# hyperguild

An MCP server that acts as a disciplined AI supervisor for Claude Code sessions.
Instead of letting Claude Code do whatever it wants, hyperguild enforces structured
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)
    ├──▶ 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, …)
    │
    ▼
brain/
├── sessions/       — JSONL log, one file per session_id
├── 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 |
|------|-------------|
| `session_log` | Appends a structured entry to the session JSONL log |
| `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 (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) |

> **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

Create `.mcp.json` in your project root:

```json
{
  "mcpServers": {
    "routing": {
      "type": "http",
      "url": "http://koala:30310/mcp"
    },
    "brain": {
      "type": "http",
      "url": "http://koala:30330/mcp"
    }
  }
}
```

Two MCP servers are exposed, both reachable over Tailscale:

- **`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`, `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 session

```
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 routing pod probes connectivity at call time:

| Tier | Label | Condition |
|------|-------|-----------| 
| 1 | full-online | Can reach api.anthropic.com |
| 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 |
| `INGEST_BASE_URL` | `http://localhost:3300` | Routing pod → brain |
| `LITELLM_BASE_URL` | — | LiteLLM proxy for Tier 2 model routing |
| `ROUTING_PORT` | `3210` | Routing pod's listen port |
| `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` | 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`. 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.

## Open issues

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