Can it identify specific speakers in the transcript?

Yes, provided the transcript includes speaker labels, the skill can isolate your contributions to identify personal speaking patterns and ratios.

How do I provide meeting data to the analyzer?

Simply save your meeting transcripts in formats like .txt, .md, .vtt, or .srt in a folder, navigate there using Claude Code, and ask for the specific analysis you need.

Which meeting platforms are compatible?

It is compatible with transcripts from any platform including Zoom, Google Meet, Microsoft Teams, Otter.ai, Fireflies, and Granola, as long as they are exported to text-based files.

Does it track communication progress over time?

Absolutely. You can ask the skill to compare transcripts from different months or quarters to visualize improvements in areas like active listening or reduced filler word usage.

Meeting Insights Analyzer

Name: Meeting Insights Analyzer
Author: Microck

byMicrock

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Productivity & Workflow

Analyzes meeting transcripts to identify communication patterns, behavioral habits, and actionable leadership feedback.

About

The Meeting Insights Analyzer is a specialized Claude Code skill designed to turn raw meeting transcripts into a roadmap for professional development. By scanning folders of transcript files, it identifies recurring behavioral patterns such as conflict avoidance, speaking-to-listening ratios, and the use of filler words. It provides specific, timestamped examples of communication effectiveness, helping users understand their facilitation style and track interpersonal growth over time. Whether you are a manager seeking to improve team dynamics or an individual contributor working on clarity and directness, this skill provides the objective feedback necessary to become a more influential and effective communicator.

Key Features

Comparative trend analysis across multiple meetings or specific time periods
Automated identification of active listening indicators and clarifying questions
Actionable feedback with timestamped examples and suggested alternative phrasings
89 GitHub stars
Behavioral pattern recognition for conflict avoidance and hedging language
Detailed speaking statistics including ratios, interruptions, and filler word counts

Use Cases

Performance review preparation using concrete data and historical meeting examples
Facilitation style audits to ensure balanced team participation and inclusion
Professional development tracking to improve communication and leadership skills

About

Key Features

Comparative trend analysis across multiple meetings or specific time periods
Automated identification of active listening indicators and clarifying questions
Actionable feedback with timestamped examples and suggested alternative phrasings
89 GitHub stars
Behavioral pattern recognition for conflict avoidance and hedging language
Detailed speaking statistics including ratios, interruptions, and filler word counts

Use Cases

Performance review preparation using concrete data and historical meeting examples
Facilitation style audits to ensure balanced team participation and inclusion
Professional development tracking to improve communication and leadership skills