Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新
無料GitHub で公開
company-brain-company-qa
Build one company memory from a SQL database, a support ticket export and a folder of documents, linked into one graph by cognee, then answer questions that need several of those sources together. Use when someone wants to connect their company's database, tickets and docs, or asks who owns, decided or is handling something across them. Runs locally; reads the sources, writes only to cognee's memory.
含まれるファイル（1）
- SKILL.md2.9 KB
SKILL.md（原文）
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Company brain: company Q&A
Runs the company brain cookbook in examples/cookbooks/company_brain/company_qa/. It
remembers a SQL database, a ticket export and a docs folder in the cognee dataset
company_qa, extracts them with one graph model so the same person, team or project
becomes one node, then answers a question across them. Setup, the scripts and what each
one does are in the cookbook's
README.md.
Run every command from the repo root.
Steps
- Work out the sources. Ask the user for the ones they want: a database URL (and tables),
one or more ticket export files (JSON or CSV), a docs folder. If they have none, or
want to see it work first, pass no sources: the run then uses the sample company (the
[setup]line says so). Tell the user the answer comes from sample data. - Run
uv run python examples/cookbooks/company_brain/company_qa/company_qa.py --checkwith the sources (--database URL [--tables a,b] --tickets FILE [FILE ...] --docs FOLDER), or none for the sample.- Exit 0: go to step 3.
- Exit 2: each
[setup] MISSING:line names one fix. Don't create credentials yourself. Tell the user which line to fix, using the README's "What it needs" table, and stop.
- Run the same command without
--check, adding the user's question as--ask "...". On the sample with no--ask, it asks a question that needs all three sample sources. - The answer is everything after
[ask] A:. Give it to the user. The[ingest]lines say which sources went in. - Later questions don't need the sources again:
uv run python examples/cookbooks/company_brain/company_qa/scripts/ask.py "...". - If a script fails,
company_qa.pyexits 1 with a line naming what went wrong. Run that script fromexamples/cookbooks/company_brain/company_qa/scripts/on its own to look closer.
Rules
- The sources are the user's company data. Don't quote them beyond what answers the
question, and don't print
.envor a database URL's password. - Every run uses LLM credits, so ingest only when the user asked for it.
- A sample run forgets the cookbook's dataset
company_qabefore it ingests (the[clear]line). On the user's own data, pass--clearonly when they asked to start over: it forgets everything the dataset holds. - Don't pass
--uiunless the user asked to browse the graph: it keeps running until Ctrl+C.
Clean up
uv run cognee-cli forget --dataset company_qa
レビュー
まだレビューはありません。使ってみた感想をお寄せください。
同じリポジトリのスキル
概要と使いどころ
Use when the user needs something that ships outside cognee core — community database adapters (Qdrant, Milvus, Weaviate, Redis, Pinecone, FalkorDB, Memgraph, DuckDB, NetworkX, …), data-source connectors (Slack, Gmail, Notion, Confluence, Google Drive), custom tasks/pipelines/retrievers (Exa, ScrapeGraph, codify), Keywords AI observability — or wants to contribute a package to the cognee-community repo.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新
Use when defining the shape of cognee's knowledge graph with graph_model= — writing DataPoint node classes, choosing identity and index fields so nodes merge and are searchable, declaring typed Edge fields and FromIdentity references, building a model from a JSON schema, or debugging duplicated nodes, missing edges, or InvalidReferenceTypeError.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新
Use when building your own cognee processing — writing custom tasks, chaining them into a pipeline with run_custom_pipeline or the lightweight run_pipeline (from cognee.pipelines import run_pipeline), storing custom DataPoints with add_data_points, running custom extraction/enrichment over the existing graph with memify, checking pipeline run status, or debugging how data flows between tasks (batch_size, data_per_batch, ctx, Drop, enriches).
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新
Use when the user wants to run cognee with Docker or docker compose — trying it out from the prebuilt image, starting the API server in a container, or bringing up the full stack (UI, MCP, Postgres, Neo4j) with compose profiles.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新
Use when removing data from cognee memory with forget() in the SDK, HTTP API, or CLI — finding which dataset and document hold the content to delete (listing datasets and data items, reading raw content), choosing between deleting one document, a whole dataset, or only the graph/vector memory, and doing it safely.
日本語の概要は準備中です。原文の説明を表示しています。
topoteretes/cognee3.2万2026年10月13日 更新