Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.
Borrowing it
Nothing to install: this file belongs to topoteretes/cognee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/topoteretes/cognee/main/.claude/skills/cognee-community/SKILL.mdgit clone --depth 1 https://github.com/topoteretes/cogneeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/topoteretes/cognee/cognee-community)<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-community"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-community/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-community"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-community.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00106 | $0.01162 |
| Opus 5 | $0.00053 | $0.00581 |
| Sonnet 5 | $0.00021 | $0.00232 |
| Haiku 4.5 | $0.00011 | $0.00116 |
Grade A, and why
cognee-community scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use and contribute cognee-community packages
Community-maintained plugins live in a separate monorepo:
https://github.com/topoteretes/cognee-community. Everything installable is
under packages/; experimental/ holds demos (n8n nodes, dlt demos,
bauplan, tower) that are not published packages. Each package publishes to
PyPI as cognee-community-<family>-<kind>-<name> and imports as the same
name with underscores.
Package families
| Family | Packages |
|---|---|
| Vector adapters | azureaisearch, milvus, moss, opengauss, opensearch, pinecone, qdrant, redis, singlestore, turbopuffer, valkey, weaviate |
| Graph adapters | arcadedb, memgraph, networkx, pggraph, spanner, turbopuffer, turingdb |
| Hybrid (graph+vector in one DB) | arcadedb, duckdb, falkordb, helixdb |
| Connectors (data sources) | confluence, gmail, google-drive, notion, slack |
| Tasks / pipelines / retrievers | codify_tasks, codify_pipeline, code_retriever, exa_tasks, scrapegraph_tasks |
| Observability | keywordsai (MONITORING_TOOL=keywordsai + KEYWORDSAI_API_KEY) |
Using a database adapter
Install, then import the package's register module before cognee touches
any engine — registration is what makes the provider name valid:
uv pip install cognee-community-vector-adapter-qdrant
import cognee
from cognee import config
from cognee_community_vector_adapter_qdrant import register # noqa: F401
config.set_vector_db_config({
"vector_db_provider": "qdrant",
"vector_db_url": "http://localhost:6333",
"vector_db_key": "...",
"vector_dataset_database_handler": "qdrant", # only if the adapter ships one
})
The register.py calls use_vector_adapter(name, AdapterClass) /
use_graph_adapter(...). Setting VECTOR_DB_PROVIDER/GRAPH_DATABASE_PROVIDER
to a community name without the register import raises "Unsupported
vector database provider". Hybrid adapters (e.g. falkordb) register as both
graph and vector — set both configs to the same provider name.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 101 lines · 106 tokens per session scan A e209920e483a
cognee-community is a skill published in the GitHub repository topoteretes/cognee (30,610 stars, last pushed today), licensed Apache-2.0. It adds 106 tokens to every session and 1,162 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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