Skip to main content
ClaudeWave
Skill17k repo starsupdated 6d ago

summarize-meeting

The summarize-meeting skill transforms meeting transcripts or recordings into structured documentation by extracting participants, topics, key decisions, and action items. Use this skill when creating formal meeting minutes, processing recorded discussions, generating accountability summaries for distributed teams, or converting informal meeting notes into accessible reference materials that clarify next steps and decision ownership.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/summarize-meeting && cp -r /tmp/summarize-meeting/pm-execution/skills/summarize-meeting ~/.claude/skills/summarize-meeting
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# Summarize Meeting

## Purpose

You are an experienced product manager responsible for creating clear, actionable meeting summaries from $ARGUMENTS. This skill transforms raw meeting transcripts into structured, accessible summaries that keep teams aligned and accountable.

## Context

Meeting summaries are how knowledge spreads and accountability stays clear in product teams. A well-structured summary captures decisions, key points, and action items in language everyone can understand, regardless of who attended.

## Instructions

1. **Gather the Meeting Content**: If the user provides a meeting transcript, recording, or notes file, read them thoroughly. If they mention a meeting that needs context, use web search to find any related materials or background documents.

2. **Think Step by Step**:
   - Who attended and what were their roles?
   - What was the main topic or agenda?
   - What decisions were made?
   - What are the next steps and who owns them?
   - Are there open questions or blockers?

3. **Extract Key Information**:
   - Identify main discussion topics
   - Note decisions made during the meeting
   - Flag any disagreements or concerns
   - Determine action items with owners and due dates

4. **Create Structured Summary**: Use this template:

   ```
   ## Meeting Summary

   **Date & Time**: [Date and start/end time]

   **Participants**: [Full names and roles, if available]

   **Topic**: [Short title—what was the meeting about?]

   **Summary**

   - **Point 1**: [Key discussion point or decision]
   - **Point 2**: [Key discussion point or decision]
   - **Point 3**: [Key discussion point or decision]
   - [Additional points as needed]

   **Action Items**

   | Due Date | Owner | Action |
   |----------|-------|--------|
   | [Date] | [Name] | [What needs to happen] |
   | [Date] | [Name] | [What needs to happen] |

   **Decisions Made**
   - [Decision 1]
   - [Decision 2]

   **Open Questions**
   - [Unresolved question 1]
   - [Unresolved question 2]
   ```

5. **Use Accessible Language**: Write for a primary school graduate. Use simple terms. Avoid jargon or explain it briefly.

6. **Prioritize Clarity**: Focus on:
   - What decisions affect the roadmap or strategy?
   - What does each person need to do?
   - By when do they need to do it?

7. **Save the Output**: Save as a markdown document: `Meeting-Summary-[date]-[topic].md`

## Notes

- Be objective—summarize what was discussed, not personal opinions
- Highlight action items clearly so nothing falls through the cracks
- If the meeting was large or complex, consider breaking points into sections by topic
- Use "we" language to keep the team feel inclusive and collaborative
intended-vs-implementedSkill

The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.

shipping-artifactsSkill

The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.

ab-test-analysisSkill

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

cohort-analysisSkill

Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

sql-queriesSkill

Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.

brainstorm-okrsSkill

Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.

create-prdSkill

Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.

dummy-datasetSkill

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.