chore: Archive completed task files (M0, M1, M3, M3.5, M4.1-2, M3.6.1)

Deleted 31 completed task files:
- M0.x: 8 tasks (cargo, domain types, recordsource, tokenizer, adapters, gate)
- M1.x: 8 tasks (llm-chat, standing-query, prompt template, parser, loop, log, e2e, gate)
- M3.x: 4 tasks (l2-synthesis, rerank, mem-query, gate)
- M3.5.x: 8 tasks (http-server, ingest, query, federation, skills, projects, rate-limiting, gate)
- M3.6.1: DocCorpusSource (heading-boundary chunking)
- M4.1-2: skill-draft, derived-filter

Updated INDEX.md:
- Removed M0 & M1 phase sections (archived in git history)
- Updated progress table: 65 active tasks (42✅ + 2🟡 + 21⬜)
- Updated status: M0/M1 complete, M3/M3.5 gates passing, M4.1-2 done
- Noted M3.5.10 JWT auth implementation complete (awaiting image rollout)
- Cleaned up broken links to deleted task files

Total test count: 239 passing, 2 ignored (up from 196 at M3.4)
Ready for M4.3 gate composition, M5 post-training, M7 source connectors.

tasks/M8.8-accuracy-benchmarks.md

@@ -0,0 +1,105 @@
+# M8.8 — Accuracy benchmarks: NDCG, MRR, Precision/Recall
+
+| Field | Value |
+|---|---|
+| Phase | M8 — Hybrid Search |
+| Size | M — 1–2 days |
+| Status | ⬜ |
+| Flags | — |
+| Spec | inlined below |
+| Blocks | M8.9 |
+| Depends | M8.6 (hybrid endpoint working), M8.7 (indices tuned) |
+
+## Goal
+
+Build a repeatable benchmark harness that measures retrieval accuracy. Run it against semantic-only, lexical-only, and hybrid strategies. Produce a comparison table that proves hybrid is better (or shows where it isn't).
+
+## Design
+
+**Test fixture format:**
+```yaml
+# fixtures/search_queries.yaml
+queries:
+  - id: q1
+    text: "How do I fix kubernetes port 8080 conflict?"
+    relevant_docs: ["runbooks/networking.md"]
+    category: troubleshooting
+
+  - id: q2
+    text: "What is a StatefulSet?"
+    relevant_docs: ["docs/kubernetes-concepts.md", "docs/statefulsets.md"]
+    category: factual
+
+  - id: q3
+    text: "#networking firewall rules"
+    relevant_docs: ["docs/network-policies.md"]
+    category: navigational
+```
+
+**Metrics implemented:**
+- **NDCG@10** — Are relevant docs ranked near the top? (0.0 worst, 1.0 perfect).
+- **MRR** — How early is the first relevant doc? (1/rank of first hit).
+- **Precision@5** — What fraction of top-5 results are relevant?
+- **Recall@10** — What fraction of all relevant docs appear in top-10?
+
+**Benchmark runner:**
+```rust
+pub struct BenchmarkResult {
+    pub strategy: String,          // "hybrid", "semantic", "lexical"
+    pub avg_ndcg: f32,
+    pub avg_mrr: f32,
+    pub avg_precision_at_5: f32,
+    pub avg_recall_at_10: f32,
+    pub avg_latency_ms: f32,
+    pub per_query: Vec<QueryBenchmark>,
+}
+```
+
+**Output:** Markdown table written to `docs/BENCHMARK_RESULTS.md`.
+
+```markdown
+| Strategy | NDCG@10 | MRR | P@5 | R@10 | Latency (ms) |
+|----------|---------|-----|-----|------|-------------|
+| semantic | 0.72 | 0.65 | 0.60 | 0.75 | 95 |
+| lexical | 0.68 | 0.70 | 0.55 | 0.70 | 62 |
+| hybrid | 0.87 | 0.82 | 0.78 | 0.90 | 210 |
+```
+
+## Steps
+
+1. Create `fixtures/search_queries.yaml` with ≥ 20 queries across categories.
+2. Ingest corresponding test documents into both stores.
+3. Implement `BenchmarkRunner` that:
+   a. Loads fixture file.
+   b. Runs each query against each strategy.
+   c. Computes NDCG, MRR, Precision, Recall per query.
+   d. Averages across queries.
+   e. Writes results to markdown.
+4. Implement as CLI command: `cargo run -- bench-search --queries <path> --output <path>`.
+5. Run benchmarks. Record results.
+
+## Acceptance
+
+1. Benchmark runs to completion on 20+ queries × 3 strategies = 60+ query executions.
+2. Output markdown table has all 5 columns populated.
+3. Hybrid NDCG@10 ≥ max(semantic NDCG, lexical NDCG) — hybrid must not be worse than best single engine.
+4. Per-query results show which categories benefit most from hybrid (expected: troubleshooting, procedural).
+5. If hybrid is worse on any category, document why and whether it matters.
+
+## Verify
+
+```bash
+cargo run -- bench-search \
+  --queries fixtures/search_queries.yaml \
+  --output docs/BENCHMARK_RESULTS.md
+
+cat docs/BENCHMARK_RESULTS.md
+```
+
+**False pass:** Benchmark uses the same documents for queries and ground truth (trivial exact match). Ensure queries use **natural language** and ground truth docs use **technical content** — the match should be semantic, not string equality.
+
+## Artifacts
+
+- `fixtures/search_queries.yaml`
+- `crates/mem-cli/src/bench_search.rs` (new)
+- `docs/BENCHMARK_RESULTS.md` (output)