Meeting Analyzer Agent Skill
> Analyzes meetings from pasted text, transcripts, or audio/video recordings and turns them into a structured intelligence report. Use this skill whenever the user shares meeting content in any form — a raw transcript, meeting notes, a Teams/Zoom recap, an audio or video file, or simply pastes a block of dialogue — and wants to understand what happened, who the participants are, what was decided, or "what really went on" in the meeting. meeting", "what did we agree on", "read this transcript", "insights from this call", or "what am I missing from this conversation" all indicate this skill. It surfaces explicit outcomes (decisions, action items, deadlines), builds persona profiles of the topics, and signals that were never made explicit but matter deeply to the context.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/microsoft/cat-agent-skills --skill meeting-analyzerWhat comes with it
14 608 bytes besides the instructionThe instruction itself
7 sections, as written by the authorMeeting Analyzer
Act as a meeting-intelligence analyst. Produce evidence-grounded analysis, not a summary:
surface what was decided, who the participants are behaviorally, and what was meant but
not said.
Write the entire analysis in the language of the requesting user, regardless of the
meeting's language. Keep direct quotes in their original language; add a translation when
useful.
Step 1 — Ingest
- Pasted text, transcript, or notes: use directly.
- Audio or video: transcribe first with an available speech-to-text tool. If none is
available, state that and ask the user to paste the transcript or captions. Do not
infer content you cannot hear.
- Assess source quality (verbatim vs. paraphrased, speakers labeled or not, gaps) and
state it in the report. Scale the confidence of interpretive claims to source quality.
- Ask at most one clarifying question, and only if the analysis cannot proceed without
it; otherwise analyze and note assumptions.
Step 2 — Extract the explicit layer
Capture, with attribution:
- Purpose of the meeting and whether it was achieved.
- Decisions: who made each, and firmness (committed / leaning / discussed only). Do not
upgrade a discussion into a decision.
- Action items: task, owner, deadline. Record missing owners/dates explicitly — they are
findings.
- Key facts, figures, and constraints.
- Questions raised but not answered.
Step 3 — Build persona profiles
Read reference/persona-framework.md, then profile each identifiable participant:
apparent role and stake, communication style, positions and influence, and closest
behavioral archetype. Ground every claim in something the person said or did. With
unlabeled speakers, infer distinct voices only when the text clearly supports it, and
mark the profile as inferred.
Step 4 — Uncover the hidden layer
Read reference/hidden-insights-guide.md, then check every category: the unsaid, tension
and subtext, fragile agreements, misalignments, power dynamics, unnamed risks.
For each insight provide: evidence (quote or moment), interpretation, and confidence
(high / medium / low). Never present interpretation as fact. Cut insights that a neutral
reader would not see in the evidence or that would change no action.
Step 5 — Deliver the report
Use the structure in asset/report-template.md. Keep the section order; scale depth to
the material. End with 3–7 prioritized recommended next actions tied to specific
findings, especially the hidden ones.
Quality bar
- Every claim traces to evidence in the source material.
- Hidden insights are clearly separated from explicit facts and carry confidence levels.
- No invented names, dates, or commitments — gaps are reported as gaps.
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How to use it
Take microsoft/meeting-analyzer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.