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cognee-quickstart

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Mis à jour11 juin 2026 à 20:19

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.

Installation

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Y4NN777

Y4NN777/mishkan-cc-harness

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SKILL.md

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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

# library smoke test (optional)
uv pip install cognee

# MCP server (cloned at a pinned ref)
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     # 96 curated nodes

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.

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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

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

# library smoke test (optional)
uv pip install cognee

# MCP server (cloned at a pinned ref)
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     # 96 curated nodes

Then a quick recall through the MCP confirms the graph answers. From here the graph grows through use (knowledge promotion at sprint close).