meeting-analyzer

name	meeting-analyzer
description	Manage — Analyzes meeting transcripts and recordings to surface behavioral patterns, communication anti-patterns, and actionable coaching feedback. Use this skill whenever the user uploads
executor	LLM_BEHAVIOR
skill_id	business.project-management.meeting-analyzer
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["business"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
output_schema	[{"name":"result","type":"string","description":"Primary output from meeting analyzer"}]

Meeting Insights Analyzer

Originally contributed by 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