ReddRadar
Agent skill
youtube-to-markdown
Use when user asks YouTube video extraction, get, fetch, transcripts, subtitles, or captions. Writes video details and transcription into structured markdown file.
Install this agent skill to your Project
npx add-skill https://github.com/vre/flow-state/tree/main/docs/youtube-to-markdown/research/2026.01.14 token usage test/code/lite
SKILL.md
YouTube to Markdown (Lite Variant)
Optimized variant: No polish steps (4, 7, 8) + combined workflows (5+6, 10a+10b). Result: 2 subagents for minimum cost.
Execute all steps sequentially without asking for user approval.
Step 0: Check extracted before
python3 ./check_existing.py "<YOUTUBE_URL>" "<output_directory>"
If returns exists: true: Skip to Step 10 (comments).
Step 1: Extract data
python3 extract_data.py "<YOUTUBE_URL>" "<output_directory>"
Step 2: Extract transcript
If video language is en, proceed directly. If non-English, ask user which language.
python3 extract_transcript.py "<YOUTUBE_URL>" "<output_directory>" "<LANG_CODE>"
Fallback (only if transcript unavailable)
python3 extract_transcript_whisper.py "<YOUTUBE_URL>" "<output_directory>"
Step 3: Deduplicate transcript
python3 ./deduplicate_vtt.py "<output_directory>/${BASE_NAME}_transcript.vtt" "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript_no_timestamps.txt"
Copy dedup as final transcript (no polish steps):
cp "<output_directory>/${BASE_NAME}_transcript_dedup.md" "<output_directory>/${BASE_NAME}_transcript.md"
Step 5: Summarize transcript (combined summarize + tighten)
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
INPUT: <output_directory>/${BASE_NAME}_transcript_no_timestamps.txt
OUTPUT: <output_directory>/${BASE_NAME}_summary_tight.md
FORMATS: ./summary_formats.md
1. Classify content type:
- TIPS: gear reviews, rankings, practical advice
- INTERVIEW: podcasts, conversations, Q&A
- EDUCATIONAL: concept explanations, analysis
- TUTORIAL: step-by-step instructions
2. Read FORMATS and create summary using format for detected type.
3. Self-review: Cut fluff, enforce <10% of transcript bytes, prefer lists over prose.
4. Save final tightened summary to OUTPUT.
Rules:
- Skip ads, sponsors, self-promotion
- Preserve original language
ACTION REQUIRED: Use Write tool NOW to save to OUTPUT file.
Step 10: Extract and analyze comments (combined)
python3 ./extract_comments.py "<YOUTUBE_URL>" "<output_directory>"
python3 ./prefilter_comments.py "<output_directory>/${BASE_NAME}_comments.md" "<output_directory>/${BASE_NAME}_comments_prefiltered.md"
task_tool:
- subagent_type: "general-purpose"
- model: "sonnet"
- prompt:
SUMMARY: <output_directory>/${BASE_NAME}_summary_tight.md
COMMENTS: <output_directory>/${BASE_NAME}_comments_prefiltered.md
Read SUMMARY to understand video content and type.
Extract insights from COMMENTS that ADD VALUE beyond summary:
- Corrections/Extensions
- Alternative applications
- Debates/controversies
Format as:
## Comment Insights
**Key Takeaway**: [one sentence]
[Categories based on video type with @username attributions]
Rules:
- Only insights NOT already in summary
- Be ruthlessly concise
ACTION REQUIRED: Read SUMMARY, append Comment Insights section, and write back using Write tool.
Step 9: Create output files
python3 finalize.py "${BASE_NAME}" "<output_directory>"
Outputs:
youtube - {title} ({video_id}).md- Main summary with comment insightsyoutube - {title} - transcript ({video_id}).md- Description and transcript
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