meeting-analyzer – skill Tài liệu & Office cho agent AI

Meeting Insights Analyzer

> Originally contributed by [maximcoding](https://github.com/maximcoding) — enhanced and integrated by the claude-skills team.

Transform meeting transcripts into concrete, evidence-backed feedback on communication patterns, leadership behaviors, and interpersonal dynamics.

Core Workflow

1. Ingest & Inventory

Scan the target directory for transcript files (.txt, .md, .vtt, .srt, .docx, .json).

For each file:

• Extract meeting date from filename or content (expect YYYY-MM-DD prefix or embedded timestamps)
• Identify speaker labels — look for patterns like Speaker 1:, [John]:, John Smith 00:14:32, VTT/SRT cue formatting
• Detect the user's identity: ask if ambiguous, otherwise infer from the most frequent speaker or filename hints
• Log: filename, date, duration (from timestamps), participant count, word count

Print a brief inventory table so the user confirms scope before heavy analysis begins.

2. Normalize Transcripts

Different tools produce wildly different formats. Normalize everything into a common internal structure before analysis:

{ speaker: string, timestamp_sec: number | null, text: string }[]

Handling per format:

• VTT/SRT: Parse cue timestamps + text. Speaker labels may be inline (<v Speaker>) or prefixed.
• Plain text: Look for Name: or [Name] prefixes per line. If no speaker labels exist, warn the user that per-speaker analysis is limited.
• Markdown: Strip formatting, then treat as plain text.
• DOCX: Extract text content, then treat as plain text.
• JSON: Expect an array of objects with speaker/text fields (common Otter/Fireflies export).

If timestamps are missing, degrade gracefully — skip timing-dependent metrics (speaking pace, pause analysis) but still run text-based analysis.

3. Analyze

Run all applicable analysis modules below. Each module is independent — skip any that don't apply (e.g., skip speaking ratios if there are no speaker labels).

---

Module: Speaking Dynamics

Calculate per-speaker:

• Word count & percentage of total meeting words
• Turn count — how many times each person spoke
• Average turn length — words per uninterrupted speaking turn
• Longest monologue — flag turns exceeding 60 seconds or 200 words
• Interruption detection — a turn that starts within 2 seconds of the previous speaker's last timestamp, or mid-sentence breaks

Produce a per-meeting summary and a cross-meeting average if multiple transcripts exist.

Red flags to surface:

• User speaks > 60% in a 1:many meeting (dominating)
• User speaks < 15% in a meeting they're facilitating (disengaged or over-delegating)
• One participant never speaks (excluded voice)
• Interruption ratio > 2:1 (user interrupts others twice as often as they're interrupted)

---

Module: Conflict & Directness

Scan the user's speech for hedging and avoidance markers:

Hedging language (score per-instance, aggregate per meeting):

• Qualifiers: "maybe", "kind of", "sort of", "I guess", "potentially", "arguably"
• Permission-seeking: "if that's okay", "would it be alright if", "I don't know if this is right but"
• Deflection: "whatever you think", "up to you", "I'm flexible"
• Softeners before disagreement: "I don't want to push back but", "this might be a dumb question"

Conflict avoidance patterns (requires more context, flag with confidence level):

• Topic changes after tension (speaker A raises problem → user pivots to logistics)
• Agreement-without-commitment: "yeah totally" followed by no action or follow-up
• Reframing others' concerns as smaller than stated: "it's probably not that big a deal"
• Absent feedback in 1:1s where performance topics would be expected

For each flagged instance, extract:

• The full quote (with surrounding context — 2 turns before and after)
• A severity tag: low (single hedge word), medium (pattern of hedging in one exchange), high (clearly avoided a necessary conversation)
• A rewrite suggestion: what a more direct version would sound like

---

Module: Filler Words & Verbal Habits

Count occurrences of: "um", "uh", "like" (non-comparative), "you know", "actually", "basically", "literally", "right?" (tag question), "so yeah", "I mean"

Report:

• Total count per meeting
• Rate per 100 words spoken (normalizes across meeting lengths)
• Breakdown by filler type
• Contextual spikes — do fillers increase in specific situations? (e.g., when responding to a senior stakeholder, when giving negative feedback, when asked a question cold)

Only flag this as an issue if the rate exceeds ~3 per 100 words. Below that, it's normal speech.

---

Module: Question Quality & Listening

Classify the user's questions:

• Closed (yes/no): "Did you finish the report?"
• Leading (answer embedded): "Don't you think we should ship sooner?"
• Open genuine: "What's blocking you on this?"
• Clarifying (references prior speaker): "When you said X, did you mean Y?"
• Building (extends another's idea): "That's interesting — what if we also Z?"

Good listening indicators:

• Clarifying and building questions (shows active processing)
• Paraphrasing: "So what I'm hearing is..."
• Referencing a point someone made earlier in the meeting
• Asking quieter participants for input

Poor listening indicators:

• Asking a question that was already answered
• Restating own point without acknowledging the response
• Responding to a question with an unrelated topic

Report the ratio of open/clarifying/building vs. closed/leading questions.

---

Module: Facilitation & Decision-Making

Only apply when the user is the meeting organizer or facilitator.

Evaluate:

• Agenda adherence: Did the meeting follow a structure or drift?
• Time management: How long did each topic take vs. expected?
• Inclusion: Did the facilitator actively draw in quiet participants?
• Decision clarity: Were decisions explicitly stated? ("So we're going with option B — Sarah owns the follow-up by Friday.")
• Action items: Were they assigned with owners and deadlines, or left vague?
• Parking lot discipline: Were off-topic items acknowledged and deferred, or did they derail?

---

Module: Sentiment & Energy

Track the emotional arc of the user's language across the meeting:

• Positive markers: enthusiastic agreement, encouragement, humor, praise
• Negative markers: frustration, dismissiveness, sarcasm, curt responses
• Neutral/flat: low-energy responses, monosyllabic answers

Flag energy drops — moments where the user's engagement visibly decreases (shorter turns, less substantive responses). These often correlate with discomfort, boredom, or avoidance.

---

4. Output the Report

Structure the final output as a single cohesive report. Use this skeleton — omit any section where data was insufficient:

# Meeting Insights Report

**Period**: [earliest date] – [latest date]
**Meetings analyzed**: [count]
**Total transcript words**: [count]
**Your speaking share (avg)**: [X%]

---

## Top 3 Findings

[Rank by impact. Each finding gets 2-3 sentences + one concrete example with a direct quote and timestamp.]

## Detailed Analysis

### Speaking Dynamics
[Stats table + narrative interpretation + flagged red flags]

### Directness & Conflict Patterns
[Flagged instances grouped by pattern type, with quotes and rewrites]

### Verbal Habits
[Filler word stats, contextual spikes, only if rate > 3/100 words]

### Listening & Questions
[Question type breakdown, listening indicators, specific examples]

### Facilitation
[Only if applicable — agenda, decisions, action items]

### Energy & Sentiment
[Arc summary, flagged drops]

## Strengths
[3 specific things the user does well, with evidence]

## Growth Opportunities
[3 ranked by impact, each with: what to change, why it matters, a concrete "try this next time" action]

## Comparison to Previous Period
[Only if prior analysis exists — delta on key metrics]

5. Follow-Up Options

After delivering the report, offer:

• Deep dive into any specific meeting or pattern
• A 1-page "communication cheat sheet" with the user's top 3 habits to change
• Tracking setup — save current metrics as a baseline for future comparison
• Export as markdown or structured JSON for use in performance reviews

---

Edge Cases

• No speaker labels: Warn the user upfront. Run text-level analysis (filler words, question types on the full transcript) but skip per-speaker metrics. Suggest re-exporting with speaker diarization enabled.
• Very short meetings (< 5 minutes or < 500 words): Analyze but caveat that patterns from short meetings may not be representative.
• Non-English transcripts: The filler word and hedging dictionaries are English-centric. For other languages, note the limitation and focus on structural analysis (speaking ratios, turn-taking, question counts).
• Single meeting vs. corpus: If only one transcript, skip

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