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: Read the transcript provided by user Load project-specific context file if provided by user (optional) Intelligent file naming : Auto-generate filename from content (see below) 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 Multi-turn generation : Use multiple passes or subagents with isolated context, merge using UNION Self-review using references/completeness_review_checklist.md Present draft to user for human line-by-line review Cross-AI comparison (optional): Human may provide output from other AI tools (e.g., Gemini, ChatGPT) - merge to reduce bias 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 ): Load team directory section Match feature patterns to known team members 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: Full path to transcript file Full path to output file (version1.md, version2.md, version3.md in transcript-specific subdirectory) Context files to load (project-specific context if provided, meeting_minutes_template.md) Reference images/documents if provided by user Output language requirement (match user's language preference, preserve technical terms in English) Quote formatting requirement (see Quote Formatting Requirements section below) 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