Meeting Analyzer

# 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 wan