docs: consolidate all docs under docs/ — single source of truth

Move issues (01–29) and PRD from .scratch/distributed-inference-network/
into docs/issues/ and docs/. Update ralph_progress.py DEFAULT_PRD path
and rewrite docs/agents/issue-tracker.md to reflect the new layout.

The distributed_inference_network.egg-info/docs/ mirror is a build
artifact already covered by *.egg-info/ in .gitignore — not committed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/issues/26-smart-model-assignment.md

@@ -0,0 +1,32 @@
+# US-026 — Smart model assignment via demand×coverage scoring
+
+Status: done
+Priority: Medium
+Stage: Implemented
+
+## Context
+
+`/v1/network/assign` currently picks the model with the largest uncovered shard gap,
+ignoring traffic. A model serving 1000 RPM at 60% coverage is far more valuable to fill
+than a zero-traffic model at 50% coverage.
+
+## Scoring formula
+
+```
+score = (demand_rpm + 1.0) × (coverage_deficit + 0.01)
+```
+
+- `demand_rpm`: combined RPM from `_StatsCollector.get_combined_stats()`
+- `coverage_deficit`: fraction of model layers with zero node coverage, in [0.0, 1.0]
+- `+1.0` floor: models with no traffic still compete by coverage
+- `+0.01` floor: fully-covered models still have a non-zero score if they have traffic
+
+`price_per_token: 0.0` reserved in the response for future billing integration.
+
+## Acceptance criteria
+
+- [ ] `_handle_network_assign` computes score per model and returns the highest
+- [ ] Demand uses combined stats (local + peer slices)
+- [ ] `price_per_token: 0.0` present in response
+- [ ] Test: high-demand low-coverage model beats low-demand high-coverage model
+- [ ] `python -m pytest` passes