| name | cognee-quickstart |
| description | Set up Cognee for MISHKAN — choose the install path, create a clean Python env, configure LLM/embedding providers and (optionally) PostgreSQL/Neo4j backends, and wire it to the harness. Use before first bringing up the knowledge graph, or when Cognee setup hits friction (Python version, venv, API keys, optional extras). Mirrors Cognee's official LLM quickstart skill. |
cognee-quickstart
Get Cognee running for MISHKAN with minimal friction. Adapted from Cognee's
official quickstart skill (https://docs.cognee.ai/getting-started/llm-quickstart-skill)
and local-setup docs (https://docs.cognee.ai/cognee-mcp/mcp-local-setup).
Cognee core is a Python library (await cognee.remember(...) /
await cognee.recall(...)). MISHKAN consumes it through the cognee-mcp
server, declared in .mcp.json. This skill gets both right.
1. Choose the integration mode
| Mode | When | Wiring |
|---|
| HTTP container (default) | you want a long-running graph service on :7777 | ~/.claude/mishkan/cognee/ compose; .mcp.json HTTP entry → http://localhost:7777/mcp |
| stdio (zero infra) | simplest, no container | .mcp.json _stdio_alternative: uv --directory <cognee-mcp> run cognee-mcp |
Per-project work stores (ADR D-012): under MISHKAN each project's cognee
alias points at its OWN container (embedded Ladybug, own port), provisioned by
ensure-work-store.sh at /mishkan-init — not a single shared :7777. The
shared :7777 box is now cognee-memory (session memory); curated is :7730.
This skill covers the underlying cognee-mcp setup those stores build on.
2. Prerequisites
- Python (confirm the version Cognee requires in its docs).
uv (brew install uv or the platform equivalent).LLM_API_KEY (OpenAI key by default) — SOPS-managed, never plaintext-committed.
3. Install the library / MCP
uv pip install cognee
git clone https://github.com/topoteretes/cognee.git
cd cognee && git checkout <PINNED_TAG_OR_COMMIT>
cd cognee-mcp && uv sync --dev --all-extras --reinstall
4. Configure providers
- Set
LLM_API_KEY in ~/.claude/mishkan/cognee/.env (SOPS).
- Default backends are local (no extra services). For PostgreSQL/pgvector or
Neo4j, set the relevant cognee env vars per the docs and add the backend to the
compose file.
5. Bring it up + verify
- Container HTTP mode:
docker compose -f docker-compose.yml -f docker-compose.hardening.yml up -d --build, then nc -z localhost 7777. (Under MISHKAN the human path that wraps this — config preflight + curated seed + selfhosted overlay — is mishkan knowledge-stack up; the raw compose here is the under-the-hood reference.)
- stdio mode: nothing to start — Claude Code spawns it from
.mcp.json.
6. Seed + confirm in the harness
~/.claude/mishkan/scripts/seed-curated-library.sh
Then a quick recall through the MCP confirms the graph answers. From here the
graph grows through use (knowledge promotion at sprint close).
Constraints
Pin the cognee ref (no floating). LLM_API_KEY and all secrets via SOPS/age.
Stateful operations (the actual docker up, key entry) are run by Y4NN, not the
agent — the skill prepares the commands. English only.
