cognee-quickstartlisted

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.
Y4NN777/mishkan-cc-harness · ★ 3 · AI & Automation · score 76

Install: claude install-skill Y4NN777/mishkan-cc-harness

# 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` | ## 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 ```bash # 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 servi