← Skills

/you2idea-extract

Mining video content for business opportunities

install: npx skills add fortunto2/solo-factory/you2idea-extract
source: fortunto2/solo-factory/tree/main/skills/you2idea-extract
phase: analysis

Extract startup ideas from YouTube videos. Two operating modes depending on available tools.

# /you2idea-extract

Extract startup ideas from YouTube videos. Two operating modes depending on available tools.

## Mode Detection

Check which tools are available:
- **With solograph MCP**: use `source_search`, `source_list`, `source_tags`, `source_related` for indexed corpus
- **Without MCP (standalone)**: use yt-dlp + Read for transcript analysis

## MCP Tools (if available)

- `source_search(query, source="youtube")` — semantic search over indexed videos
- `source_list()` — check indexed video counts
- `source_tags()` — auto-detected topics with confidence scores
- `source_related(video_url)` — find related videos by shared tags
- `kb_search(query)` — cross-reference with knowledge base
- `web_search(query)` — discover new videos to index

## Steps

### Mode 1: Index + Analyze (with solograph MCP)

1. **Parse input** from `$ARGUMENTS`:
   - URL (`https://youtube.com/watch?v=...`) → single video index
   - Channel name (`GregIsenberg`) → channel batch index
   - Query text → search existing corpus (skip to step 4)
   - If empty, ask: "Video URL, channel name, or search query?"

2. **Index video(s)** via solograph:
   ```bash
   # Install if needed
   pip install solograph  # or: uvx solograph

   # Single video
   solograph-cli index-youtube -u "$URL"

   # Channel batch (needs web search for discovery)
   solograph-cli index-youtube -c "$CHANNEL" -n 5
   ```

3. **Verify indexing** — `source_list()` to confirm new video count. `source_tags()` for topic distribution.

4. **Search corpus** — `source_search(query="startup ideas", source="youtube")`.

5. **Cross-reference** — `kb_search(query)` for related existing opportunities (if knowledge base available).

6. **Extract insights** — for each relevant video chunk:
   - Identify the startup idea mentioned
   - Note timestamp and speaker context
   - Rate idea potential (specificity, market evidence, feasibility)
   - Flag ideas that match trends or validated patterns

7. **Write results** to `docs/youtube-ideas.md` or print summary.

### Mode 2: Standalone (without MCP)

1. **Parse input** — same as Mode 1 step 1.

2. **Download transcript** via yt-dlp:
   ```bash
   # Check yt-dlp is available
   command -v yt-dlp >/dev/null 2>&1 && echo "yt-dlp: ok" || echo "Install: pip install yt-dlp"

   # Download subtitles only (no video)
   yt-dlp --write-auto-sub --sub-lang en --skip-download -o "transcript" "$URL"

   # Convert VTT to plain text
   sed '/^$/d; /^[0-9]/d; /-->/d; /WEBVTT/d; /Kind:/d; /Language:/d' transcript.en.vtt | sort -u > transcript.txt
   ```

3. **Read transcript** — Read the transcript.txt file.

4. **Analyze for startup ideas:**
   - Scan for business opportunities, pain points, product ideas
   - Note approximate timestamps from VTT cues
   - Rate each idea on specificity and market potential
   - Cross-reference with WebSearch for market validation

5. **For channel analysis** — download multiple video transcripts:
   ```bash
   # Get video list from channel
   yt-dlp --flat-playlist --print "%(id)s %(title)s" "https://youtube.com/@$CHANNEL" | head -10

   # Download transcripts for top videos
   for id in $VIDEO_IDS; do
     yt-dlp --write-auto-sub --sub-lang en --skip-download -o "transcripts/%(id)s" "https://youtube.com/watch?v=$id"
   done
   ```

6. **Write results** to `docs/youtube-ideas.md` with format:
   ```markdown
   # YouTube Ideas — [Channel/Video]
   Date: YYYY-MM-DD

   ## Idea 1: [Name]
   - **Source:** [Video title] @ [timestamp]
   - **Problem:** [What pain point]
   - **Solution:** [What they propose]
   - **Market signal:** [Evidence of demand]
   - **Potential:** [High/Medium/Low] — [why]

   ## Idea 2: ...
   ```

## Common Issues

### yt-dlp not found
**Fix:** `pip install yt-dlp` or `brew install yt-dlp`

### No subtitles available
**Cause:** Video has no auto-generated or manual captions.
**Fix:** Try `--sub-lang en,ru` for multiple languages. Some videos only have auto-generated subs.

### solograph MCP not available
**Fix:** Skills work in standalone mode (yt-dlp + Read). For indexed search across many videos, install solograph: `pip install solograph`. For enhanced web search, set up [SearXNG](https://github.com/fortunto2/searxng-docker-tavily-adapter) (private, self-hosted, free).

### Too many ideas, hard to prioritize
**Fix:** Use `/validate` on the top 3 ideas to score them through STREAM framework.
Sources