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 — 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.

Connect a project

Create .mcp.json in your project root:

{
  "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 — 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