summarize-pro

name	summarize-pro
description	Audio/video transcription + intelligent scenario analysis. Supports local audio/video, online video URLs (YouTube, TikTok, Instagram, X, etc.), PDF, images, web pages, and text documents — 8 input types total. Uses platform transcription API (no user API key needed). Auto-detects scenario type (meeting/interview/lecture/podcast/general) and generates a structured analysis report for the Agent to summarize.
metadata	{"openclaw":{"emoji":"🎙️","requires":{"bins":["python3"],"optional_bins":["ffmpeg","yt-dlp"]},"config":{"transcription":{"model":"gpt-4o-mini-transcribe","maxFileSize":26214400,"supportedFormats":["mp3","mp4","mpeg","mpga","m4a","wav","webm","flac","ogg","oga"]}}}}

Overview

This Skill converts various input content into a structured analysis report for the Agent to summarize, in two phases:

1. Skill handles: Content acquisition (download/read) → Transcription → Scenario recognition → Output analysis report
2. Agent handles: Read analysis report → Call user-configured LLM → Generate final summary

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Supported Input Types

Input Type	Example	Processing Method
Local audio	meeting.mp3, record.m4a	Upload directly to transcription API
Local video	video.mp4, lesson.mov	Extract audio then transcribe (requires ffmpeg)
Online video URL	YouTube, TikTok, Instagram, X, etc.	yt-dlp extracts audio then transcribes
PDF file	report.pdf	Agent reads text directly, no script needed
Image	screenshot.png	Agent uses vision, no script needed
Web page URL	https://example.com/article	Agent fetches body content, no script needed
Text document	notes.txt, doc.md	Read file content directly, skip transcription

PDF, images, and web pages are handled directly by the Agent — no need to call summarize.py.

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Core Scripts

scripts/summarize.py — Main Entry Point

Typical usage (Agent call):

python3 skills/summarize-pro/scripts/summarize.py <input_file_or_URL> --full --quiet

Parameters:

Parameter	Description
--full / -f	Full mode: transcription + scenario recognition + analysis report (recommended)
--quiet / -q	Quiet mode: only key progress output, suitable for Agent calls
--transcribe-only / -t	Transcription only, no scenario analysis
--summarize-only	Scenario analysis only (input must be existing text file)
--type <type>	Force scenario type (meeting/interview/lecture/podcast/general)
--language / -l	Language code (default: auto-detect)
--output / -o	Specify output directory; defaults to auto-generated timestamp directory

Output files (default path):

./summarizer-files/<timestamp>/<filename>-transcript.txt
./summarizer-files/<timestamp>/<filename>-summary.md

The last line of stdout outputs the absolute path to summary.md for the Agent to read.

scripts/transcribe.py — Transcription Sub-script

Called internally by summarize.py — Agent does not need to call this directly.

python3 scripts/transcribe.py <audio_file> [--language en]

Transcription result is output to stdout; summarize.py captures it and writes to *-transcript.txt.

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Dependencies

Phase	Dependency	Description
Transcription	Platform transcription API	Built-in, no user API key needed
Scenario Recognition	None	Pure keyword matching, no API needed
Summarization	User-configured LLM	Agent handles this, not in this script
Video format conversion	ffmpeg (optional)	For mov/avi/mkv and files >25MB
Online video download	yt-dlp (optional)	YouTube, TikTok, Instagram, X, etc.

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Scenario Recognition Rules

Logic: Count keyword matches in the first 3000 characters; 3+ hits → classify as that scenario.

Scenario	Code	Keywords	Threshold
Meeting	meeting	discussion, decision, action item, agenda, meeting, team, deadline, owner, next step...	≥3
Interview	interview	interview, pain point, feedback, user, experience, candidate, background...	≥3
Lecture	lecture	course, lecture, learning, concept, outline, student, lesson, chapter...	≥3
Podcast	podcast	podcast, guest, episode, host, topic, story, opinion, show...	≥3
General	general	Fallback when no scenario matches	—

Use --type to force a specific scenario type if auto-detection isn't accurate.

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Workflow

flowchart TD
    A[User Input] --> B{Input Type?}

    B -->|Local audio/video| C[summarize.py transcribes]
    B -->|Online video URL| E[yt-dlp download → transcribe]
    B -->|Text file txt/md| F[Read directly, skip transcription]
    B -->|PDF/image/web page| G[Agent handles directly]

    C --> H[Scenario keyword recognition]
    E --> H
    F --> H

    H --> I[Generate analysis report *-summary.md]
    G --> I

    I --> J[Agent reads report]
    J --> K[Call user LLM for summary]
    K --> L[Output structured Markdown]

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Analysis Report Format

*-summary.md structure:

# 📊 Transcription Analysis Report

Filename, processing time, content type, character count, estimated duration

## 🎯 Scenario Recognition Result
Scenario type + summary strategy guidance

## 📝 Full Transcript
Complete transcription text

## 🤖 Next Step
Agent operation prompt

The Agent reads this report, then follows the "Summary Strategy" to call the LLM and generate the final user summary.

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Error Handling

Error	Behavior
OpenClaw not logged in	sys.exit(1), prints login instructions
yt-dlp not installed	Prints install instructions, return False
Transcription API failure	sys.exit(1), prints API error
File not found	sys.exit(1), prints path
ffmpeg not available	Graceful degradation (upload original file directly), no interruption