph_diff(G_old, G_new)
+    print(diff['summary'])
+    if diff['new_nodes']:
+        print('New nodes:', ', '.join(n['label'] for n in diff['new_nodes'][:5]))
+    if diff['new_edges']:
+        print('New edges:', len(diff['new_edges']))
+"
+```
+
+Before the merge step, save the old graph: `cp graphify-out/graph.json graphify-out/.graphify_old.json`
+Clean up after: `rm -f graphify-out/.graphify_old.json`
+
+---
+
+## For --cluster-only
+
+Skip Steps 1–3. Re-run clustering on the existing graph:
+
+```bash
+graphify cluster-only .
+```
+
+`graphify cluster-only .` is **self-contained**: it re-clusters, names communities, and regenerates `GRAPH_REPORT.md`, `graph.json`, and `graph.html` from the existing graph. **Do not re-run Steps 5–9** — they read intermediate files (`.graphify_extract.json`, `.graphify_detect.json`, `.graphify_analysis.json`) that a prior build's cleanup (Step 9) already deleted, so they raise `FileNotFoundError` (#1392). When it finishes, present the refreshed `GRAPH_REPORT.md` summary as usual.

.scratch/architecture-deepening/PRD.md

@@ -0,0 +1,26 @@
+# Architecture Deepening
+
+## Goal
+
+Increase depth, locality, and testability in the existing Meshnet runtime without changing its domain behavior or reopening accepted architecture decisions.
+
+## Scope
+
+This feature backlog is derived from the Graphify code graph and the architecture review. It targets three high-coupling modules:
+
+1. Distributed Route Session execution in the node HTTP path.
+2. Node startup orchestration.
+3. Tracker request intake and HTTP dispatch.
+
+## Constraints
+
+- Preserve ADR-0009: the Tracker is the control plane and public proxy; workers own tokenizer and model execution.
+- Preserve the active Distributed GGUF Runtime plan: DGR-040 owns native-worker supervision; DGR-041 owns native capability registration. Do not duplicate or redesign those stories.
+- Preserve existing Transformers behavior and backend-agnostic Tracker routing, billing, admission, telemetry, and relay semantics.
+- Each task starts with focused characterization tests, then moves behavior behind one deep module interface.
+
+## Order
+
+1. Route Session execution, because it has the clearest seam and lets distributed execution be tested without HTTP.
+2. Node startup orchestration, using the existing capability-validator adapters.
+3. Tracker intake, only after the first two establish the preferred deep-module style.

.scratch/architecture-deepening/issues/01-deepen-route-session-execution.md

@@ -0,0 +1,36 @@
+# AD-001: Deepen Route Session execution behind one node seam
+
+- **Status:** needs-triage
+- **Priority:** p0
+- **Dependencies:** none
+- **Blocks:** AD-002
+- **Evidence:** Graphify identifies `torch_server.py` as the Activation Transport & Binary Frames hub; `_TorchHandler._do_chat_completions` has cyclomatic complexity 53 and owns request parsing, complete-model generation, distributed prefill/decode, Hot KV State recovery, transport clients, SSE, telemetry, and cleanup.
+
+## Objective
+
+Move distributed Route Session execution behind one deep module interface so the HTTP module only translates a client request into a Route Session result/stream.
+
+## Constraints
+
+- Preserve ADR-0009: the head worker owns tokenization and shard execution.
+- Preserve the existing OpenAI-compatible HTTP/SSE behavior.
+- Keep Hot KV State local to each shard and retain cache-miss re-prefill behavior.
+- Do not introduce native GGUF worker work; DGR-040 and DGR-041 own that scope.
+
+## Acceptance criteria
+
+- [ ] Characterization tests cover prefill, decode, cache-miss re-prefill, cancellation, and cleanup through the new module interface without an HTTP server.
+- [ ] The HTTP module retains only request translation, response translation, and request accounting.
+- [ ] Route Session lifecycle owns downstream direct/relay client cleanup in one place.
+- [ ] Existing two-node, KV-cache, relay, and OpenAI compatibility tests retain behavior.
+- [ ] `pytest` targeted tests and `python -m compileall packages tests` pass.
+
+## Likely files
+
+- Modify: `packages/node/meshnet_node/torch_server.py`
+- Create: module adjacent to `torch_server.py` for Route Session execution
+- Modify/add: `tests/test_two_node_pipeline.py`, `tests/test_kv_cache_distributed.py`, focused new tests
+
+## Non-goals
+
+No change to public route selection, model architecture behavior, native worker protocol, or WAN KV migration.

.scratch/architecture-deepening/issues/02-deepen-node-startup-orchestration.md

@@ -0,0 +1,33 @@
+# AD-002: Deepen Node startup orchestration
+
+- **Status:** needs-triage
+- **Priority:** p1
+- **Dependencies:** AD-001
+- **Evidence:** `run_startup()` in `packages/node/meshnet_node/startup.py` has cyclomatic complexity 101, a broad caller-facing parameter surface, and coordinates hardware, wallet, assignment, artifacts, server construction, capability proof, and Tracker registration.
+
+## Objective
+
+Create a deep Node startup module with explicit immutable startup intent and one execution seam, so callers and tests do not need to understand the full startup sequence.
+
+## Constraints
+
+- Retain the existing explicit capability-validator adapter used by tests.
+- Preserve current CLI behavior, registration data, startup ordering, and Transformers behavior.
+- Keep native-worker supervision out of scope: DGR-040 owns it. The result may expose a phase where DGR-040 can later attach, but must not implement that worker supervision.
+
+## Acceptance criteria
+
+- [ ] Characterization tests pin successful startup, capability refusal before registration, assignment behavior, and failure classification.
+- [ ] The public startup interface accepts a cohesive intent/plan rather than leaking orchestration details across callers.
+- [ ] Hardware/assignment, artifact/server, and proof/registration behavior are internally ordered and individually testable through internal seams.
+- [ ] Existing `tests/test_node_startup.py`, `tests/test_node_admission.py`, and mining CLI tests retain behavior.
+- [ ] `pytest` targeted tests and `python -m compileall packages tests` pass.
+
+## Likely files
+
+- Modify: `packages/node/meshnet_node/startup.py`, `packages/node/meshnet_node/testing.py`, `packages/node/meshnet_node/cli.py`
+- Modify/add: `tests/test_node_startup.py`, `tests/test_node_admission.py`, `tests/test_mining_cli.py`
+
+## Non-goals
+
+No new backend type, no Tracker placement algorithm change, and no native-worker process supervision.

.scratch/architecture-deepening/issues/03-deepen-tracker-request-intake.md

@@ -0,0 +1,35 @@
+# AD-003: Deepen Tracker request intake without changing control-plane semantics
+
+- **Status:** needs-triage
+- **Priority:** p1
+- **Dependencies:** AD-001, AD-002
+- **Evidence:** Graphify marks `_TrackerHandler` as the highest-degree node (93 edges). `do_POST` dispatches auth, accounts, billing, registry, raft, gossip, placement, calibration, model, and inference paths; `do_GET` mixes operational projections and public request paths. Major handlers include proxy chat (CC 127), registration (CC 82), models (CC 43), and network assignment (CC 42).
+
+## Objective
+
+Deepen Tracker request intake around existing domain seams so HTTP dispatch stays thin and request-specific policy no longer leaks across unrelated control-plane workflows.
+
+## Constraints
+
+- Preserve ADR-0009: Tracker remains a control plane and public inference proxy, never a model host.
+- Preserve coverage-first assignment, billing, admission, relay, telemetry, Raft, and existing endpoint contracts.
+- Do not create a speculative adapter: each new seam must have at least two real callers/adapters or remain internal.
+
+## Acceptance criteria
+
+- [ ] Characterization tests pin all affected public endpoint response and error behavior before moving code.
+- [ ] HTTP dispatch delegates to cohesive intake modules for inference, node/registry lifecycle, and operator projections.
+- [ ] Route selection, billing attribution, admission, and coverage logic remain backend-agnostic and do not move into the HTTP module.
+- [ ] `_TrackerHandler` no longer owns unrelated endpoint policy directly.
+- [ ] Existing routing, capability-admission, billing, account, and consensus tests retain behavior.
+- [ ] `pytest` targeted tests and `python -m compileall packages tests` pass.
+
+## Likely files
+
+- Modify: `packages/tracker/meshnet_tracker/server.py`
+- Potentially modify: `packages/tracker/meshnet_tracker/billing.py`, `accounts.py`, `capability.py`, `recipe.py`
+- Modify/add: focused tests alongside `tests/test_tracker_routing.py`, `tests/test_tracker_capability_admission.py`, `tests/test_billing_ledger.py`, and `tests/test_tracker_consensus.py`
+
+## Non-goals
+
+No redesign of the Tracker architecture, no public endpoint removal, and no change to backend-neutral provider semantics.

.scratch/architecture-deepening/prd.json

@@ -0,0 +1,10 @@
+{
+  "name": "Architecture Deepening",
+  "description": "Deepen high-coupling Meshnet modules behind narrow interfaces while preserving current domain behavior and locked ADR decisions.",
+  "sourceOfTruth": "This prd.json and its issue files are planning artifacts; no task is approved for implementation until triaged.",
+  "stories": [
+    {"id":"AD-001","title":"Deepen Route Session execution behind one node seam","status":"needs-triage","priority":"p0","dependsOn":[],"blocks":["AD-002"],"files":["packages/node/meshnet_node/torch_server.py","tests/test_two_node_pipeline.py","tests/test_kv_cache_distributed.py"]},
+    {"id":"AD-002","title":"Deepen Node startup orchestration","status":"needs-triage","priority":"p1","dependsOn":["AD-001"],"blocks":[],"files":["packages/node/meshnet_node/startup.py","packages/node/meshnet_node/testing.py","tests/test_node_startup.py","tests/test_node_admission.py"]},
+    {"id":"AD-003","title":"Deepen Tracker request intake without changing control-plane semantics","status":"needs-triage","priority":"p1","dependsOn":["AD-001","AD-002"],"blocks":[],"files":["packages/tracker/meshnet_tracker/server.py","tests/test_tracker_routing.py"]}
+  ]
+}

AGENTS.md

@@ -15,3 +15,16 @@ Default mattpocock/skills label strings (`needs-triage`, `needs-info`, `ready-fo
 ### Domain docs
 
 Multi-context layout: `CONTEXT-MAP.md` at root points to per-context `CONTEXT.md` files; system-wide ADRs in `docs/adr/`, context-scoped ADRs in `src/<context>/docs/adr/`. See `docs/agents/domain.md`.
+
+## graphify
+
+This project has a knowledge graph at graphify-out/ with god nodes, community structure, and cross-file relationships.
+
+When the user types `/graphify`, use the installed graphify skill or instructions before doing anything else.
+
+Rules:
+- For codebase questions, first run `graphify query "<question>"` when graphify-out/graph.json exists. Use `graphify path "<A>" "<B>"` for relationships and `graphify explain "<concept>"` for focused concepts. These return a scoped subgraph, usually much smaller than GRAPH_REPORT.md or raw grep output.
+- Dirty graphify-out/ files are expected after hooks or incremental updates; dirty graph files are not a reason to skip graphify. Only skip graphify if the task is about stale or incorrect graph output, or the user explicitly says not to use it.
+- If graphify-out/wiki/index.md exists, use it for broad navigation instead of raw source browsing.
+- Read graphify-out/GRAPH_REPORT.md only for broad architecture review or when query/path/explain do not surface enough context.
+- After modifying code, run `graphify update .` to keep the graph current (AST-only, no API cost).

graphify-out/.graphify_labels.json

@@ -0,0 +1,125 @@
+{
+  "0": "Tracker Routing & Placement",
+  "1": "Validator Proof Audits",
+  "2": "Capability Admission",
+  "3": "Shard Artifact Download",
+  "4": "PyTorch Shard Execution",
+  "5": "Native Shard Lifecycle",
+  "6": "Tracker Operations & CLI",
+  "7": "Node Capability Reports",
+  "8": "Gateway Request Routing",
+  "9": "Activation Transport & Binary Frames",
+  "10": "Tracker Capability Evaluation",
+  "11": "Node Diagnostics",
+  "12": "Relay Peer Registry",
+  "13": "Native Worker Adapter",
+  "14": "KV Session Cache",
+  "15": "Performance Contracts",
+  "16": "Route Session Benchmarks",
+  "17": "GLM Alpha Planning",
+  "18": "Tracker Admin & Network APIs",
+  "19": "Benchmark Recipe Drivers",
+  "20": "Community 20",
+  "21": "Community 21",
+  "22": "Community 22",
+  "23": "Community 23",
+  "24": "Community 24",
+  "25": "Community 25",
+  "26": "Community 26",
+  "27": "Community 27",
+  "28": "Community 28",
+  "29": "Community 29",
+  "30": "Community 30",
+  "31": "Community 31",
+  "32": "Community 32",
+  "33": "Community 33",
+  "34": "Community 34",
+  "35": "Community 35",
+  "36": "Community 36",
+  "37": "Community 37",
+  "38": "Community 38",
+  "39": "Community 39",
+  "40": "Community 40",
+  "41": "Community 41",
+  "42": "Community 42",
+  "43": "Community 43",
+  "44": "Community 44",
+  "45": "Community 45",
+  "46": "Community 46",
+  "47": "Community 47",
+  "48": "Community 48",
+  "49": "Community 49",
+  "50": "Community 50",
+  "51": "Community 51",
+  "52": "Community 52",
+  "53": "Community 53",
+  "54": "Community 54",
+  "55": "Community 55",
+  "56": "Community 56",
+  "57": "Community 57",
+  "58": "Community 58",
+  "59": "Community 59",
+  "60": "Community 60",
+  "61": "Community 61",
+  "62": "Community 62",
+  "63": "Community 63",
+  "64": "Community 64",
+  "65": "Community 65",
+  "66": "Community 66",
+  "67": "Community 67",
+  "68": "Community 68",
+  "69": "Community 69",
+  "70": "Community 70",
+  "71": "Community 71",
+  "72": "Community 72",
+  "73": "Community 73",
+  "74": "Community 74",
+  "75": "Community 75",
+  "76": "Community 76",
+  "77": "Community 77",
+  "78": "Community 78",
+  "79": "Community 79",
+  "80": "Community 80",
+  "81": "Community 81",
+  "82": "Community 82",
+  "83": "Community 83",
+  "84": "Community 84",
+  "85": "Community 85",
+  "86": "Community 86",
+  "87": "Community 87",
+  "88": "Community 88",
+  "89": "Community 89",
+  "90": "Community 90",
+  "91": "Community 91",
+  "92": "Community 92",
+  "93": "Community 93",
+  "94": "Community 94",
+  "95": "Community 95",
+  "96": "Community 96",
+  "97": "Community 97",
+  "98": "Community 98",
+  "99": "Community 99",
+  "100": "Community 100",
+  "101": "Community 101",
+  "102": "Community 102",
+  "103": "Community 103",
+  "104": "Community 104",
+  "105": "Community 105",
+  "106": "Community 106",
+  "107": "Community 107",
+  "108": "Community 108",
+  "109": "Community 109",
+  "110": "Community 110",
+  "111": "Community 111",
+  "112": "Community 112",
+  "113": "Community 113",
+  "114": "Community 114",
+  "115": "Community 115",
+  "116": "Community 116",
+  "117": "Community 117",
+  "118": "Community 118",
+  "119": "Community 119",
+  "120": "Community 120",
+  "121": "Community 121",
+  "122": "Community 122"
+}

graphify-out/.graphify_python

@@ -0,0 +1 @@
+C:\Users\popov\AppData\Roaming\uv\tools\graphifyy\Scripts\python.exe

graphify-out/.graphify_root

@@ -0,0 +1 @@
+D:\DEV\workspace\REPOS\git.d-popov.com\neuron-tai\packages

graphify-out/GRAPH_REPORT.md

@@ -0,0 +1,586 @@
+# Graphify Code-Architecture Scope
+
+This graph deliberately covers the executable `packages/` code (98 detected source/configuration files). The six package-local documentation/build artifacts were excluded from semantic extraction for this code-only pass.
+
+# Graph Report - packages  (2026-07-29)
+
+## Corpus Check
+- 106 files · ~188,575 words
+- Verdict: corpus is large enough that graph structure adds value.
+
+## Summary
+- 2556 nodes · 5763 edges · 123 communities (100 shown, 23 thin omitted)
+- Extraction: 92% EXTRACTED · 8% INFERRED · 0% AMBIGUOUS · INFERRED: 451 edges (avg confidence: 0.51)
+- Token cost: 0 input · 0 output
+
+## Community Hubs (Navigation)
+- Tracker Routing & Placement
+- Validator Proof Audits
+- Capability Admission
+- Shard Artifact Download
+- PyTorch Shard Execution
+- Native Shard Lifecycle
+- Tracker Operations & CLI
+- Node Capability Reports
+- Gateway Request Routing
+- Activation Transport & Binary Frames
+- Tracker Capability Evaluation
+- Node Diagnostics
+- Relay Peer Registry
+- Native Worker Adapter
+- KV Session Cache
+- Performance Contracts
+- Route Session Benchmarks
+- GLM Alpha Planning
+- Tracker Admin & Network APIs
+- Benchmark Recipe Drivers
+- Community 20
+- Community 21
+- Community 22
+- Community 23
+- Community 24
+- Community 25
+- Community 26
+- Community 27
+- Community 28
+- Community 29
+- Community 30
+- Community 31
+- Community 32
+- Community 33
+- Community 34
+- Community 35
+- Community 36
+- Community 37
+- Community 38
+- Community 39
+- Community 40
+- Community 41
+- Community 42
+- Community 43
+- Community 44
+- Community 45
+- Community 46
+- Community 47
+- Community 48
+- Community 49
+- Community 50
+- Community 51
+- Community 52
+- Community 53
+- Community 54
+- Community 55
+- Community 56
+- Community 57
+- Community 58
+- Community 59
+- Community 60
+- Community 61
+- Community 62
+- Community 63
+- Community 64
+- Community 65
+- Community 66
+- Community 67
+- Community 68
+- Community 69
+- Community 70
+- Community 71
+- Community 72
+- Community 73
+- Community 74
+- Community 75
+- Community 76
+- Community 77
+- Community 78
+- Community 79
+- Community 80
+- Community 81
+- Community 82
+- Community 83
+- Community 84
+- Community 85
+- Community 86
+- Community 87
+- Community 88
+- Community 89
+- Community 90
+- Community 91
+- Community 92
+- Community 93
+- Community 94
+- Community 95
+- Community 96
+- Community 97
+- Community 98
+- Community 99
+- Community 100
+- Community 101
+- Community 102
+- Community 103
+- Community 104
+- Community 105
+- Community 106
+- Community 107
+- Community 109
+- Community 110
+- Community 111
+- Community 112
+- Community 113
+- Community 114
+- Community 115
+- Community 116
+- Community 117
+- Community 118
+- Community 119
+- Community 120
+- Community 121
+- Community 122
+
+## God Nodes (most connected - your core abstractions)
+1. `_TrackerHandler` - 93 edges
+2. `_NodeEntry` - 50 edges
+3. `run_startup()` - 46 edges
+4. `BillingLedger` - 45 edges
+5. `TorchModelShard` - 41 edges
+6. `AccountStore` - 37 edges
+7. `TrackerServer` - 37 edges
+8. `CapabilityReport` - 35 edges
+9. `RaftNode` - 33 edges
+10. `_StatsCollector` - 32 edges
+
+## Surprising Connections (you probably didn't know these)
+- `main()` --calls--> `LocalSolanaContracts`  [INFERRED]
+  tracker/meshnet_tracker/cli.py → contracts/meshnet_contracts/__init__.py
+- `main()` --calls--> `SolanaCustodialTreasury`  [INFERRED]
+  tracker/meshnet_tracker/cli.py → contracts/meshnet_contracts/solana_adapter.py
+- `_RollingCounter` --uses--> `ToplocProofClaim`  [INFERRED]
+  tracker/meshnet_tracker/server.py → validator/meshnet_validator/audit.py
+- `_RollingThroughput` --uses--> `ToplocProofClaim`  [INFERRED]
+  tracker/meshnet_tracker/server.py → validator/meshnet_validator/audit.py
+- `_ModelStats` --uses--> `ToplocProofClaim`  [INFERRED]
+  tracker/meshnet_tracker/server.py → validator/meshnet_validator/audit.py
+
+## Import Cycles
+- None detected.
+
+## Communities (123 total, 23 thin omitted)
+
+### Community 0 - "Tracker Routing & Placement"
+Cohesion: 0.06
+Nodes (84): tracker_logger(), _add_shard_directive(), _assign_redundant_managed_nodes(), _assignment_memory_bytes(), _available_quantizations(), _billable_stream_tokens(), _coverage_gaps(), _coverage_map() (+76 more)
+
+### Community 1 - "Validator Proof Audits"
+Cohesion: 0.05
+Nodes (57): ProofEncoding, build_activation_proofs(), _call_toploc(), _chunk_field(), _extract_divergence(), _load_toploc(), _proof_encoding(), Any (+49 more)
+
+### Community 2 - "Capability Admission"
+Cohesion: 0.05
+Nodes (25): CapabilityState, The tracker's sanitized verdict on one node's presented proof. This is what the…, The presented proof covers exactly what the node advertised., What route formation compares. `None` when the node declares no identity., CertificationLedger, DistributedForwardEvidence, PresentedIdentity, One node's declared artifact/recipe identity, with digests re-derived here.… (+17 more)
+
+### Community 3 - "Shard Artifact Download"
+Cohesion: 0.06
+Nodes (51): _allow_patterns_from_remote_index(), _allow_patterns_from_sources(), compute_shard_checksum(), _download_huggingface_subset(), _download_model_source(), download_shard(), _download_shard_from_peer(), _download_source_files() (+43 more)
+
+### Community 4 - "PyTorch Shard Execution"
+Cohesion: 0.08
+Nodes (55): _active_modules_for_shard(), build_quantization_config(), _call_layer(), _causal_lm_config(), _checkpoint_tensor_name_for_model(), _config_candidates(), _decoder_attention_mask(), _embed_tokens() (+47 more)
+
+### Community 5 - "Native Shard Lifecycle"
+Cohesion: 0.06
+Nodes (35): CacheExpectation, CacheResult, CancellationToken, CancelRequest, CapabilityRequest, CapabilityResponse, DeadlinePolicy, FlowControl (+27 more)
+
+### Community 6 - "Tracker Operations & CLI"
+Cohesion: 0.05
+Nodes (42): HTMLParser, Logger, LogRecord, Namespace, RotatingFileHandler, TextIO, _load_env_defaults(), _load_env_file() (+34 more)
+
+### Community 7 - "Node Capability Reports"
+Cohesion: 0.06
+Nodes (40): CapabilityContext, probe_capability(), Production validator: one bounded real forward through the loaded shard., What is about to be advertised, and the loaded backend that would serve it., BackendIdentity, build_capability_report(), CapabilityReport, config_fingerprint() (+32 more)
+
+### Community 8 - "Gateway Request Routing"
+Cohesion: 0.07
+Nodes (50): Any, route_signature(), _admitted_nodes(), _billable_non_stream_split(), _billable_non_stream_tokens(), _capability_routable(), _clear_proxy_progress_log_state(), _effective_queue_depth() (+42 more)
+
+### Community 9 - "Activation Transport & Binary Frames"
+Cohesion: 0.06
+Nodes (43): ConnectionError, MissingModelDependencyError, Tail-shard decode result: decoded text plus the raw token id. The token id lets…, Raised when optional model dependencies are not installed., TailTokenResult, _tensor_from_bfloat16_bytes(), decode_binary_frame(), encode_binary_frame() (+35 more)
+
+### Community 10 - "Tracker Capability Evaluation"
+Cohesion: 0.09
+Nodes (46): absent_state(), catalogue_is_compatible(), _diagnostics(), evaluate_report(), _index(), _maybe_int(), _object(), _optional_text() (+38 more)
+
+### Community 11 - "Node Diagnostics"
+Cohesion: 0.08
+Nodes (40): _backend_device(), _backend_device_name(), build_probe_input(), classify_failure(), default_load_backend(), _describe(), _describe_output(), DoctorError (+32 more)
+
+### Community 12 - "Relay Peer Registry"
+Cohesion: 0.07
+Nodes (22): main(), meshnet-relay CLI entry point., PeerEntry, PeerRegistry, In-memory registry of connected gossip peers., _broadcast(), decode_binary_frame(), encode_binary_frame() (+14 more)
+
+### Community 13 - "Native Worker Adapter"
+Cohesion: 0.08
+Nodes (28): ImmutableArtifactPin, NativeIdentityInputs, NativeLoadedArtifactReport, NativeNumericalRecipe, NativeSessionRejected, NativeWorkerBackendAdapter, Authoritative identity boundary for a native GGUF Shard backend. The native…, A native worker refused a ``SessionOpen`` before allocating session state. (+20 more)
+
+### Community 14 - "KV Session Cache"
+Cohesion: 0.07
+Nodes (20): _cache_unsupported_for_shard(), KVCacheMiss, BaseException, True when a layer failure means session cache is unsupported, not fatal., Per-session cached state for one shard's layer range. `cache` is whatever…, TTL + LRU bounded map of session_id → SessionCacheEntry. Each node caches state…, Executable subset of a HuggingFace causal language model., Decode step: embed one new token against this head's cached session. Raises… (+12 more)
+
+### Community 15 - "Performance Contracts"
+Cohesion: 0.10
+Nodes (35): baseline_from_report(), _canonical_sha256(), _cell(), ContractEvaluation, ContractThresholds, _decode_base64(), evaluate_contract(), _evaluate_recipe() (+27 more)
+
+### Community 16 - "Route Session Benchmarks"
+Cohesion: 0.09
+Nodes (33): CacheMode, _activation(), assert_benchmark(), assert_performance_gate(), BenchmarkRun, BenchmarkScenario, format_summary(), main() (+25 more)
+
+### Community 17 - "GLM Alpha Planning"
+Cohesion: 0.11
+Nodes (28): IndexerLayout, The locked GLM-5.2 Max alpha target: identity, resource plan, and acceptance…, ArchitectureSnapshot, Architecture-critical metadata derived from the pinned ``config.json``., kv_bytes(), NodeMemory, plan_all_tiers(), plan_route() (+20 more)
+
+### Community 18 - "Tracker Admin & Network APIs"
+Cohesion: 0.11
+Nodes (11): _normalize_current_requests(), ParseResult, Gate a privileged handler; sends 401/403 and returns False on failure. 401 when…, Return head workers: worker nodes that can start inference for a model. The…, Admin session first, then the explicit enable gate — both fail closed., Privileged: forfeit a node's pending balance + record a strike (US-034).…, Dispute-auditability log for the dynamic HF-benchmarked pricing (issue 23)., Return an optimal shard assignment for a node given its hardware profile. Query… (+3 more)
+
+### Community 19 - "Benchmark Recipe Drivers"
+Cohesion: 0.12
+Nodes (34): Ed25519PrivateKey, BenchmarkError, RuntimeError, Raised when a benchmark cannot be run as specified., _artifact_sha256(), build_driver(), _directory_bytes(), _gpu_offload_evidence() (+26 more)
+
+### Community 20 - "Community 20"
+Cohesion: 0.10
+Nodes (23): deque, Popen, CollectionError, discover_repo_root(), _function_metadata(), Exception, Path, Opt-in tracker test runner backing the dashboard Testing tab. Security posture… (+15 more)
+
+### Community 21 - "Community 21"
+Cohesion: 0.10
+Nodes (20): Random, Minimal Raft consensus for tracker shard assignments. Only shard-assignment…, choose_route(), Learned route statistics for dynamic bandit-style route selection (ADR-0021).…, Fold one completed request into the route's EWMA. Returns False (and records…, Point-in-time view of one route's learned state., All measured route samples, including pinned experiment routes., Drop routes with no samples for `prune_after_seconds`. (+12 more)
+
+### Community 22 - "Community 22"
+Cohesion: 0.10
+Nodes (9): AccountStore, _hash_password(), _normalize_nickname(), Tracker user accounts: registration, login, API-key management. Accounts are…, Update display fields for an account. Pass nickname=None to clear., Return the public account view when credentials match, else None., Revoke a key owned by ``account_id``. Returns False if not owned., Thread-safe account/API-key store with SQLite persistence and event replication. (+1 more)
+
+### Community 23 - "Community 23"
+Cohesion: 0.12
+Nodes (24): ProbeInput, One recipe's validation outcome, with the report it produced., A synthetic hidden-state payload in the same wire format peers send., RecipeResult, load_recipe_manifest(), _parse_recipe(), parse_recipe_manifest(), Any (+16 more)
+
+### Community 24 - "Community 24"
+Cohesion: 0.11
+Nodes (31): Return a hardware profile forced to CPU execution. Keeps detected GPU metadata…, with_forced_cpu(), peer_id_from_wallet(), Build a per-node relay peer id from the wallet plus node identity. Multiple…, _assignment_bytes_per_layer(), _cap_auto_assigned_shard(), _configure_torch_threads(), _discover_relay_url() (+23 more)
+
+### Community 25 - "Community 25"
+Cohesion: 0.12
+Nodes (18): ChatCompletion, _chat_completion(), Client, CostEstimate, _ModelsClient, _openai_base_url(), Any, Typed Python SDK for the meshnet OpenAI-compatible gateway. (+10 more)
+
+### Community 26 - "Community 26"
+Cohesion: 0.11
+Nodes (15): Deposit, load_keypair(), Custodial Solana treasury adapter (ADR-0015, US-032/US-033). The entire on-…, Confirmed incoming USDT transfers whose signature is not yet seen.…, Send one batched transaction of USDT transfers treasury → wallets. Creates the…, Create a fresh 6-decimal mock-USDT mint (treasury = mint authority). Returns a…, Mint mock USDT to a wallet (devnet only — treasury is mint authority)., A confirmed incoming USDT transfer into the treasury token account. (+7 more)
+
+### Community 27 - "Community 27"
+Cohesion: 0.12
+Nodes (22): compress_activation(), CompressionPolicies, CompressionPolicy, CompressionResult, decompress_activation(), _env_bool(), _env_float(), _env_int() (+14 more)
+
+### Community 28 - "Community 28"
+Cohesion: 0.09
+Nodes (19): Lock, normalize_policy(), policy_from_env(), Return a known policy name, falling back to the default for anything else., HfPricingLog, Thread-safe SQLite-backed audit log of dynamic price changes (issue 23). Every…, _clone_model_presets(), _preset_price_keys() (+11 more)
+
+### Community 29 - "Community 29"
+Cohesion: 0.12
+Nodes (28): _apply_relay_concurrency_flag(), _cmd_default(), _cmd_doctor(), _cmd_models(), _cmd_start(), _doctor_overrides(), _first_available_port(), _load_env_defaults() (+20 more)
+
+### Community 30 - "Community 30"
+Cohesion: 0.11
+Nodes (14): _LoopbackHttpClientPool, _make_envelope(), _max_concurrency_from_env(), _peer_id_suffix(), HTTPConnection, Outbound relay bridge for NAT-safe node HTTP requests., Connect outbound to a relay and proxy relay HTTP requests to localhost.…, Send one relay-http-response frame; False if the socket is gone. The lock is… (+6 more)
+
+### Community 31 - "Community 31"
+Cohesion: 0.09
+Nodes (17): _capability_from_registration(), _local_relay_url(), _node_id_for_registration(), _normalize_friendly_name(), Resolve the active precision a registration is routable at. Only the raw body…, Return a node-consumable ws:// URL for an embedded relay bind address., HTTP tracker that manages node registration and resolves inference routes.…, Start the shared RelayServer class in-process for tracker+relay deployments. (+9 more)
+
+### Community 32 - "Community 32"
+Cohesion: 0.09
+Nodes (12): validate_quantization(), _load_backend(), Any, Path, HTTP server backed by a HuggingFace causal language model shard., The loaded backend serving `model_id` — full repo id or short name., Apply tracker shard directives (LOAD_SHARD replace, ADD_SHARD load-more)., Set the LAN-facing endpoint used for route self-detection. (+4 more)
+
+### Community 33 - "Community 33"
+Cohesion: 0.16
+Nodes (25): Checksum, checksum_of(), crc32c(), decode_bundle(), decode_step_bundle(), decode_tensor(), encode_tensor(), expected_bytes() (+17 more)
+
+### Community 34 - "Community 34"
+Cohesion: 0.12
+Nodes (22): _api_key_from_authorization(), _BinaryActivation, _completion_response(), _compress_body(), _decompress_body(), _estimate_token_count(), _last_message_content(), _majority_response() (+14 more)
+
+### Community 35 - "Community 35"
+Cohesion: 0.15
+Nodes (23): AlphaContract, AlphaContractError, compute_contract_digest(), contract_signing_payload(), _freeze_json(), load_alpha_contract(), parse_alpha_contract(), Any (+15 more)
+
+### Community 36 - "Community 36"
+Cohesion: 0.13
+Nodes (8): LogEntry, RaftNode, Leader: append and replicate an entry. Returns True when committed. Blocks…, Send AppendEntries to all peers and update commit_index on majority ack., Single Raft participant. ``apply_fn(command, payload)`` is called (under no…, Must be called with _lock held., Must be called with _lock held., Must be called with _lock held.
+
+### Community 37 - "Community 37"
+Cohesion: 0.13
+Nodes (18): _build_rich_renderable(), _EMA, _format_uptime(), _gpu_stats(), is_interactive_tty(), _make_bar(), _node_stats(), _nvml_gpu_util() (+10 more)
+
+### Community 38 - "Community 38"
+Cohesion: 0.14
+Nodes (25): browse_hf_hub(), detect_num_layers(), Return num_hidden_layers from HuggingFace config.json (downloads ~1 KB only)., Fetch top downloaded text-generation models from HuggingFace Hub., _ask(), _ask_int(), _ask_quant(), _ask_yn() (+17 more)
+
+### Community 39 - "Community 39"
+Cohesion: 0.08
+Nodes (12): Drop session state out of band. Idempotent., Cancel out of band, on a fresh call. In-band CancelSignal is preferred, but a…, ---------------------------------------------------------------------------…, ---------------------------------------------------------------------------…, Constructor. Args: channel: A grpc.Channel., ---------------------------------------------------------------------------…, What this worker can execute. Read before a route is built., Live load and serving state. (+4 more)
+
+### Community 40 - "Community 40"
+Cohesion: 0.13
+Nodes (19): check_handshake(), check_route(), check_session_open(), _coverage_gap(), explain_mismatch(), handshake_error(), Exact Model Artifact and runtime recipe identity (DGR-003). A route is a chain…, The protocol status a rejected handshake closes the stream with. Each rejection… (+11 more)
+
+### Community 41 - "Community 41"
+Cohesion: 0.13
+Nodes (16): adapter_for(), Architecture, ArchitectureBoundaryAdapter, BoundaryStage, ProtocolIdentity, Enum, str, Certified architecture adapters for the public TensorBundle boundary. The… (+8 more)
+
+### Community 42 - "Community 42"
+Cohesion: 0.24
+Nodes (22): GlmTargetError, load_architecture_snapshot(), _load_json(), load_target_manifest(), parse_architecture_snapshot(), _parse_shards(), parse_target_manifest(), Any (+14 more)
+
+### Community 43 - "Community 43"
+Cohesion: 0.16
+Nodes (10): _canonical_json(), _fragment_bytes(), NamedTensor, _normalize_shape(), Any, Versioned activation-stream envelope for shard hops. The transport still moves…, A tensor named within a versioned activation envelope., One bounded chunk of a named tensor. (+2 more)
+
+### Community 44 - "Community 44"
+Cohesion: 0.14
+Nodes (6): Read + verify a hive gossip body (HMAC per ADR-0017 §3). Fails closed: without…, Record a rolling wish-list signal for an unavailable precision., Privileged: run the same prompt through 1/2/3-node pinned routes (US-030). Data…, Privileged: honest-noise TOPLOC calibration dispatch (issue 21). Fans the same…, One node's calibration outcome: fetch its on-demand commitment, teacher-force…, _TrackerHandler
+
+### Community 45 - "Community 45"
+Cohesion: 0.18
+Nodes (8): Registry wrapper for node stake, strikes, and bans., Stake, strike, and ban state for a node operator wallet., Snapshot of all known wallets (dashboard / monitoring)., ADR-0018 §6: the only reputation signal — clean audits build score slowly, a…, ADR-0018 §6: ×0.8 routing/payout weight per strike, separate from the…, ADR-0018 §6: reputation decays for wallets with no completed job in…, RegistryContract, RegistryWallet
+
+### Community 46 - "Community 46"
+Cohesion: 0.12
+Nodes (22): benchmark_throughput(), benchmark_throughput_checked(), detect_hardware(), _detect_nvidia_smi_gpu_memory(), _detect_ram_mb(), _detect_torch_cuda_inventory(), _detect_windows_gpu_memory(), _detect_windows_ram_mb() (+14 more)
+
+### Community 47 - "Community 47"
+Cohesion: 0.14
+Nodes (15): measure_recipe(), _PeakMemory, Event, One runtime recipe under test. ``is_reference`` marks the single recipe every…, The seam every runtime implements; the measurement core knows nothing else., Load the artifact and return its cost., Run one complete generation under the given sampling policy., Return ``(rss_bytes, vram_bytes)`` observed right now. (+7 more)
+
+### Community 48 - "Community 48"
+Cohesion: 0.25
+Nodes (12): _as_mapping(), Any, ValueError, Malformed identity input. Messages name the field, never echo a payload., A revision must identify one immutable thing, not a ref that moves., Parse an identity block, re-deriving — never trusting — its fingerprint. A…, RecipeIdentityError, _require_hex64() (+4 more)
+
+### Community 49 - "Community 49"
+Cohesion: 0.19
+Nodes (3): Stream tokens from an iterator as SSE chunks., Open an OpenAI-compatible SSE response and return a token emitter., _TorchHandler
+
+### Community 50 - "Community 50"
+Cohesion: 0.26
+Nodes (4): _GatewayHandler, _lamports_to_sol(), ParseResult, Forward a raw request body to a head worker and relay SSE without buffering.
+
+### Community 51 - "Community 51"
+Cohesion: 0.23
+Nodes (10): _as_mapping(), CapabilityReportError, _optional_text(), Any, ValueError, Inclusive layer range, matching the CLI and backend convention., Raised when report input is malformed. Messages name the offending field and…, _require_int() (+2 more)
+
+### Community 52 - "Community 52"
+Cohesion: 0.13
+Nodes (17): ConcurrencyMetrics, format_summary(), Lane, main(), _mean(), _percentile(), Enum, str (+9 more)
+
+### Community 53 - "Community 53"
+Cohesion: 0.13
+Nodes (14): BenchmarkPlan, build_report(), compute_drift(), DriftReport, _first_divergence(), Any, Everything measured for one recipe across every concurrency level., First successful output per prompt, at the lowest concurrency measured. Drift… (+6 more)
+
+### Community 54 - "Community 54"
+Cohesion: 0.13
+Nodes (9): GossipClient, _make_envelope(), WebSocket gossip client — connects to relay, publish/subscribe to topics., Thread-safe WebSocket gossip client. Usage:: client =…, Register a sync callback for messages on topic., Send a gossip message to all peers via the relay. Thread-safe., Start the gossip client in a background thread., Block until connected to relay or timeout. Returns True if connected. (+1 more)
+
+### Community 55 - "Community 55"
+Cohesion: 0.19
+Nodes (17): files_for_layer_range(), _is_head_tensor(), _is_tail_tensor(), _layer_index(), _layers_from_config(), _metadata_files(), _normalise_relative_file(), Any (+9 more)
+
+### Community 56 - "Community 56"
+Cohesion: 0.18
+Nodes (6): Resolve the session token in the Authorization header, or None., Balance, usage totals, and API keys for the logged-in account., Per-request charge history for the logged-in account (billing tab)., Devnet faucet (US-040): credit the configured amount to one of the logged-in…, Admin-only: all accounts with their keys and balances., _session_cookie_header()
+
+### Community 57 - "Community 57"
+Cohesion: 0.20
+Nodes (15): _canonical_sha256(), load_runtime_pin(), Path, ValueError, Canonical runtime pin identity for the recipe fingerprint (DGR-025). The recipe…, Derive the exact runtime pin from a DGR-027 lock workspace, or refuse. Refuses…, The lock workspace is missing, malformed, or internally inconsistent., One exact runtime: a name, an upstream commit, and an ordered patch stack. (+7 more)
+
+### Community 58 - "Community 58"
+Cohesion: 0.16
+Nodes (8): GenerationSample, PromptSpec, One completed generation as reported by a driver. ``prefill_ms``/``decode_ms``…, The sampling policy every recipe must be given, identically. Greedy by default:…, One fixed prompt, tagged with the context length it is meant to exercise., SamplingPolicy, The current Transformers/safetensors recipe: the correctness reference.…, TransformersDriver
+
+### Community 59 - "Community 59"
+Cohesion: 0.16
+Nodes (8): LoadStats, What loading the recipe cost, before any token is generated., _free_port(), _gpu_layer_config_detail(), LlamaCppServerDriver, _process_rss(), Resident bytes for a process and its children, or 0 when unobservable., The whole-model llama.cpp/GGUF recipe, driven through ``llama-server``.…
+
+### Community 60 - "Community 60"
+Cohesion: 0.15
+Nodes (9): main(), meshnet-gateway CLI entry point., _GatewayHTTPServer, GatewayServer, _get_head_workers(), Any, Return head-worker endpoint URLs for this model, or empty list on failure., HTTP gateway that routes /v1/chat/completions through an ordered inference… (+1 more)
+
+### Community 61 - "Community 61"
+Cohesion: 0.15
+Nodes (15): _admit_capability(), _capability_device(), _capability_refresher(), _post_json(), _probationary_status_line(), Any, CapabilityValidator, Daemon thread: sends heartbeats and re-registers automatically after tracker… (+7 more)
+
+### Community 62 - "Community 62"
+Cohesion: 0.33
+Nodes (14): Fingerprint, CheckFingerprint(), path, Crc32c(), main(), ReadFile(), ReassembleUncompressed(), TestCapabilityReportVector() (+6 more)
+
+### Community 63 - "Community 63"
+Cohesion: 0.17
+Nodes (10): _local_model_path(), model_metadata_for(), ModelPreset, Path, Curated list of models supported by the network with VRAM requirements., Resolve a curated name, repository, or alias case-insensitively., Return operator-facing model metadata for a HuggingFace repo., Return VRAM requirement in GB for the given quantization. (+2 more)
+
+### Community 64 - "Community 64"
+Cohesion: 0.15
+Nodes (6): GenerationTelemetry, Bounded, in-process telemetry for distributed activation seams. The generation…, Attach compression work to the same bounded seam aggregate., Aggregate activation measurements for one stable Route Session., Record one activation locally and say whether a summary is due., _SeamAggregate
+
+### Community 65 - "Community 65"
+Cohesion: 0.16
+Nodes (6): _Listener, _local_ip(), MdnsDiscovery, mDNS peer discovery using zeroconf (optional dependency). Falls back gracefully…, Internal zeroconf service listener., Announce this node on mDNS and discover peers on the same LAN. If `zeroconf` is…
+
+### Community 66 - "Community 66"
+Cohesion: 0.13
+Nodes (5): BillingLedger, Wallets due a payout: pending ≥ threshold OR pending age ≥ max_period, never…, Settlement batches whose pending was debited but whose transaction has not been…, Thread-safe USDT ledger with SQLite persistence and event replication., Aggregate charge totals without per-request records (dashboard summary).
+
+### Community 67 - "Community 67"
+Cohesion: 0.14
+Nodes (6): NodeGossip, CRDT gossip for node liveness heartbeats. Uses a last-write-wins (LWW) register…, LWW gossip table for inference-node heartbeat timestamps. ``record(node_id)``…, Record a heartbeat for *node_id* at *wall_ts* (default: now)., Merge a gossip snapshot from a peer tracker (LWW per key)., Return wall-clock timestamp of last known heartbeat, or None.
+
+### Community 68 - "Community 68"
+Cohesion: 0.21
+Nodes (10): AdmissionRequirement, admit(), CapabilityAdmissionError, _diagnostics_suffix(), _mismatch(), RuntimeError, Fail-closed admission: no routable registration without a fresh matching proof.…, Return `report` if it admits `requirement`; otherwise refuse to register.… (+2 more)
+
+### Community 69 - "Community 69"
+Cohesion: 0.14
+Nodes (6): Grant the one-time Caller Credit for an account (US-039). The event id is…, Bind a client wallet pubkey to an API key (US-032 deposits, C6). A wallet…, Credit an on-chain deposit exactly once. The event id embeds the transaction…, Record the on-chain transaction signature for a settlement batch., Deduct a paid-out amount from a node's pending balance (US-033 hook). ADR-0015:…, Apply peer events not yet seen locally. Returns how many applied.
+
+### Community 70 - "Community 70"
+Cohesion: 0.18
+Nodes (11): _api_key_from_headers(), Resolve the caller to (role, account). Roles: "validator" (service token),…, Bind a client wallet pubkey to an API key (US-032, C6). Deposits from that…, _session_token_from_headers(), b58decode(), binding_message(), Ed25519 proof-of-ownership for client wallet binding (ADR-0017 §5, issue C6).…, Decode a base58 string (Solana/Bitcoin alphabet) to bytes. (+3 more)
+
+### Community 71 - "Community 71"
+Cohesion: 0.21
+Nodes (8): _notify_slash(), Solana contract boundary for the Distributed Inference Network. The prototype…, Completed inference data consumed by fraud validators., Local slash transaction receipt., Validation event log consumed by the optimistic fraud detector., SlashReceipt, ValidationContract, ValidationEvent
+
+### Community 72 - "Community 72"
+Cohesion: 0.19
+Nodes (6): ApiKeyBalance, ComputeAttribution, PaymentContract, Client API key payment account balance., On-chain work attribution recorded by the gateway after inference., Payment wrapper for funded API keys and compute attribution.
+
+### Community 73 - "Community 73"
+Cohesion: 0.17
+Nodes (13): DecodeStep, encode_bundle(), encode_decode_step(), Encode a decode boundary, retaining the deliberate compact fallback., Reject an oversized complete stream frame, including protobuf overhead. Bundle…, validate_session_message_size(), canonical_decode_step(), canonical_session_request() (+5 more)
+
+### Community 74 - "Community 74"
+Cohesion: 0.18
+Nodes (7): Path, Single-process stub node that returns fixed inference responses. shard_start /…, True if this node received an activation tensor since it was started., Number of binary /forward chunks handled since this node was started., _StubHTTPServer, StubNodeServer, _attach_relay_bridge()
+
+### Community 75 - "Community 75"
+Cohesion: 0.21
+Nodes (11): cert_fingerprint(), generate_self_signed_cert(), make_client_ssl_context(), make_server_ssl_context(), Path, TLS certificate generation and fingerprint helpers for node-to-node comms., Return a client SSLContext. verify=False for self-signed TOFU connections., Generate a self-signed RSA-2048 cert valid for 10 years. Returns (cert_path,… (+3 more)
+
+### Community 76 - "Community 76"
+Cohesion: 0.23
+Nodes (8): _ceiling(), _false_positive_rate(), _floor(), _percentile(), TOPLOC honest-noise calibration corpus (ADR-0018 consequences, issue 21).…, Fraction of the (honest, by construction) corpus that would be flagged by the…, Whether the corpus is broad enough to enable production thresholds. Alpha…, Recommended tolerance constants derived from the corpus. `exp_intersections`…
+
+### Community 77 - "Community 77"
+Cohesion: 0.20
+Nodes (7): _positive_env(), PrefillTransferLimits, Bounded, ordered prefill transfer primitives. Prefill chunks mutate the…, Configuration for one ordered prefill seam., Current peers require ordered session-cache mutation, hence one ack., Hard accounting bound, including any future wider ack window., _chunk_token_count()
+
+### Community 78 - "Community 78"
+Cohesion: 0.31
+Nodes (10): _cmd_config(), Print current config., config_path(), delete_config(), load_config(), Path, Persistent node configuration — stored in ~/.config/meshnet/config.json., Return parsed config dict, or None if no config file exists. (+2 more)
+
+### Community 79 - "Community 79"
+Cohesion: 0.18
+Nodes (8): _detect_layers(), DoctorResult, Path, The outcome of a doctor run over one or more recipes., Write the capability report(s) as JSON. A failed run writes too., The human summary: what was validated, what to do if it failed., render_result(), write_reports()
+
+### Community 80 - "Community 80"
+Cohesion: 0.18
+Nodes (6): The pinned, self-consistent GLM-5.2 ``UD-IQ1_S`` target., Reject any target whose revisions are not the ones alpha was locked against.…, One GGUF shard of the alpha artifact., require_pinned_target(), Shard, TargetManifest
+
+### Community 81 - "Community 81"
+Cohesion: 0.22
+Nodes (9): _hive_digest(), is_validator_token(), Unified tracker auth primitives (ADR-0017, alpha issues 01/02/20). Two…, Headers a tracker attaches when pushing gossip to a hive peer., True only when the request carries a fresh, valid hive signature. Fails closed:…, Constant-time check of a presented bearer token against the configured…, sign_hive_request(), verify_hive_request() (+1 more)
+
+### Community 82 - "Community 82"
+Cohesion: 0.29
+Nodes (3): Thread-safe registry wallet event log with SQLite persistence., _registry_wallet_with(), RegistryEventLog
+
+### Community 83 - "Community 83"
+Cohesion: 0.22
+Nodes (7): BoundedPrefillSender, Event, Release accounting after cancellation or route failure. The sender deliberately…, Send lazily-produced chunks with bounded ownership and ordered acks., Forward chunks in source order, releasing each body after its ack. ``forward``…, R, T
+
+### Community 84 - "Community 84"
+Cohesion: 0.20
+Nodes (8): default_flow_control(), canonical_capability_report(), canonical_payload(), Canonical conformance vectors for the native Shard protocol. Two independently-…, The canonical capability report a worker answers admission with., Serialize deterministically, so committed golden bytes are stable., Deterministic bfloat16-sized payload for the canonical tensor., serialize()
+
+### Community 85 - "Community 85"
+Cohesion: 0.25
+Nodes (5): Summary returned by an epoch settlement transaction., Settlement wrapper that debits clients and credits token rewards., Return the configured manual testnet deployment targets., SettlementContract, SettlementResult
+
+### Community 86 - "Community 86"
+Cohesion: 0.39
+Nodes (7): _banned_route_wallet(), _get_json(), _ModelUnavailable, Exception, HTTPError, _safe_error_body(), _TrackerUnavailable
+
+### Community 87 - "Community 87"
+Cohesion: 0.31
+Nodes (4): An immutable, request-owned binary activation payload. ``body`` is always the…, TensorPayload, ActivationEnvelope, Versioned envelope for shard activation traffic.
+
+### Community 88 - "Community 88"
+Cohesion: 0.22
+Nodes (6): canonical_sha256(), _digest(), SHA-256 over canonical JSON — the repository's digest convention., The artifact half of the compatibility fingerprint. Commits to the source…, The recipe half of the compatibility fingerprint., This Shard's own bytes and exact range, bound to the source artifact. The…
+
+### Community 89 - "Community 89"
+Cohesion: 0.29
+Nodes (8): _detect_num_layers(), _downloaded_model_inventory(), _model_cache_path(), Path, Only checksum tiny stub shards; real model folders are too large to hash at…, Fetch num_hidden_layers from HuggingFace model config (downloads ~1 KB…, Return a cheap local inventory record without reading model file contents., _registration_shard_checksum()
+
+### Community 90 - "Community 90"
+Cohesion: 0.32
+Nodes (7): _generate_keypair(), load_or_create_identity(), _peer_id_from_pubkey(), Path, Peer identity — stable peer_id and RSA keypair, persisted to disk., Return (private_key_pem, public_key_pem) for a new RSA-2048 keypair., Return identity dict with peer_id, private_key_pem, public_key_pem. Creates and…
+
+### Community 92 - "Community 92"
+Cohesion: 0.29
+Nodes (4): ChunkInfo, PrefillChunk, One token-aligned slice of a prefill., PositionSpan
+
+### Community 93 - "Community 93"
+Cohesion: 0.29
+Nodes (3): _LocalContractState, LocalSolanaContracts, Facade that exposes all three contract wrappers over local validator state.
+
+### Community 94 - "Community 94"
+Cohesion: 0.29
+Nodes (3): _normalize_rates(), Off-chain USDT billing ledger (ADR-0015, US-031). Tracks client API-key…, Coerce a price spec into an (input_per_1k, output_per_1k) pair.
+
+### Community 95 - "Community 95"
+Cohesion: 0.40
+Nodes (5): _b58encode(), load_or_create_wallet(), Path, Solana wallet management — load or generate an Ed25519 keypair. Solana keypair…, Return (secret_32, public_32, address_base58). Loads from *path* if it exists;…
+
+### Community 96 - "Community 96"
+Cohesion: 0.33
+Nodes (3): Debit the client and split the fee 90/10. With…, Blended (average) per-1k rate — kept for estimators and history logs., (input_per_1k, output_per_1k) for a model (US-045).
+
+### Community 97 - "Community 97"
+Cohesion: 0.40
+Nodes (3): canonical_sha256(), Stable identity of this manifest, for the DGR-003 runtime recipe., SHA-256 over canonical JSON — the repository's digest convention.
+
+### Community 99 - "Community 99"
+Cohesion: 0.50
+Nodes (4): _coverage_map_detailed(), _node_health(), Per-node health detail for the availability map., Like _coverage_map but with per-node identity and health in each band. Includes…
+
+### Community 100 - "Community 100"
+Cohesion: 0.67
+Nodes (3): HTTPError, Describe an HTTP rejection from the tracker, including its JSON error., _tracker_http_error_message()
+
+## Knowledge Gaps
+- **16 isolated node(s):** `meshnet-contracts`, `meshnet-gateway`, `CapabilityRequest`, `HealthRequest`, `HealthResponse` (+11 more)
+  These have ≤1 connection - possible missing edges or undocumented components.
+- **23 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
+
+## Suggested Questions
+_Questions this graph is uniquely positioned to answer:_
+
+- **Why does `_coverage_map_detailed()` connect `Community 99` to `Tracker Routing & Placement`, `Community 48`, `Tracker Admin & Network APIs`?**
+  _High betweenness centrality (0.315) - this node is a cross-community bridge._
+- **Why does `DerivativeBinding` connect `Native Worker Adapter` to `Community 40`, `Community 48`?**
+  _High betweenness centrality (0.261) - this node is a cross-community bridge._
+- **Why does `NativeWorkerBackendAdapter` connect `Native Worker Adapter` to `Node Capability Reports`, `Node Diagnostics`, `Community 79`, `Community 48`, `Community 23`?**
+  _High betweenness centrality (0.186) - this node is a cross-community bridge._
+- **Are the 18 inferred relationships involving `_TrackerHandler` (e.g. with `AccountStore` and `BillingLedger`) actually correct?**
+  _`_TrackerHandler` has 18 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 18 inferred relationships involving `_NodeEntry` (e.g. with `AccountStore` and `BillingLedger`) actually correct?**
+  _`_NodeEntry` has 18 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 10 inferred relationships involving `BillingLedger` (e.g. with `_ActiveProxyContext` and `_ModelStats`) actually correct?**
+  _`BillingLedger` has 10 INFERRED edges - model-reasoned connections that need verification._
+- **Are the 9 inferred relationships involving `TorchModelShard` (e.g. with `ActivationEnvelope` and `_DirectHopClient`) actually correct?**
+  _`TorchModelShard` has 9 INFERRED edges - model-reasoned connections that need verification._

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