cognee
Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory, self-improving agents, agents learning from feednack, knowledge graphs, graph-based RAG, long-term memory for agents, short-term memory for agents, personalization, personas, temporal search, temporal knowledge graphs, ontology-based extraction, ontology grounding, feedback, Cypher search, natural-language graph search, chunk search, RAG search, cross-session memory, session feedback, feedback loops, session based memory, redis based memory, knowledge promotion. Also use when the user describes the workflo
Profile →npx skills add topoteretes/cognee --skill cognee --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Cognee Use this skill for **Cognee-specific Python API help** and for mapping user goals to the right Cognee workflow. ## When to apply this skill Apply this skill whenever the user wants to do any of the following with Cognee: - ingest text, files, URLs, repos, or datasets - build or rebuild a knowledge graph - search documents, chunks, summaries, triplets, or graph context - choose a `SearchType` - enrich an existing graph with `memify` - define custom graph extraction models or `DataPoint` types - run custom task pipelines - configure LLM, graph DB, vector DB, or storage settings - tag and scope memory with `node_set` / NodeSets - build persistent memory for agents across sessions - create feedback loops or self-improving agent workflows - work with temporal extraction, ontologies, Cypher, or natural-language graph queries - manage datasets, sessions, feedback, pruning, updates, or visualization If the user’s intent is “store information in memory and query it later,” prefer Cognee’s core flow: **add -> cognify -> search** ## Core workflow ```python import cognee from cognee import SearchType await cognee.add( "Your text, file path, URL, or list of inputs", dataset_name="main"
- When to apply this skill
- Core workflow
- Default guidance
- Common tasks
- Add data
- Build the graph
- Search the graph
- Scope search with NodeSets
- Enrich an existing graph
- Create domain-specific structures
- Run custom pipelines
- DataPoints
- NodeSets
- Why NodeSets matter
What does the cognee skill do?
Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory, self-improving agents, agents learning from feednack, knowledge graphs, graph-based RAG, long-term memory for agents, short-term memory for agents, personalization, personas, temporal search, temporal knowledge graphs, ontology-based extraction, ontology grounding, feedback, Cypher search, natural-language graph search, chunk search, RAG search, cross-session memory, session feedback, feedback loops, session based memory, redis based memory, knowledge promotion. Also use when the user describes the workflo
How do I install it?
Run `npx skills add topoteretes/cognee --skill cognee --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From topoteretes/cognee, a repository with 29,768 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.