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Stream Transcript Processor
Process @techfren stream transcripts into actionable content assets: clips, script notes, X posts, YouTube metadata, and analytics.
This skill orchestrates existing skills and adds new capabilities:
- Uses clipper for clip extraction
- Uses x-post-extractor patterns for X posts
- Uses media-fetcher for screenshots and media
- NEW : Fetch transcripts from URLs (auto-captions)
- NEW : Generate script notes for short-form recording
- NEW : YouTube metadata (title, description with timestamps, SEO tags)
- NEW : Content analytics and insights
When to Use This Skill
Activate when user:
- Provides a Twitch VOD or YouTube stream URL
- Pastes a stream transcript
- Asks to "find clips" or "find highlights" from a stream
- Wants "script notes" for short-form content
- Asks for X post drafts from stream content
- Needs YouTube title, description, or tags
- Requests stream analytics or insights
Quick Commands
| User Request | Action |
|---|
| "Process this stream [URL]" | Full pipeline: fetch → analyze → clips → posts → YouTube metadata |
| "Find clips from [transcript]" | Delegate to clipper skill |
| "Generate script notes for [timestamp]" | Create recording notes |
| "Draft X posts from [stream]" | Delegate to x-post-extractor or use draft script |
| "Generate YouTube description" | Create title, description with timestamps, tags |
| "Analyze this stream" | Content analytics report |
Execution Workflow
Full Pipeline (URL Provided)
When user provides a stream URL:
Phase 1: Fetch Transcript
python .claude/skills/stream-transcript-processor/scripts/fetch_transcript.py "<URL>" transcript.txt
This creates:
transcript.txt - Timestamped text filetranscript.json - Structured JSON for processing
Phase 2: Parse for Clipper Compatibility
python .claude/skills/clipper/scripts/parse_transcription.py transcript.json > parsed.json
Phase 3: Find Clips (Use Clipper Skill)
Follow the clipper skill workflow:
- Analyze
parsed.json in windows
- Identify segments using clipper categories + voice patterns
- Create
segments.json
- Report clip candidates to user
Phase 4: Generate Content Assets
For each high-priority clip:
Script Notes:
python .claude/skills/stream-transcript-processor/scripts/generate_script_notes.py parsed.json <start_ts> <end_ts>
X Post Drafts:
python .claude/skills/stream-transcript-processor/scripts/draft_x_posts.py parsed.json <timestamp> --style discovery
Phase 5: Fetch Media (Optional)
Use media-fetcher for screenshots of tools mentioned:
python .claude/skills/media-fetcher/scripts/capture_screenshot.py "<url>" --output media/
Transcript Already Exists
If parsed.json or out.json already exists:
- Skip fetch_transcript step
- Use existing files for analysis
- Proceed with clipper workflow
- Add script notes and X posts as needed
Script Notes Only
When user asks for script notes for a specific segment:
python .claude/skills/stream-transcript-processor/scripts/generate_script_notes.py \
<transcript_file> <start_timestamp> <end_timestamp> \
--output script_notes.md
Output format (sentence starters for recording):
## HOOK
- [Bold claim or result]...
- This is [Tool] and it...
## VALUE STACK
- Free, open source...
- Runs locally...
## DEMO
- Let's try it...
- [React naturally]
## CLOSE
- [Quick recap]...
- Try it out...
X Posts Only
When user asks to draft X posts from stream:
Option 1: Use x-post-extractor skill (recommended for full workflow)
Follow x-post-extractor SKILL.md for comprehensive extraction.
Option 2: Quick draft from timestamp
python .claude/skills/stream-transcript-processor/scripts/draft_x_posts.py \
<transcript_file> <timestamp> --style <style>
Styles: discovery, result, comparison, tutorial
YouTube Metadata Generation
Generate SEO-optimized YouTube title, description with timestamps, and tags:
python .claude/skills/stream-transcript-processor/scripts/generate_youtube_metadata.py \
<transcript_file> [--title "Custom Title"] [--output youtube_metadata.md]
Output includes:
- Engaging title with hook patterns
- Description with auto-generated timestamps
- Tool links for all mentioned products
- Social links section
- SEO-optimized tags (up to 30)
Example output:
## Title
Claude Haiku Benchmark: New Record! | Live Testing
## Description
🎯 Claude Haiku Benchmark: New Record! | Live Testing
In this stream, I test and demo various AI tools and share my findings live. Watch to see real-time benchmarks, comparisons, and honest reactions.
⏱️ TIMESTAMPS 0:00 - Intro 6:12 - Setting up Playwright MCP 11:05 - Claude Code $200 plan discussion 47:35 - Testing Playrider MCP 1:01:31 - Playrider speed results 1:04:29 - Haiku 43 second record!
🔗 TOOLS MENTIONED • Claude: https://claude.ai • Playwright: https://playwright.dev • Augment: https://augmentcode.com
📱 CONNECT • Twitter/X: https://x.com/techfren • GitHub: https://github.com/aj47 • Website: https://techfren.net
#AI #Coding #Programming #Claude #Benchmark #TechStream
## Tags
claude, haiku, benchmark, ai coding, playwright mcp, live coding, ai tools, programming, developer tools, automation
See youtube-description-template.md for full template and SEO guidelines.
Analytics Report
When user asks to "analyze" a stream, generate insights:
Report Template
# Stream Analytics: [Title/Date]
## Overview
- Duration: X hours
- Total transcript entries: N
- High-energy moments detected: N
## Topics Covered
| Topic | Mentions | Peak Timestamp |
|-------|----------|----------------|
| Cursor | 15 | 01:23:45 |
| Claude | 12 | 02:15:30 |
| ...
## Energy Peaks
Timestamps with highest concentration of reaction words:
1. [01:23:45] "Dude, this is insane..." (Score: 12)
2. [02:15:30] "Whoa, that actually worked..." (Score: 10)
3. ...
## Clip Potential
- High priority clips: N
- Medium priority clips: N
- Estimated short-form content: N minutes
## Value Props Mentioned
- Free: X times
- Open source: X times
- Local/offline: X times
## Recommended Extractions
1. [Timestamp] - [Brief description] - **Clip + X Post**
2. [Timestamp] - [Brief description] - **Script Notes**
3. ...
Integration with Existing Skills
Using Clipper
For full clip extraction, delegate to clipper:
# Claude internally:
# 1. Ensure parsed.json exists
# 2. Follow clipper SKILL.md workflow
# 3. Create segments.json
# 4. Extract clips
The clipper skill handles:
- Window-based analysis
- Segment identification
- FFmpeg extraction
- Cleanup processing
Using X-Post-Extractor
For comprehensive X post extraction:
# Claude internally:
# 1. Ensure parsed.json exists
# 2. Follow x-post-extractor SKILL.md workflow
# 3. Create x_posts_output/ folder
# 4. Include media capture
The x-post-extractor skill handles:
- Trigger phrase detection
- Scoring and prioritization
- Post/thread formatting
- Media fetching
Using Media-Fetcher
For screenshots and supplementary media:
# Screenshots of tools/websites
python .claude/skills/media-fetcher/scripts/capture_screenshot.py "<url>"
# Fetch logos
python .claude/skills/media-fetcher/scripts/fetch_logos.py "<domain>"
# Extract video clips
ffmpeg -ss <start> -i "<video>" -t <duration> clip.mp4
Voice Pattern Detection
When analyzing transcripts, look for these signals (see voice-patterns.md):
High-Value Signals (Score +4)
- Discovery phrases: "just discovered", "trending", "check this out"
- Comparison signals: "better than", "beats", "vs"
- Resolution markers: "finally", "figured it out", "turns out"
Medium-Value Signals (Score +2-3)
- High-energy reactions: "dude", "whoa", "sick", "boom"
- Value props: "free", "open source", "local"
- Demo phrases: "let's see", "let's try", "here we go"
- Result moments: "it worked", "there we go", "boom"
Tool Detection
TOOLS = [
'cursor', 'claude', 'gpt', 'copilot', 'windsurf', 'augment',
'ollama', 'llama', 'mistral', 'groq', 'anthropic', 'openai'
]
Clip Criteria Summary
From clip-criteria.md:
What Makes a Good Clip
- Discovery moments with specific tools/results
- Authentic reactions that feel genuine
- Value props stacked together (free + open source + local)
- Result moments showing proof something works
- Tool demos with live testing
Optimal Lengths
- TikTok/Reels/Shorts: 30-60 seconds
- Longer tutorials: 2-3 minutes
- X video: 30-45 seconds
Skip These
- Slow starts ("so...", "hey what's up...")
- Incomplete thoughts
- Negative content without resolution
- Off-topic tangents
Output Folder Structure
When running full pipeline:
./
├── transcript.txt # Fetched auto-captions
├── transcript.json # Structured transcript
├── parsed.json # Clipper-compatible format
├── segments.json # Identified clips
├── youtube_metadata.md # Title, description, tags
├── clips/ # Extracted video clips
│ ├── 001_Tool_Demo.mp4
│ ├── 002_Discovery.mp4
│ └── cleaned/
│ └── ...
├── script_notes/ # Recording notes
│ ├── 01_tool_demo.md
│ └── 02_discovery.md
├── x_posts_output/ # X post drafts
│ ├── 01_tool.txt
│ ├── 02_discovery.txt
│ └── README.md
└── media/ # Screenshots, logos
├── cursor.com.png
└── github_tool.png
Requirements
For Transcript Fetching
yt-dlp - Download auto-captions (brew install yt-dlp)
For Clip Extraction
ffmpeg - Video processing (brew install ffmpeg)- Python 3.8+
For Screenshots (Optional)
playwright - Browser automation (pip install playwright && playwright install chromium)
Example Usage
Full Pipeline
User: "Process this stream https://youtube.com/watch?v=abc123"
Claude:
1. Fetches transcript → transcript.txt
2. Parses for clipper → parsed.json
3. Analyzes and finds 15 clips
4. Generates script notes for top 5
5. Drafts 3 X posts
6. Captures screenshots for tools mentioned
7. Reports summary with links to all outputs
Quick Script Notes
User: "Generate script notes for the segment at 1:23:45"
Claude:
1. Loads transcript
2. Extracts 60-second window around timestamp
3. Generates HOOK/VALUE/DEMO/CLOSE structure
4. Outputs to terminal or saves to file
Analytics Only
User: "Analyze this stream for clip potential"
Claude:
1. Scans transcript for voice patterns
2. Counts topics, energy peaks, value props
3. Identifies top clip candidates
4. Generates analytics report
Troubleshooting
"No transcript found"
- Video may not have auto-generated captions
- Try different language variants (en-US, en-GB)
- Use full transcription instead (batch-processor skill with
tr alias)
"Low clip scores"
- Stream may be low-energy or monotone
- Lower thresholds or look for teaching moments
- Focus on specific tool mentions
"Script notes feel generic"
- Need more specific timestamp with clear tool/demo
- Check if segment has enough context
- May need to expand timestamp window
Supporting Documents
- voice-patterns.md - Detection patterns for scoring
- clip-criteria.md - What makes a good clip
- script-notes-template.md - Script notes format
- youtube-description-template.md - YouTube SEO template
- clipper/SKILL.md - Full clip extraction workflow
- x-post-extractor/SKILL.md - X post extraction workflow
- x-post-extractor/VOICE.md - Writing voice guidelines
Weekly Installs
1
Repository
aj47/agent-skills
GitHub Stars
1
First Seen
Today
Security Audits
Gen Agent Trust HubPassSocketFailSnykWarn
Installed on
amp1
cline1
openclaw1
opencode1
cursor1
kimi-cli1
