Extract decisions, actions, opinions, questions, and glossary terms from meeting transcripts.
npx skills add https://github.com/pinion05/skills --skill transcript-analyzer
Please help me install this Agent Skill. Skill: transcript-analyzer Source: https://github.com/pinion05/skills/tree/main/transcript-analyzer Command: npx skills add https://github.com/pinion05/skills --skill transcript-analyzer
This skill analyzes meeting transcripts to extract decisions, action items, opinions, questions, and glossary terms using Cerebras AI (llama-3.3-70b). It helps users automatically structure discussions, track commitments, and build glossaries from conversations.
Run the CLI against a meeting transcript to generate a structured analysis. For example: npm run cli -- /path/to/meeting.md -o /path/to/analysis.md
High-intent search queries and answers about installing and using this skill.
To extract decisions and action items from meeting transcripts, run the CLI tool against a transcript file to automatically classify and output structured categories including actions, opinions, and questions. This process uses Cerebras AI to transform conversations into actionable insights.
Yes, you can build a glossary from project review transcripts automatically. The skill identifies domain-specific terms from conversations and generates concise definitions, creating a living glossary alongside extracted decisions and summaries for knowledge capture.
The transcript analyzer works with the Cerebras API, specifically utilizing the Cerebras llama-3.3-70b model to produce deterministic extractions. The environment includes the OpenAI SDK dependency, but extractions are powered by Cerebras for structured analysis.
You run transcript analysis by executing the CLI script with a file path to your meeting transcript and an output path. For example, use 'npm run cli -- /path/to/meeting.md -o /path/to/analysis.md' to generate a structured analysis report.
The best way to summarize meetings for stakeholders is to process transcripts through the analyzer to produce a compact summary of key points and decisions. This generates shareable reports containing structured decisions, action items, and glossary terms.
Yes, you can test transcript extraction without making API calls by using the built-in mock mode. This testing mode allows you to validate the configurable chunking strategy and extraction pipeline locally before processing actual conversations.
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