Meeting Minutes Taker

Intelligent File Naming

Auto-generate output filename from transcript content:

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

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:

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):

Quality Tiers:

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 complete meeting minutes from transcript...", run_in_background=false) → version3.md

// After all complete:
Main Agent: Read all versions → UNION merge, consolidate duplicates → draft_minutes.md

CRITICAL: Subagent Prompt Must Include:

1. Full path to transcript file
2. Full path to output file (version1.md, version2.md, version3.md in transcript-specific subdirectory)
3. Context files to load (project-specific context if provided, meeting_minutes_template.md)
4. Reference images/documents if provided by user
5. Output language requirement (match user's language preference, preserve technical terms in English)
6. Quote formatting requirement (see Quote Formatting Requirements section below)

Why multiple complete passes work:

• Each pass independently analyzes the SAME content
• Different context states catch different details (no single pass catches everything)
• Pass 1 might catch decision X but miss action item Y
• Pass 2 might catch action item Y but miss decision X
• UNION merge captures both X and Y

Why isolated context matters:

• Each pass/agent starts fresh without prior assumptions
• No cross-contamination between passes
• Different "perspectives" emerge naturally from context isolation

Progressive Context Offloading (Use File System)

Critical: Write each pass output to files, not conversation context.

Path Convention: All intermediate files should be created in a transcript-specific subdirectory under <output_dir>/intermediate/ to avoid conflicts between different transcripts being processed.

CRITICAL: Use transcript-specific subdirectory structure:

<output_dir>/intermediate/<transcript-name>/version1.md
<output_dir>/intermediate/<transcript-name>/version2.md
<output_dir>/intermediate/<transcript-name>/version3.md

Example: If final minutes will be project-docs/meeting-minutes/2026-01-14-api-design.md, then:

• Intermediate files: project-docs/meeting-minutes/intermediate/2026-01-14-api-design/version1.md
• This prevents conflicts when multiple transcripts are processed in the same session
• The intermediate/ folder should be added to .gitignore (temporary working files)

// Create transcript-specific subdirectory first