Turn the latest Granola call into action items with owners, teams, due dates, and matching existing Linear issues. It uses cognee memory of calls, issues, and Gmail, then prints the results or posts them to configured Slack.
中文简介正在准备中。当前显示英语简介。
Turn your latest Granola call into next steps (owner, team, due date, and the Linear issue that already tracks each one) and post them to Slack, using cognee memory of your calls, Linear issues and Gmail inbox. Use when someone asks for the next steps or action items of their latest call. Runs locally; reads Gmail read-only; posts to Slack only when Slack is set up.
安装前可以查看交给 AI 代理的指令内容。
Runs the follow-up agent cookbook in examples/cookbooks/company_brain/follow_up_agent/.
It remembers your Granola calls, Linear issues and Gmail inbox in the cognee dataset
follow_up_agent, then works out the next steps of your latest call and posts them to
Slack, or prints them. Setup, the scripts and what each one does are in the cookbook's
README.md.
Run every command from the repo root.
With none of Granola, Linear and Gmail set up, the commands below run on sample calls,
issues and email (the [setup] line says so), follow up a fictional call and never post to
Slack; only LLM_API_KEY is needed. Tell the user the steps come from sample data. Add
--sample to use the sample even when their accounts are set up.
uv run python examples/cookbooks/company_brain/follow_up_agent/follow_up_agent.py --check.
[setup] SKIPPED: lines name optional sources that won't be
used; mention them to the user, but they don't block the run.[setup] MISSING: line names one fix. Don't create credentials yourself.
Tell the user exactly which line to fix, using the README's "What it needs" table,
and stop.uv run python examples/cookbooks/company_brain/follow_up_agent/follow_up_agent.py.
Add --days N if the user named a time range, and --no-linear / --no-email if they
don't want a source used.[follow_up] Posted to Slack: or [follow_up] Steps
line. Give them to the user, say which call they come from (the [follow_up] Call:
line), which sources went in (the [ingest_*] lines), and whether they were posted.follow_up_agent.py exits 1 with a line naming what went wrong. Run
that script from examples/cookbooks/company_brain/follow_up_agent/scripts/ on its own
to look closer.credentials.json, token.json or .env.follow_up_agent before it ingests (the [clear]
line). On the user's own data, pass --clear only when they asked to start over: it
forgets everything the dataset holds.--ui unless the user asked to browse the graph: it keeps running until
Ctrl+C.uv run cognee-cli forget --dataset follow_up_agent
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Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.
中文简介正在准备中。当前显示英语简介。
Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.
中文简介正在准备中。当前显示英语简介。
Define custom entities and relationships in cognee, a memory system for AI applications. This skill guides model design, identity matching, searchable fields, and troubleshooting when entities are duplicated or relationships are missing.
中文简介正在准备中。当前显示英语简介。
Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.
中文简介正在准备中。当前显示英语简介。
Run cognee in Docker, starting with a prebuilt image for local document ingestion and questions. It also covers Docker Compose profiles for adding the UI, MCP server, Postgres, and Neo4j, plus persistent data storage.
中文简介正在准备中。当前显示英语简介。
Find and remove stored content from cognee using its SDK, HTTP API, or CLI. Inspect documents before deletion, choose document or dataset removal, or keep raw files while clearing derived graph relationships and search vectors.
中文简介正在准备中。当前显示英语简介。