meeting-minutes-taker

name

meeting-minutes-taker

description

Transforms raw meeting transcripts into high-fidelity, structured meeting minutes (notes / summaries). Use when (1) a meeting transcript is provided and meeting minutes, notes, or a summary are requested; (2) multiple versions of minutes must be merged without losing content; (3) existing minutes need review against the original transcript for missing items; (4) the transcript has anonymous speakers like "Speaker 1/2/3" or "发言人1" that need identifying (optionally mapped via a context.md team directory). Triggers on 会议纪要 / 会议记录 / 整理纪要 / 妙记转纪要, "write meeting minutes", "summarize this meeting", "merge these minutes", "what's missing from these notes". For fixing ASR/STT recognition errors in the raw transcript first, use transcript-fixer; this skill structures clean transcripts into minutes.

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 doc-to-markdown 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", FIRST ask the user to label speakers on the source platform and re-export (see Step 1.5 Phase 0); infer from text only as a fallback
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")
  - [ ] Phase 0 FIRST: ask user to label speakers on the source platform (Feishu Minutes / Tencent Meeting), re-export, use labeled transcript
  - [ ] Fallback only (source labeling unavailable or declined by user):
    - [ ] Analyze speaker features (word count, style, topic focus)
    - [ ] Match against context.md team directory (if provided)
    - [ ] Present speaker mapping with per-speaker evidence 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 3.5: Retrieval Self-Test (consumption-side verification)
  - [ ] Fresh-context subagent extracts future-query claims list from transcript ONLY (never sees draft) → intermediate/<transcript-name>/retrieval-claims.md
  - [ ] Hit-test each claim against retrievable layer (Key Decisions / Action Items / Parking Lot / Open Questions), by component, with lexical anchors
  - [ ] Revocation scan before ANY promotion; promote with [self-test promoted] tag + greppable verbatim quote; uncertain → Open Questions
  - [ ] Report "enumerated N / hits M / promoted K / uncertain list" (never a binary pass); fail-open with visible NOT-RUN note if extraction fails
- [ ] 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.

Phase 0: Source-Side Labeling (ALWAYS TRY FIRST)

When the transcript comes from a platform that supports manual speaker labeling (Feishu Minutes 飞书妙记, Tencent Meeting 腾讯会议, or any tool with a diarization-editing page), stop and ask the user to label the speakers at the source, then re-export/re-ingest the labeled transcript before generating minutes. Send the user the source page link — it is usually in the transcript's frontmatter (minute_url, meeting URL).

Why this beats inference:

• Platform labeling is a human listening to the actual voices — the authoritative source. Text-based inference can only resolve speakers who happen to get name-called during the meeting; everyone else stays a guess.
• Diarization-merged segments (multiple people collapsed into one label) are unrecoverable from text alone — no amount of inference fixes them, but source-side relabeling does.
• Inference output forces [inferred] markers everywhere plus a per-speaker human review round; source labeling produces clean ground truth once.

Fall back to Phase A–C below only when: (a) the user explicitly says to proceed by inference, or (b) the source cannot be labeled (raw audio file with no platform page, no edit permission). In the fallback, every mapping must carry evidence and a confidence level, unresolved labels stay as-is (never force-assign), and when the user later labels the source, go back and correct the minutes.

Fallback 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 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)
7. Decision-recognition rule: identify decisions by elements, not phrasing — an instruction embedded in narrative/case-review speech is still a decision if it has an authority (who), an action (do what), and optionally a condition (until when). Precision guardrail: when unsure whether something is a real decision, route it to Open Questions instead of Key Decisions (never inflate the decision table).

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
mkdir: <output_dir>/intermediate/<transcript-name>/

// Launch all 3 subagents IN PARALLEL (must be single message with 3 Task tool calls)
Task 1 → Write to: <output_dir>/intermediate/<transcript-name>/version1.md (complete minutes)
Task 2 → Write to: <output_dir>/intermediate/<transcript-name>/version2.md (complete minutes)
Task 3 → Write to: <output_dir>/intermediate/<transcript-name>/version3.md (complete minutes)

Merge Phase:
  Read: <output_dir>/intermediate/<transcript-name>/version1.md
  Read: <output_dir>/intermediate/<transcript-name>/version2.md
  Read: <output_dir>/intermediate/<transcript-name>/version3.md
  → UNION merge, consolidate duplicates, INCLUDE ALL DIAGRAMS → Write to: draft_minutes.md

Final Review:
  Read: draft_minutes.md
  Read: original_transcript.md
  → Compare & add omissions → Write to: final_minutes.md

Benefits of file-based context offloading:

• Conversation context stays clean (avoids token overflow)
• Intermediate results persist (can be re-read if needed)
• Each pass starts with fresh context window
• Merge phase reads only what it needs
• Human can inspect intermediate files for review - Critical for understanding what each pass captured
• Supports very long transcripts that exceed context limits
• Enables post-hoc debugging - If final output is missing something, human can trace which pass missed it

IMPORTANT: Always preserve intermediate versions in transcript-specific subdirectory:

• <output_dir>/intermediate/<transcript-name>/version1.md, version2.md, version3.md - Each subagent output
• These files help human reviewers understand the merge process
• Do NOT delete intermediate files after merge
• Human may want to compare intermediate versions to understand coverage gaps
• Add intermediate/ to .gitignore - These are temporary working files, not final deliverables
• Transcript-specific subdirectory prevents conflicts when processing multiple transcripts

Output Requirements

• Chinese output with English technical terms preserved
• Evidence-based decisions - Every significant decision needs a supporting quote
• Structured sections - Executive Summary, Key Decisions, Discussion, Action Items, Parking Lot
• Proper quote formatting - See Quote Formatting Requirements section below
• Mermaid diagrams (STRONGLY ENCOURAGED) - Visual diagrams elevate minutes beyond pure text:
  • ER diagrams for database/schema discussions
  • Sequence diagrams for data flow and API interactions
  • Flowcharts for process/workflow decisions
  • State diagrams for state machine discussions
  • Diagrams make minutes significantly easier for humans to review and understand
• Context-first document structure - Place all reviewed artifacts (UI mockups, API docs, design images) at the TOP of the document (after metadata, before Executive Summary) to establish context before decisions; copy images to meeting-media/<meeting-name>/ folder and embed inline using ![description](meeting-media/...) syntax; include brief descriptions with the visuals - this creates "next level" human-readable minutes where readers understand what was discussed before reading the discussion
• Speaker attribution - Correctly attribute decisions to speakers

Key Rules

• Never assume - Ask user to confirm if unclear
• Quote controversial decisions verbatim
• Assign action items to specific people, not teams
• Preserve numerical values (ranges, counts, priorities)
• Always use multiple passes - Single turn is guaranteed to lose content
• Normalize equivalent terminology - Treat trivial variations (e.g., "backend architecture" vs "backend", "API endpoint" vs "endpoint") as equivalent; do NOT point out or highlight such differences between speakers
• Single source of truth - Place each piece of information in ONE location only; avoid duplicating tables, lists, or summaries across sections (e.g., API list belongs in Discussion OR Reference, not both)
• Decisions are identified by elements, not phrasing - "We decided X" is not required; a narrative-voiced instruction with who + do-what (+ condition) belongs in Key Decisions, not buried in Discussion prose

Step 3: Self-Review for Completeness

After initial generation, immediately review against transcript:

Completeness Checklist:
- [ ] All discussion topics covered?
- [ ] All decisions have supporting quotes?
- [ ] All speakers attributed correctly?
- [ ] All action items have specific owners?
- [ ] Numerical values preserved (ranges, counts)?
- [ ] Entity relationships captured?
- [ ] State machines complete (all states listed)?

If gaps found, add missing content silently without mentioning what was missed.

Step 3.5: Retrieval Self-Test (Consumption-Side Verification)

Why this step exists. The three generation subagents share one prompt and one implicit model of "what a decision looks like" — their errors are correlated, so UNION merge protects against content loss but not against a shared blind spot. Step 3's checklist verifies the decisions you already identified; it cannot find what none of the passes classified as a decision. Meanwhile, minutes are consumed by retrieval: weeks later someone asks "did X ever say we must do Y?" and scans the Key Decisions / Action Items tables. A directive that lives only in Discussion prose fails that query even though no content was "lost". This step tests that terminal property directly. (Honest scope: it is a second, partially-decorrelated recall layer — not an oracle. Known blind spots it does NOT cover: assertions whose pragmatic force contradicts their phrasing ("fine, do it without load-testing, just don't say I didn't warn you" = an on-record objection), and silent-consent moments that produce no utterance at all.)

Procedure (after merge, before presenting the draft):

1. Reverse-extract a future-query claims list. Spawn ONE fresh-context subagent that reads ONLY the transcript — it must NOT see the draft minutes (a speaker-name mapping is fine; the anchoring risk comes from the draft's structure, not from a roster). Its task:

   Imagine project members returning 1–8 weeks from now to verify: "did [person] ever instruct / promise / decide / veto / commit-a-number on X?" Enumerate every such claim. Each claim = who + type (instruction / promise / decision / veto / number-commitment / conditional-consensus) + content + scope (+ expiry condition if any) + timestamp. Include only claims with a clear owner; skip opinions and background narration.

   For long transcripts (roughly > 60k characters or > 90 minutes), extract in overlapping chunks and merge — a single pass inherits lost-in-the-middle attention decay exactly where long meetings bury directives. Write the list to <output_dir>/intermediate/<transcript-name>/retrieval-claims.md (auditable evidence that the check ran, and reviewable by the human later).

2. Hit-test each claim against the retrievable layer (main agent): the retrievable layer = Key Decisions titles + Decision lines, Action Items table, Parking Lot, Open Questions — a hit means a reader scanning ONLY those finds the claim, no prose reading required. Judge hits by component: who / action / deadline / expiry-condition each need to be present (a table row that names the task but drops the expiry condition is a partial miss). Require lexical anchors — the row must share the claim's key actor/action words; do not accept purely semantic "an LLM could argue they match" equivalence, because the future reader is a hurried human, not a charitable model.

3. Disposition for misses. Before promoting anything, run a revocation scan: grep the transcript for later reversals / modifications / conditioning of the same topic ("earlier we said air-freight — hold that until customs confirms"). A claim that was superseded in-meeting must NOT be promoted as-is; record the final state instead. This scan is mandatory — promoting a revoked decision is the one way this step can make minutes worse than not running it. Then:

   • Miss + genuinely an instruction/promise/decision → promote into Key Decisions or Action Items, tagged [self-test promoted], with a verbatim quote + timestamp that can be grepped back to the transcript (a promotion without a greppable quote is rejected — this caps hallucinated-claim risk).
   • Miss + uncertain → route to Open Questions / Parking Lot. Never let uncertain promotions inflate the decision table; its precision IS its retrievability.
   • Miss + genuinely just detail → leave in Discussion, but be able to say why it won't be queried.

Step 4: Present to User for Human Review

Present the complete minutes as a draft for human review. Emphasize:

• Minutes require careful line-by-line human review
• Domain experts catch terminology conflicts AI may miss
• Final version emerges through iterative refinement

User may:

• Accept as-is (rare for complex meetings)
• Request deeper review for missing content
• Identify terminology issues (e.g., naming conflicts with existing systems)
• Provide another AI's output for cross-comparison

Step 5: Cross-AI Comparison (Reduces Bias)

When human provides output from another AI tool (e.g., Gemini, ChatGPT, etc.):

This step is valuable because:

• Different AI models have different biases - Each AI catches different details
• Cross-validation - Content appearing in both outputs is likely accurate
• Gap detection - Content in one but not the other reveals potential omissions
• Error correction - One AI may catch factual errors the other missed (e.g., wrong date, wrong attendee name)

Comparison Process:

1. Read the external AI output carefully
2. Identify items present in external output but missing from our draft
3. Verify each item against original transcript before adding (don't blindly copy)
4. Identify items where external AI has errors (wrong facts) - note but don't copy errors
5. UNION merge valid new content into our draft
6. Document any corrections made based on cross-comparison

Example findings from cross-AI comparison:

• Missing decision about API authentication method ✓ (add to our draft)
• Missing naming convention specification ✓ (add to our draft)
• Wrong date (2026-01-13 vs actual 2026-01-14) ✗ (don't copy error)
• Wrong attendee name ✗ (don't copy error)
• Missing database performance concern ✓ (add to parking lot)

Step 6: Iterate on Human Feedback (Critical)

When user requests deeper review ("deep review", "check again", "anything missing"):

1. Re-read transcript section by section
2. Compare each section against current minutes
3. Look for: entities, field names, numerical ranges, state transitions, trade-offs, deferred items
4. Add any omitted content
5. Never claim "nothing missing" without thorough section-by-section review

When user provides another version to merge:

Merge Principle: UNION, never remove

1. Keep ALL content from existing version
2. Add ALL new content from incoming version
3. Consolidate duplicates (don't repeat same info)
4. Preserve more detailed version when depth differs
5. Maintain logical section numbering

Aggressive Diagram Inclusion (CRITICAL)

During merge phase, MUST aggressively include ALL diagrams from ALL versions.

Diagrams are high-value content that took effort to generate. Different subagents may produce different diagrams based on what they focused on. Missing a diagram during merge is a significant loss.

Merge diagram strategy:

1. Inventory ALL diagrams from each version (v1, v2, v3)
2. Include ALL unique diagrams - don't assume a diagram is redundant
3. If similar diagrams exist, keep the more detailed/complete one
4. Check every section that could contain diagrams: Executive Summary, Discussion, API design, State machines, Data flow, etc.

Common diagram types to look for:

• Sequence diagrams (data flow, API interactions)
• ER diagrams (database schema, table relationships)
• State diagrams (state machines, status transitions)
• Flowcharts (decision flows, process workflows)
• Component diagrams (system architecture)

Example: Missed diagram from v3 If v3 has a flowchart for "Status Query Mechanism" but v1/v2 don't have it, that flowchart MUST appear in the merged output. Don't assume it's covered by other diagrams.

Output Language

• Primary: Match the language of the transcript (or user's preference if specified)
• Preserve in English: Technical terms, entity names, abbreviations (standard practice)
• Quotes: Keep original language from transcript

Reference Files

File	When to Load
meeting_minutes_template.md	First generation - Contains template structure
completeness_review_checklist.md	During review steps - Contains completeness checks
context_file_template.md	When helping user create context.md - Contains team directory template
Project context file (user-provided)	When user provides project-specific context (team directory, terminology, conventions)

Recommended Pre-processing Pipeline

Full pipeline for .docx transcripts:

Step 0: doc-to-markdown      # Convert .docx → Markdown (preserves tables/images)
        ↓
Step 0.5: transcript-fixer  # Fix ASR errors (optional, if quality is poor)
        ↓
Step 1+: meeting-minutes-taker  # Generate structured minutes

Commands:

# 1. Install markitdown (one-time)
uv tool install "markitdown[pdf]"

# 2. Convert .docx to markdown
markitdown "录音转写.docx" -o transcript.md

# 3. Then use meeting-minutes-taker on transcript.md

Benefits of combo workflow:

• Tables preserved: markitdown converts Word tables to Markdown tables
• Images extracted: Can be embedded in final minutes
• Cleaner quotes: transcript-fixer removes ASR typos before quote extraction
• Accurate speaker ID: Style analysis works better on clean text
• 正交设计: Each skill does one thing well, composable pipeline

Common Patterns

Architecture Discussions → Mermaid Diagrams (Next-Level Minutes)

Diagrams elevate meeting minutes beyond pure text. They make complex discussions immediately understandable for human reviewers. Always look for opportunities to add visual diagrams.

When to Use Diagrams:

• Data flow discussions → Sequence diagram
• Database schema discussions → ER diagram
• Process/workflow decisions → Flowchart
• State machine discussions → State diagram
• System architecture → Component diagram

Example: Data Flow (Sequence Diagram)

sequenceDiagram
    participant FE as Frontend
    participant BE as Backend
    participant SVC as External Service
    participant DB as Database
    FE->>BE: Click "Submit Order"
    BE->>SVC: POST /process (send data)
    SVC-->>BE: Return {status}
    BE->>DB: Save result
    BE-->>FE: Return success

Example: Database Schema (ER Diagram)

erDiagram
    ORDER ||--o{ ORDER_ITEM : "1:N"
    ORDER {
        uuid id PK
        string customer_name
        decimal total_amount
    }
    ORDER_ITEM {
        uuid id PK
        uuid order_id FK
        int quantity
    }

Example: Version Switching (Workflow Diagram)

sequenceDiagram
    participant User
    participant System
    Note over System: Current: V2 Active
    User->>System: Create V3 (inactive)
    User->>System: Set V2 inactive
    User->>System: Set V3 active
    Note over System: New: V3 Active

Quote Formatting Requirements (CRITICAL)

Quotes MUST use proper markdown blockquote format on separate lines:

❌ WRONG: Inline quote format

* **Quote:** > "This is wrong" - **Speaker**

✅ CORRECT: Blockquote on separate lines

* **Quote:**
  > "This is the correct format" - **Speaker**

✅ CORRECT: Multiple quotes

* **Quote:**
  > "First quote from the discussion" - **Speaker1**
  > "Second quote supporting the same decision" - **Speaker2**

Key formatting rules:

• * **Quote:** on its own line (no quote content on this line)
• Blank line NOT needed after * **Quote:**
• Quote content indented with 2 spaces, then > prefix
• Speaker attribution at end of quote line: - **SpeakerName**
• Multiple quotes use same indentation, each on its own line

Technical Decisions → Decision Block

### 2.X [Category] Decision Title

* **Decision:** Specific decision made
* **Logic:**
  * Reasoning point 1
  * Reasoning point 2
* **Quote:**
  > "Exact quote from transcript" - **Speaker Name**

Deferred Items → Parking Lot

Items with keywords like "defer to later", "Phase 2", "not in MVP" go to Parking Lot with context.

Human-in-the-Loop Iteration (Essential)

Meeting minutes are not one-shot outputs. High-quality minutes emerge through multiple review cycles:

Why Human Review is Critical

1. Terminology conflicts: Humans know existing system naming (e.g., "Note" already means comments in the existing system)
2. Domain context: Humans catch when a term could be confused with another (e.g., "UserProfile" vs "Account")
3. Organizational knowledge: Humans know team conventions and prior decisions
4. Completeness gaps: Humans can request "deep review" review when something feels missing

Example Iteration Pattern

Round 1: Initial generation
  └─ Human review: "Check original transcript for missing items"
Round 2: Deep transcript review, add omitted content
  └─ Human review: "UserProfile conflicts with existing Account entity naming"
Round 3: Update terminology to use "CustomerProfile" instead
  └─ Human review: "Note field conflicts with existing Comment system"
Round 4: Update to use "Annotation" instead of "Note"
  └─ Human approval: Final version ready

Key Principle

The AI generates the first draft; humans refine to the final version. Never assume the first output is complete or uses correct terminology. Always encourage human review and be ready for multiple iteration cycles.

Anti-Patterns

• ❌ Inferring speaker identity from text when the source platform supports labeling - Ask the user to label speakers in Feishu Minutes / Tencent Meeting and re-export FIRST (Step 1.5 Phase 0); text inference only resolves name-called speakers and cannot recover diarization-merged segments
• ❌ Self-reviewing with the same mental model that generated the draft - Step 3's checklist can only verify what the passes already classified; a shared blind spot (e.g., narrative-voiced directives) survives it. Run Step 3.5's fresh-context Retrieval Self-Test; its extractor must never see the draft
• ❌ Promoting a self-test miss without the revocation scan - a decision reversed later in the meeting, promoted as-is, writes a false decision into the table: the one way the self-test makes minutes worse than skipping it
• ❌ Reporting the self-test as a binary "passed" - always report "enumerated N / hits M / promoted K / uncertain list"; a green light with no numbers trains humans to stop reading
• ❌ Single-pass generation - One turn through transcript will absolutely lose content
• ❌ Divided sections without overlap - Each pass must cover FULL transcript, not split by sections
• ❌ Narrow-focused passes - Each pass must generate COMPLETE minutes (all sections), not just one section type (wastes tokens, causes bias)
• ❌ Generic summaries without supporting quotes
• ❌ Action items assigned to "team" instead of specific person
• ❌ Missing numerical values (priorities, ranges, state counts)
• ❌ State machines with incomplete states
• ❌ Circular debate transcribed verbatim instead of summarized
• ❌ Removing content during multi-version merge
• ❌ Claiming "nothing missing" without section-by-section review
• ❌ Treating first draft as final without human review
• ❌ Using terminology without checking for conflicts with existing systems
• ❌ Shared context between subagents (causes cross-contamination and missed content)
• ❌ Keeping all intermediate outputs in conversation context (causes token overflow, use file system)
• ❌ Pure text minutes without diagrams - Architecture/schema discussions deserve visual representation
• ❌ Deleting intermediate files after merge - Preserve for human review and debugging
• ❌ Blindly copying external AI output - Always verify against transcript before merging
• ❌ Ignoring cross-AI comparison opportunity - Different AI models catch different details
• ❌ - MUST launch v1, v2, v3 subagents in PARALLEL using multiple Task tool calls in a single message

Next Step: Export to Deliverable Format

After structuring meeting minutes, suggest exporting:

Meeting minutes complete: [N] decisions, [M] action items captured.

Options:
A) Export as PDF — run /daymade-docs:pdf-creator (Recommended for sharing)
B) Export as slides — run /daymade-docs:ppt-creator (for presentation)
C) No thanks — the markdown is sufficient