Skip to content
cccskills
FreePublished on GitHub

cognee

Routes Cognee AI memory platform tasks across SDK memory APIs, graph/RAG retrieval, configuration, custom pipelines, session memory, CLI, API, MCP, and deployment workflows.

The English summary is being prepared. Showing the original description.

Works withDocker
See how to install

Files included (38)

  • SKILL.md3.7 KB
  • references/package-overview.md2.9 KB
  • references/repo-provenance.md2.1 KB
  • references/repo-routing-metadata.json479 B
  • references/troubleshooting.md2.3 KB
  • scripts/check_install.py3.2 KB
  • sub-skills/advanced-graphs-pipelines/references/custom-graphs-pipelines.md3.2 KB
  • sub-skills/advanced-graphs-pipelines/references/migration-visualization.md2.4 KB
  • sub-skills/advanced-graphs-pipelines/references/troubleshooting.md2.1 KB
  • sub-skills/advanced-graphs-pipelines/scripts/inspect_custom_model.py2.4 KB
  • sub-skills/advanced-graphs-pipelines/SKILL.md2.3 KB
  • sub-skills/agent-session-memory/references/agent-session-memory.md3.1 KB
  • sub-skills/agent-session-memory/references/api-reference.md2.8 KB
  • sub-skills/agent-session-memory/references/troubleshooting.md1.9 KB
  • sub-skills/agent-session-memory/scripts/check_agent_memory_payloads.py1.7 KB
  • sub-skills/agent-session-memory/SKILL.md2.1 KB
  • sub-skills/api-cli-services/references/cli-reference.md5.2 KB
  • sub-skills/api-cli-services/references/deployment.md3.3 KB
  • sub-skills/api-cli-services/references/services-mcp.md7.6 KB
  • sub-skills/api-cli-services/references/troubleshooting.md4.2 KB
  • sub-skills/api-cli-services/scripts/check_cli_surface.py4.9 KB
  • sub-skills/api-cli-services/scripts/check_mcp_surface.py3.1 KB
  • sub-skills/api-cli-services/SKILL.md2.3 KB
  • sub-skills/configuration-backends/references/backend-matrix.md4.5 KB
  • sub-skills/configuration-backends/references/configuration.md13.5 KB
  • sub-skills/configuration-backends/references/troubleshooting.md3.2 KB
  • sub-skills/configuration-backends/scripts/check_cognee_environment.py3.8 KB
  • sub-skills/configuration-backends/SKILL.md3.2 KB
  • sub-skills/core-memory/references/api-reference.md3.1 KB
  • sub-skills/core-memory/references/troubleshooting.md1.9 KB
  • sub-skills/core-memory/references/workflows.md2.9 KB
  • sub-skills/core-memory/scripts/cognee_memory_smoke.py1.2 KB
  • sub-skills/core-memory/SKILL.md2.4 KB
  • sub-skills/search-retrieval/references/api-reference.md3.2 KB
  • sub-skills/search-retrieval/references/search-modes.md2.6 KB
  • sub-skills/search-retrieval/references/troubleshooting.md1.9 KB
  • sub-skills/search-retrieval/scripts/choose_search_mode.py2.3 KB
  • sub-skills/search-retrieval/SKILL.md2.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Cognee Repo Skill

Use this skill when the user asks about Cognee, AI memory, persistent agent memory, graph RAG, Cognee SDK/CLI/API/MCP usage, Cognee configuration, custom graph extraction, session memory, or self-hosted Cognee services.

Cognee is a Python package and service stack for ingesting data, building a graph/vector-backed memory layer, and recalling knowledge for agents.

First checks

For a safe installed-package check:

python scripts/check_install.py --help
python scripts/check_install.py --json

Minimal package install:

python -m pip install "cognee"
python - <<'PY'
import cognee
print(cognee.__version__)
print(cognee.SearchType.GRAPH_COMPLETION)
PY

Do not assume provider credentials or optional databases are configured. Most real ingestion/search workflows need an LLM and embedding provider; backend-specific workflows need matching extras and services.

Route map

User intentRead
Store, build, recall, improve, or delete memory with SDK callssub-skills/core-memory/SKILL.md
Choose SearchType, tune recall/search, handle empty results or invalid query knobssub-skills/search-retrieval/SKILL.md
Configure LLMs, embeddings, database/storage backends, optional extras, env vars, and pathssub-skills/configuration-backends/SKILL.md
Define custom graph schemas, custom tasks/pipelines, ontology workflows, memify, migration, export, or visualizationsub-skills/advanced-graphs-pipelines/SKILL.md
Use session memory, feedback entries, agent memory decorators, agent identities, or multi-agent isolationsub-skills/agent-session-memory/SKILL.md
Operate Cognee through CLI, FastAPI, MCP, Docker Compose, UI, cloud/local service connection, or push/syncsub-skills/api-cli-services/SKILL.md

Shared references

Operating rules

  • Prefer remember/recall for memory-shaped user requests; prefer add/cognify/search for explicit pipeline control.
  • Before diagnosing workflow failures, separate three causes: package install, provider credentials, and database/storage backend state.
  • Never tell a future agent to open original Cognee docs, examples, notebooks, tests, or scripts. Use the bundled references and scripts in this skill tree.
  • Keep real API keys, cloud tokens, local filesystem paths, and service URLs in the user’s runtime configuration, not in copied guidance.
  • Treat Docker, MCP, UI, and API servers as long-running services; run only --help, version, or check scripts unless the user asks to start them.
  • If a current checkout has changed from the provenance snapshot, refresh this skill before relying on implementation-specific details.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill