docs: add hypothesis statement and harness boundary clarification

README.md

@@ -5,14 +5,31 @@ Instead of letting Claude Code do whatever it wants, hyperguild enforces structu
 workflows (TDD red/green/refactor), logs every session, and accumulates learnings
 into a searchable brain.
 
+## Hypothesis
+
+> We believe routing skill tasks through local models, backed by brain context,
+> produces measurably better outcomes than raw Claude Code alone —
+> measurable by per-skill pass rate over rolling 30-day windows
+> (available at `GET /pass-rate?skill=<name>&window=30d` on the brain pod).
+
+This is the falsifiable claim the routing pod and pass-rate infrastructure exist to test.
+If per-skill pass rates don't improve over baseline (all-cloud) after 30 days of real
+usage, the fast-model routing path should be reconsidered.
+
+## Harness
+
+**hyperguild = Claude Code + MCP.** This is a supervisor for Claude Code sessions specifically.
+For multi-agent orchestration (OpenCode + LiteLLM, executor/reviewer pipelines), see
+[agentsquad](http://gitea.d-ma.be/mathias/agentsquad) — a separate harness for a different
+orchestration model. Skills (mathias/skills) are shared between both.
+
 ## How it works
 
 ```
 Your Claude Code session (in any project)
     │
     │  MCP over HTTP (Tailscale)
-    ├──▶ supervisor  :3200 (NodePort 30320 on koala) — skill workers: tdd, debug, spec, …
-    ├──▶ routing     :3210 (NodePort 30310 on koala) — Mode 2 only: review, debug, retrospective, trainer
+    ├──▶ routing     :3210 (NodePort 30310 on koala) — review, debug, retrospective, trainer
     └──▶ brain       :3300 (NodePort 30330 on koala) — brain_query, brain_write, brain_ingest, session_log
                        │
                        └─ also serves the legacy REST endpoints (/query, /write, /ingest, …)
@@ -20,34 +37,28 @@ Your Claude Code session (in any project)
     ▼
 brain/
 ├── sessions/       — JSONL log, one file per session_id
-├── wiki/           — searchable knowledge (full-text)
-│   ├── concepts/
-│   ├── entities/
-│   └── sources/
-├── raw/            — retrospective output, staged for review
-└── training-data/  — SFT/DPO/RL data (Phase 2)
+├── wiki/           — searchable knowledge (wing/hall layout)
+│   ├── homelab/
+│   ├── claude-sessions/
+│   └── ...
+└── knowledge/      — legacy flat notes (migration pending: hyperguild#22)
 ```
 
 ## Phase 1 tools (available now)
 
 | Tool | What it does |
 |------|-------------|
-| `tdd_red` | Writes a failing test for a spec, verifies it fails |
-| `tdd_green` | Writes the minimal implementation to make tests pass |
-| `tdd_refactor` | Cleans up implementation while keeping tests green |
 | `session_log` | Appends a structured entry to the session JSONL log |
-| `retrospective` | Reads the session log, identifies novel learnings, writes to brain/raw/ |
+| `retrospective` | Reads the session log, identifies novel learnings, writes to brain |
+| `review` | Structured code review via local model, brain-context injected |
+| `debug` | Hypothesis-driven debugging via local model |
 | `brain_query` | Full-text search over brain/wiki/ |
-| `brain_write` | Writes a note to brain/raw/ (with optional YAML frontmatter) |
+| `brain_write` | Writes a note to brain (with wing/hall routing) |
+| `brain_answer` | BM25 + LLM synthesis — Q&A over brain corpus |
 | `tier` | Returns the current connectivity tier (1=cloud, 2=LAN, 3=offline) |
 
-## Start the servers
-
-```bash
-# Requires goreman: go install github.com/mattn/goreman@latest
-task start    # starts ingestion (:3300) + supervisor (:3200) via goreman
-task stop     # kills both by port
-```
+> **Note:** `tdd_red/green/refactor` and `spec` were retired in Plan 7 (2026-05-12).
+> They are now SKILL.md files in [mathias/skills](http://gitea.d-ma.be/mathias/skills).
 
 ## Connect a project
 
@@ -56,9 +67,9 @@ Create `.mcp.json` in your project root:
 ```json
 {
   "mcpServers": {
-    "supervisor": {
+    "routing": {
       "type": "http",
-      "url": "http://koala:30320/mcp"
+      "url": "http://koala:30310/mcp"
     },
     "brain": {
       "type": "http",
@@ -68,33 +79,29 @@ Create `.mcp.json` in your project root:
 }
 ```
 
-Two MCP servers are exposed today, both reachable over Tailscale:
+Two MCP servers are exposed, both reachable over Tailscale:
 
-- **`supervisor`** at `koala:30320` — skill workers (`tdd_red/green/refactor`,
-  `review`, `debug`, `spec`, `retrospective`, `trainer`, `tier`).
+- **`routing`** at `koala:30310` — skill workers (`review`, `debug`, `retrospective`, `trainer`).
+  Routes each call to fast local model or thinking model based on per-skill pass rate.
 - **`brain`** at `koala:30330` — knowledge access (`brain_query`, `brain_write`,
-  `brain_ingest`, `brain_ingest_raw`) and `session_log`. Hosted by the ingestion
-  service directly, no separate pod.
+  `brain_ingest`, `brain_ingest_raw`, `brain_answer`, `brain_classify`) and `session_log`.
 
 No local binary or stdio shim is required — Claude Code talks to both via HTTP.
 
 Open Claude Code in your project — run `/mcp` to confirm both servers are listed.
 
-## A typical TDD session
+## A typical session
 
 ```
-1. Call tdd_red    → spec in, failing test file out
-2. Call tdd_green  → test path in, implementation out
-3. Call tdd_refactor → impl + test in, cleaned code out
-4. Call session_log  → log each phase result
-5. Call retrospective → extracts learnings → brain/raw/
-6. Review brain/raw/, move worthy notes to brain/wiki/concepts/
-7. Future sessions: call brain_query to retrieve relevant context
+1. Call review      → brain context injected + local model review → findings
+2. Call session_log → log each phase result
+3. Call retrospective → extracts learnings → brain
+4. Future sessions: call brain_query / brain_answer to retrieve relevant context
 ```
 
 ## Tier detection
 
-The supervisor probes connectivity at call time:
+The routing pod probes connectivity at call time:
 
 | Tier | Label | Condition |
 |------|-------|-----------| 
@@ -102,31 +109,47 @@ The supervisor probes connectivity at call time:
 | 2 | lan-only | Can reach LiteLLM but not Anthropic |
 | 3 | airplane | No external connectivity |
 
+## Model routing
+
+The routing pod selects models per skill call based on historical pass rate:
+
+| Pass rate | Decision |
+|-----------|----------|
+| ≥ 0.90 (FLOOR) | Fast model (`HYPERGUILD_FAST_MODEL`) |
+| ≤ 0.70 (CEIL) | Thinking model (`HYPERGUILD_THINKING_MODEL`) |
+| between CEIL and FLOOR | Sample band — probabilistic routing |
+| nil (no history yet) | Defaults to thinking model |
+
+> **Bootstrap note:** With no session history, all calls route to the thinking model.
+> The fast-model path activates only after real pass-rate data accumulates at `/pass-rate`.
+> Seed with real usage — don't try to pre-populate.
+
 ## Key env vars
 
 | Variable | Default | Purpose |
 |----------|---------|---------| 
 | `INGEST_BRAIN_DIR` | `../brain` | Brain directory for ingestion server |
 | `INGEST_PORT` | `3300` | Ingestion server port |
-| `SUPERVISOR_CONFIG_DIR` | `./config/supervisor` | Skill discipline files |
-| `SUPERVISOR_SESSIONS_DIR` | `./brain/sessions` | JSONL session logs |
-| `INGEST_BASE_URL` | `http://localhost:3300` | Supervisor → ingestion |
+| `INGEST_BASE_URL` | `http://localhost:3300` | Routing pod → brain |
 | `LITELLM_BASE_URL` | — | LiteLLM proxy for Tier 2 model routing |
-| `SUPERVISOR_MCP_TOKEN` | — | Optional bearer token for the supervisor MCP HTTP endpoint; when empty, no auth is enforced |
 | `ROUTING_PORT` | `3210` | Routing pod's listen port |
-| `ROUTING_MCP_TOKEN` | — | Optional bearer token for the routing MCP HTTP endpoint |
+| `ROUTING_MCP_TOKEN` | — | Optional bearer token; when empty, no auth enforced |
 | `BRAIN_URL` | `http://ingestion.supervisor:3300` | Routing pod → brain (in-cluster) |
 | `HYPERGUILD_FAST_MODEL` | `koala/qwen35-9b-fast` | Fast model for high-pass-rate skill calls |
 | `HYPERGUILD_THINKING_MODEL` | `iguana/gemma4-26b` | Thinking model for low-pass-rate skill calls |
-| `HYPERGUILD_ROUTE_LOCAL_FLOOR` | `0.90` | At/above pass rate, route to fast model |
-| `HYPERGUILD_ROUTE_LOCAL_CEIL` | `0.70` | Below pass rate, route to thinking model. Between CEIL and FLOOR is the sample band. |
+| `HYPERGUILD_ROUTE_LOCAL_FLOOR` | `0.90` | Fast model threshold |
+| `HYPERGUILD_ROUTE_LOCAL_CEIL` | `0.70` | Thinking model threshold |
 | `HYPERGUILD_PASS_RATE_TTL_SECONDS` | `60` | Per-skill pass-rate cache TTL |
 
-> **Operator note:** LiteLLM at `LITELLM_BASE_URL` must register both `HYPERGUILD_FAST_MODEL` and `HYPERGUILD_THINKING_MODEL` for routing to do useful work. If a model is missing, LiteLLM returns 4xx, the routing pod's fast route fails, the fail-open retry on the thinking model likely also fails (since both are missing), and the only signal is `final_status: "fail"` on `_routing` entries in the brain.
+> **Operator note:** LiteLLM at `LITELLM_BASE_URL` must register both `HYPERGUILD_FAST_MODEL`
+> and `HYPERGUILD_THINKING_MODEL`. If a model is missing, the fail-open retry also fails and
+> the only signal is `final_status: "fail"` on `_routing` entries in the brain.
 
-## Phase 2 (planned)
+## Open issues
 
-- `review` skill — structured code review with iron law enforcement
-- `debug` skill — hypothesis-driven debugging sessions
-- `spec` skill — generates specs from conversations
-- `trainer` — extracts SFT/DPO pairs from session logs for fine-tuning
+See [issues](http://gitea.d-ma.be/mathias/hyperguild/issues) — key open items:
+
+- **#25** — skills platform overhaul (audit first, then lazy loading + brain feedback loop)
+- **#24** — reduce context burn from skill listing
+- **#22** — migrate legacy brain notes to wing/hall layout (one-shot script, low risk)
+- **#31** — connect routing-mcp to claude.ai as custom connector