meeting-processor

Detect meeting type and extract structured data from transcripts.

Updated Apr 16, 2026

Search Tags:#interactive #transcripts #auto-detect #meeting #batch-processing #structured-analysis

Ready to run

npx skills add https://github.com/KISHOR403/claude-skills --skill meeting-processor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: meeting-processor
Source: https://github.com/KISHOR403/claude-skills/tree/main/meeting-processor
Command: npx skills add https://github.com/KISHOR403/claude-skills --skill meeting-processor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, pyyaml, and includes scripts (resource) components.

What problem does it solve?

Intelligent meeting transcripts require manual analysis and extraction of commitments, decisions, and follow-ups. This skill automates detection of meeting type and generates a structured, type-specific analysis that can be appended to transcripts.

Core Features & Use Cases

Auto-detects meeting type (leadgen, partnership, coaching, internal) from transcripts.
Type-specific extraction: commitments, follow-ups, context, and sentiment per type.
Interactive mode for clarifying questions and guided data completion.
Batch mode for automated processing of large transcript sets.
Frontmatter-aware: preserves metadata and appends a consistent Meeting Analysis section.

Quick Start

Run the processor on a transcript to auto-detect its type and generate a structured analysis.

Frequently Asked Questions about meeting-processor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I auto-detect meeting type from transcripts and extract structured insights?▼

To auto-detect meeting type from transcripts, the processor identifies whether the session is leadgen, partnership, coaching, or internal, then extracts commitments, follow-ups, context, and sentiment into a structured payload appended directly to the transcript.

Can I batch process meeting transcripts to extract decisions and follow-ups?▼

Yes, batch processing meeting transcripts is fully supported. The processor automates type detection and structured extraction across large sets of real-world sales, partnership, coaching, and internal review transcripts without requiring manual intervention.

Do I need Python and OpenAI to extract structured data from meeting transcripts?▼

Yes, extracting structured meeting data requires Python 3.8+ and an OpenAI or Cerebras interface. These dependencies handle the natural language processing needed to identify meeting types and generate type-specific analysis sections.

What is the best way to preserve YAML frontmatter when appending meeting analysis to transcripts?▼

The best way to preserve YAML frontmatter during meeting analysis is using a frontmatter-aware processor that maintains existing metadata integrity while appending a consistent, structured Meeting Analysis section to the end of the transcript file.

Does interactive mode support clarifying questions for incomplete meeting transcripts?▼

Yes, interactive mode supports clarifying questions for incomplete transcripts. It guides users through data completion by asking targeted questions, ensuring accurate meeting type detection and comprehensive extraction of commitments and follow-ups.

Why does my structured meeting analysis return the wrong meeting type?▼

Wrong meeting type detection usually happens when transcript content is ambiguous or lacks clear indicators for leadgen, partnership, coaching, or internal categories. Using interactive mode can help clarify context and improve auto-detection accuracy.