meeting-minutes-taker

Meeting Minutes Taker

Transform raw meeting transcripts into comprehensive, evidence-based meeting minutes through iterative review.

Quick Start

Pre-processing (Optional but Recommended):

• Document conversion: Use markdown-tools skill to convert .docx/.pdf to Markdown first (preserves tables/images)
• Transcript cleanup: Use transcript-fixer skill to fix ASR/STT errors if transcript quality is poor
• Context file: Prepare context.md with team directory for accurate speaker identification

Core Workflow:

1. Read the transcript provided by user
2. Load project-specific context file if provided by user (optional)
3. Intelligent file naming: Auto-generate filename from content (see below)
4. Speaker identification: If transcript has "Speaker 1/2/3", identify speakers before generation
5. Multi-turn generation: Use multiple passes or subagents with isolated context, merge using UNION
6. Self-review using references/completeness_review_checklist.md
7. Present draft to user for human line-by-line review
8. Cross-AI comparison (optional): Human may provide output from other AI tools (e.g., Gemini, ChatGPT) - merge to reduce bias
9. Iterate on feedback until human approves final version

Intelligent File Naming

Auto-generate output filename from transcript content:

Pattern: YYYY-MM-DD-<topic>-<type>.md

Component	Source	Examples
Date	Transcript metadata or first date mention	2026-01-25
Topic	Main discussion subject (2-4 words, kebab-case)	api-design, product-roadmap
Type	Meeting category	review, sync, planning, retro, kickoff

Examples:

• 2026-01-25-order-api-design-review.md
• 2026-01-20-q1-sprint-planning.md
• 2026-01-18-onboarding-flow-sync.md

Ask user to confirm the suggested filename before writing.

Core Workflow

Copy this checklist and track progress:

Meeting Minutes Progress:
- [ ] Step 0 (Optional): Pre-process transcript with transcript-fixer
- [ ] Step 1: Read and analyze transcript
- [ ] Step 1.5: Speaker identification (if transcript has "Speaker 1/2/3")
  - [ ] Analyze speaker features (word count, style, topic focus)
  - [ ] Match against context.md team directory (if provided)
  - [ ] Present speaker mapping to user for confirmation
- [ ] Step 1.6: Generate intelligent filename, confirm with user
- [ ] Step 1.7: Quality assessment (optional, affects processing depth)
- [ ] Step 2: Multi-turn generation (PARALLEL subagents with Task tool)
  - [ ] Create transcript-specific dir: <output_dir>/intermediate/<transcript-name>/
  - [ ] Launch 3 Task subagents IN PARALLEL (single message, 3 Task tool calls)
    - [ ] Subagent 1 → <output_dir>/intermediate/<transcript-name>/version1.md
    - [ ] Subagent 2 → <output_dir>/intermediate/<transcript-name>/version2.md
    - [ ] Subagent 3 → <output_dir>/intermediate/<transcript-name>/version3.md
  - [ ] Merge: UNION all versions, AGGRESSIVELY include ALL diagrams → draft_minutes.md
  - [ ] Final: Compare draft against transcript, add omissions
- [ ] Step 3: Self-review for completeness
- [ ] Step 4: Present draft to user for human review
- [ ] Step 5: Cross-AI comparison (if human provides external AI output)
- [ ] Step 6: Iterate on human feedback (expect multiple rounds)
- [ ] Step 7: Human approves final version

Note: <output_dir> = directory where final meeting minutes will be saved (e.g., project-docs/meeting-minutes/)
Note: <transcript-name> = name derived from transcript file (e.g., 2026-01-15-product-api-design)

Step 1: Read and Analyze Transcript

Analyze the transcript to identify:

• Meeting topic and attendees
• Key decisions with supporting quotes
• Action items with owners
• Deferred items / open questions

Step 1.5: Speaker Identification (When Needed)

Trigger: Transcript only has generic labels like "Speaker 1", "Speaker 2", "发言人1", etc.

Approach (inspired by Anker Skill):

Phase A: Feature Analysis (Pattern Recognition)

For each speaker, analyze:

Feature	What to Look For
Word count	Total words spoken (high = senior/lead, low = observer)
Segment count	Number of times they speak (frequent = active participant)
Avg segment length	Average words per turn (long = presenter, short = responder)
Filler ratio	% of filler words (对/嗯/啊/就是/然后) - low = prepared speaker
Speaking style	Formal/informal, technical depth, decision authority
Topic focus	Areas they discuss most (backend, frontend, product, etc.)
Interaction pattern	Do others ask them questions? Do they assign tasks?

Example analysis output:

Speaker Analysis:
┌──────────┬────────┬──────────┬─────────────┬─────────────┬────────────────────────┐
│ Speaker  │ Words  │ Segments │ Avg Length  │ Filler %    │ Role Guess             │
├──────────┼────────┼──────────┼─────────────┼─────────────┼────────────────────────┤
│ 发言人1  │ 41,736 │ 93       │ 449 chars   │ 3.6%        │ 主讲人 (99% of content)│
│ 发言人2  │ 101    │ 8        │ 13 chars    │ 4.0%        │ 对话者 (short responses)│
└──────────┴────────┴──────────┴─────────────┴─────────────┴────────────────────────┘

Inference rules:
- 占比 > 70% + 平均长度 > 100字 → 主讲人
- 平均长度 < 50字 → 对话者/响应者
- 语气词占比 < 5% → 正式/准备充分
- 语气词占比 > 10% → 非正式/即兴发言

Phase B: Context Mapping (If Context File Provided)

When user provides a project context file (e.g., context.md):

1. Load team directory section
2. Match feature patterns to known team members
3. Cross-reference roles with speaking patterns

Context file should include:

## Team Directory
| Name | Role | Communication Style |
|------|------|---------------------|
| Alice | Backend Lead | Technical, decisive, assigns backend tasks |
| Bob | PM | Product-focused, asks requirements questions |
| Carol | TPM | Process-focused, tracks timeline/resources |

Phase C: Confirmation Before Proceeding

CRITICAL: Never silently assume speaker identity.

Present analysis summary to user:

Speaker Analysis:
- Speaker 1 → Alice (Backend Lead) - 80% confidence based on: technical focus, task assignment pattern
- Speaker 2 → Bob (PM) - 75% confidence based on: product questions, requirements discussion
- Speaker 3 → Carol (TPM) - 70% confidence based on: timeline concerns, resource tracking

Please confirm or correct these mappings before I proceed.

After user confirmation, apply mappings consistently throughout the document.

Step 1.7: Transcript Quality Assessment (Optional)

Evaluate transcript quality to determine processing depth:

Scoring Criteria (1-10 scale):

Factor	Score Impact
Content volume	>10k chars: +2, 5-10k: +1, <2k: cap at 3
Filler word ratio	<5%: +2, 5-10%: +1, >10%: -1
Speaker clarity	Main speaker >80%: +1 (clear presenter)
Technical depth	High technical content: +1

Quality Tiers:

Score	Tier	Processing Approach
≥8	High	Full structured minutes with all sections, diagrams, quotes
5-7	Medium	Standard minutes, focus on key decisions and action items
<5	Low	Summary only - brief highlights, skip detailed transcription

Example assessment:

📊 Transcript Quality Assessment:
- Content: 41,837 chars (+2)
- Filler ratio: 3.6% (+2)
- Main speaker: 99% (+1)
- Technical depth: High (+1)
→ Quality Score: 10/10 (High)
→ Recommended: Full structured minutes with diagrams

User decision point: If quality is Low (<5), ask user:

"Transcript quality is low (碎片对话/噪音较多). Generate full minutes or summary only?"

Step 2: Multi-Turn Initial Generation (Critical)

A single pass will absolutely lose content. Use multi-turn generation with redundant complete passes:

Core Principle: Multiple Complete Passes + UNION Merge

Each pass generates COMPLETE minutes (all sections) from the full transcript. Multiple passes with isolated context catch different details. UNION merge consolidates all findings.

❌ WRONG: Narrow-focused passes (wastes tokens, causes bias)

Pass 1: Only extract decisions
Pass 2: Only extract action items
Pass 3: Only extract discussion

✅ CORRECT: Complete passes with isolated context

Pass 1: Generate COMPLETE minutes (all sections) → version1.md
Pass 2: Generate COMPLETE minutes (all sections) with fresh context → version2.md
Pass 3: Generate COMPLETE minutes (all sections) with fresh context → version3.md
Merge: UNION all versions, consolidate duplicates → draft_minutes.md

Strategy A: Sequential Multi-Pass (Complete Minutes Each Pass)

Pass 1: Read transcript → Generate complete minutes → Write to: <output_dir>/intermediate/version1.md
Pass 2: Fresh context → Read transcript → Generate complete minutes → Write to: <output_dir>/intermediate/version2.md
Pass 3: Fresh context → Read transcript → Generate complete minutes → Write to: <output_dir>/intermediate/version3.md
Merge: Read all versions → UNION merge (consolidate duplicates) → Write to: draft_minutes.md
Final: Compare draft against transcript → Add any remaining omissions → final_minutes.md

Strategy B: Parallel Multi-Agent (Complete Minutes Each Agent) - PREFERRED

MUST use the Task tool to spawn multiple subagents with isolated context, each generating complete minutes:

Implementation using Task tool:

// Launch ALL 3 subagents in PARALLEL (single message, multiple Task tool calls)
Task(subagent_type="general-purpose", prompt="Generate complete meeting minutes from transcript...", run_in_background=false) → version1.md
Task(subagent_type="general-purpose", prompt="Generate complete meeting minutes from transcript...", run_in_background=false) → version2.md
Task(subagent_type="general-purpose", prompt="Generate complet

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