Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

cognee-quickstart

Estrellas3

Forks1

Actualizado11 de junio de 2026 a las 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.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Ejecutar en Manus

Fuente

Y4NN777

Y4NN777/mishkan-cc-harness

Abrir repositorio de GitHub Ver repositorios del creador

Descarga

Ejecutar en Manus

Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

SKILL.md

readonly

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.

Más de este repositorio

mismo repositorio

baruch-research-reporting-craft

Y4NN777/mishkan-cc-harness

How Baruch records the terminal output of a research run — the contract-bound research-log shape, when to write a Cognee node and when not to, the curated-library-match short circuit, and the discipline of being a faithful carrier. Invoke when emitting a research-log entry, when the Cognee write decision is in scope, or when an upstream stage returned malformed input.

2026-06-133

cognee-promote

Y4NN777/mishkan-cc-harness

Promote knowledge into Cognee by blast radius. Routes a learning to the right tier — agent-private (stays in MEMORY.md), team-level (team rules + topic files, Team Lead decides), or cross-harness (Cognee project graph, Nehemiah + Bezalel at sprint close). Use when an agent has a learning worth keeping.

2026-06-133

ezra-research-formulation-craft

Y4NN777/mishkan-cc-harness

How Ezra turns clarified intent into a research brief — the curated-library-first rule, the sub-question decomposition, source prioritisation, acceptance criteria for "good answer," and the short-circuit when the curated library already holds the answer. Invoke as the second stage of the research pipeline after Jakin clarifies.

2026-06-133

shemaiah-evaluation-craft

Y4NN777/mishkan-cc-harness

How Shemaiah judges the summarised research for signal vs noise — the cross-reference against the curated library, the verdict shape (resolved / partial / blocked), confidence calibration anchored to source quality and coverage, gap identification, and the rule that an evaluation never produces new content. Invoke as the fifth stage of the research pipeline.

2026-06-133

mishkan-init

Y4NN777/mishkan-cc-harness

Initialise a project under MISHKAN. Runs the SWE-BASICS-BEFORE-CODE sequence through the right specialists (PRD → SRS → CONTRACT → ARCHITECTURE → THREAT_MODEL → C4 → docs scaffold), seeds Cognee, writes the project CLAUDE.md, and begins Sprint S0. Use once per project, triggered by /mishkan-init.

2026-06-113

mishkan-ingest

Y4NN777/mishkan-cc-harness

Selectively ingest specific documents into the project's cognee work store. Use to deliberately add docs to memory instead of bulk-ingesting a whole tree — the default is "nothing enters memory unless tagged or invoked", which prevents PII bleed (e.g. real addresses in incident reports) and oversized-doc embedding failures. Walks ./docs/ filtered by a `mishkan: ingest` YAML frontmatter tag, or accepts explicit paths. Always runs cognify → memify after adding.

2026-06-113

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).