Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Routes Cognee AI memory platform tasks across SDK memory APIs, graph/RAG retrieval, configuration, custom pipelines, session memory, CLI, API, MCP, and deployment workflows.
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Review the instructions the agent will receive before you install.
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.
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.
| User intent | Read |
|---|---|
| Store, build, recall, improve, or delete memory with SDK calls | sub-skills/core-memory/SKILL.md |
Choose SearchType, tune recall/search, handle empty results or invalid query knobs | sub-skills/search-retrieval/SKILL.md |
| Configure LLMs, embeddings, database/storage backends, optional extras, env vars, and paths | sub-skills/configuration-backends/SKILL.md |
| Define custom graph schemas, custom tasks/pipelines, ontology workflows, memify, migration, export, or visualization | sub-skills/advanced-graphs-pipelines/SKILL.md |
| Use session memory, feedback entries, agent memory decorators, agent identities, or multi-agent isolation | sub-skills/agent-session-memory/SKILL.md |
| Operate Cognee through CLI, FastAPI, MCP, Docker Compose, UI, cloud/local service connection, or push/sync | sub-skills/api-cli-services/SKILL.md |
remember/recall for memory-shaped user requests; prefer add/cognify/search for explicit pipeline control.--help, version, or check scripts unless the user asks to start them.No reviews yet. Tell others what you thought.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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