Summarize Meetings
Meeting transcripts pile up. The names, the action items, the project ideas, they're all in there but nobody's going back to read them. This skill turns a backlog of Granola transcripts into a connected Obsidian knowledge graph. Processes meetings in monthly batches, extracts the stuff that matters, and wires it all together with wiki-links. We used it to process ~600 meetings into something actually useful: My Now Immaculate Knowledge Graph of Life
!network-annotated_hu_dc5f11550bad1d72
Obsidian graph view after processing ~600 meetings. Nodes are people and concepts, edges are co-occurrence in the same meeting.
Installation
/plugin marketplace add 2389-research/claude-plugins
/plugin install summarize-meetings@2389-research
What it does
Point this at a month of meeting transcripts and it will:
- Read each transcript, skip the junk (empty stubs, scheduling fragments, recording setup chats)
- Dispatch 10-15 parallel agents to process the real meetings
- Write a summary file for each meeting with YAML frontmatter and a narrative recap
- Create or update People notes in
People/(checks for existing entries and aliases first) - Pull out concepts worth their own atomic note in
Concepts/ - Stub out any projects mentioned that don't already exist
- Report back with a table of what got processed and what got skipped
What gets extracted
From each meeting, agents pull out seven categories: people, action items, project ideas, blog ideas, knowledge graph connections, concepts, and general ideas. The summary file includes all of these plus a 2-4 paragraph narrative that reads like a real meeting recap — what happened, what mattered, what the vibe was.
Triage
Not everything in Meetings/transcripts/ is worth a summary. The skill skips files that are empty stubs, have fewer than 20 words of real content, are garbled recordings, or are medical appointments and kid-related content. Sparse but real notes (like bullet points from a fundraising call) still get processed.
How it works
Scan month → Triage → Dispatch agents in waves → Compile report → Post update
Agents run in parallel (waves of 10-15) and each one handles a single transcript end-to-end. Large files (50K+) get chunked automatically.
Documentation
The full workflow, templates, and extraction rules are in skills/SKILL.md.
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