meeting-note_skill

This skill helps convert multi-party meeting discussions into structured conclusions, decisions, risks, and actionable items linked to knowledge networks.
  • Python

2.5k

GitHub Stars

3

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill meeting-note

  • _meta.json277 B
  • README.md1.5 KB
  • SKILL.md7.7 KB

Overview

This skill converts exploratory and decision-focused meetings into concise, actionable, and networked meeting notes. It emphasizes why decisions were made, who holds views or power, hidden assumptions, risks/opportunities, and connects outcomes into a Zettelkasten-style knowledge network. Use it to turn discussion into traceable decisions and executable tasks.

How this skill works

The skill follows a strict multi-step workflow: plan (TODO), collect meeting metadata and importance level, perform layered readings to split topics and identify facts/arguments/assumptions, and then structure each topic into conclusion/consensus/dissent/decision-trajectory/risk/actions. It atomizes insights into reusable Zettelkasten notes and enforces quality checks (TBD markers, measurable actions, ≥2 internal links).

When to use it

  • Decision or exploration meetings where outcomes, responsibilities, or trade-offs must be captured
  • Cross-functional discussions that require traceable decision trajectories and follow-up
  • Situations needing explicit capture of hidden assumptions, power dynamics, or unresolved risks
  • Project- or strategy-level meetings that must feed into a knowledge index or execution system
  • When you want meeting outputs to become reusable knowledge (Zettelkasten) rather than a transcript

Best practices

  • Do a Step 0 TODO plan before producing notes to set scope and deliverables
  • Mark unknowns explicitly with 'TBD' rather than guessing
  • Split content by topic first, then analyze each topic to avoid superficial summaries
  • List non-consensus views with speaker and reasoning; include evidence for any inferred assumptions
  • Produce action items that are actionable, measurable, attributable, and timebound (use TBD if missing)
  • Create at least two [[Zettelkasten]] links per note to integrate into the knowledge network

Example use cases

  • Product roadmap meeting where trade-offs, decision trail, and owners must be recorded
  • Cross-team alignment session that leaves open questions and needs prioritized follow-ups
  • Strategic review requiring capture of hidden assumptions and proposed mitigation actions
  • Vendor negotiation debrief that must document concessions, decision logic, and next steps
  • Executive decision session where outputs feed into organizational index and accountability

FAQ

If the meeting is primarily informational or learning-focused (one speaker, others mainly listening), use the deep-learning approach to treat the talk as source material and perform multi-pass extraction and atomicization.

What if some details are unknown after the meeting?

Mark unknowns as 'TBD', list who must provide the info, and add a follow-up action with owner and deadline to close the gap.

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meeting-note skill by openclaw/skills | VeilStrat