Claudest search-youtube

install

source · Clone the upstream repo

git clone https://github.com/gupsammy/Claudest

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/gupsammy/Claudest "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/claude-research/skills/search-youtube" ~/.claude/skills/gupsammy-claudest-search-youtube && rm -rf "$T"

manifest: plugins/claude-research/skills/search-youtube/SKILL.md

source content

YouTube Research

Multi-platform video research toolkit. Operates in two modes: toolkit (individual operations) and research (autonomous search-to-synthesis pipeline). All operations use a single CLI at

${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py

Run

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py <subcommand> --help

for full flag reference on any subcommand.

Toolkit Mode

Invoke individual subcommands for targeted operations. Default mode when the user requests a specific action (transcript, search, metadata, audio, channel scan).

Search

Find videos matching a query. Returns structured results with metadata.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py search "<query>" --count 10

Add filters to narrow results:

--min-duration 600

(seconds),

--after 20250101

(YYYYMMDD),

--min-views 50000

. Filters are applied client-side after fetching, so the tool over-fetches automatically to compensate. Output is JSON by default; add

-f text

for human-readable.

Transcript

Download and clean subtitles to LLM-ready text.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py transcript "<url>"

Outputs clean prose to stdout by default. Add

--timestamps

for SRT with timing cues. Add

--save -t <topic>

to persist to

~/youtube-research/<topic>/

. Use

--lang all

to list available subtitle languages before downloading. Fallback chain: manual subs then auto-generated. Exit 4 if no subtitles exist in the requested language.

Metadata

Extract full video information without downloading.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py metadata "<url>"

Returns: title, description, channel, duration, chapters, view/like counts, tags, available subtitle languages, thumbnail URL. Add

--playlist

for playlist entry listings.

Audio

Download audio in the requested format.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py audio "<url>" --audio-format mp3 -t <topic>

Saves to

~/youtube-research/<topic>/audio/

. Supported formats: mp3, m4a, opus, wav. Always saves to disk (audio cannot go to stdout). Prints the file path on success.

Channel

Scan a channel's content.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py channel "<url-or-@handle>" --limit 20

Supports tabs:

--tab videos

(default),

shorts

streams

playlists

. Filter with

--after

--before

(YYYYMMDD). Sort with

--sort views

for most-viewed-first.

Batch Processing

Any subcommand except

search

accepts

--batch <file>

(or

--batch -

for stdin) to process multiple URLs. One URL per line; lines starting with

are skipped.

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py transcript --batch urls.txt --save -t <topic>

Research Mode

Activate when the user asks to "research", "investigate", or "find out about" a topic using YouTube as a source. This is an adaptive multi-round discovery pipeline designed for niche and emerging topics where popular videos often under-serve.

Round 1: Divergent Search

Generate 4-6 query variants that cover different angles of the topic:

Exact tool/concept name (e.g.,
```
"openclaw"
```
)
Tool + ecosystem context (e.g.,
```
"openclaw claude code"
```
)
Problem the tool solves (e.g.,
```
"claude code documentation framework"
```
)
Workflow/demo framing (e.g.,
```
"openclaw walkthrough demo"
```
)
Alternative names, abbreviations, or common misspellings if applicable

Spawn one

Task

agent per query variant simultaneously, each running:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py search "<query-variant>" --count 15

Collect all results and deduplicate by video ID. Aim for 30-50 unique candidates across all threads.

Round 1 Evaluation: Niche-First Heuristics

For niche, emerging, or edge-of-tech topics, these signals predict quality:

Positive signals (use these):

Title specifically names the tool or concept (not "top AI tools 2025")
Small channel (< 50K subscribers) — for new tech, practitioners publish before educators discover the topic
Technical, specific description (mentions code, config, architecture, or commands)
Structured content: chapters, timestamps, or detailed description
Recent upload date

Negative signals (treat as red flags on niche topics):

View count > 100K — on a narrow topic this usually means beginner-level or clickbait
"Tutorial for beginners" / "complete guide" in the title for brand-new tools
Large generalist channel covering many unrelated topics

Select 6-10 candidates from the combined pool. Note which channels produced the strongest results — those are targets for Round 2.

Round 2: Channel Discovery and Refinement

For each channel that surfaced a strong Round 1 result, scan its recent videos:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py channel "<channel-url>" --limit 20

Also run 1-2 refined search queries using specific terminology that appeared in strong Round 1 titles or descriptions. Add any new candidates to the pool.

Quality gate: If Round 1 candidates are weak (generic titles, all high-view generalist content, nothing specifically about the topic), surface this to the user and run another search round with reformulated queries before proceeding to transcripts.

Round 3: Confirm and Transcribe

From the enriched candidate pool, select 4-7 videos using the Round 1 criteria. Extract metadata to confirm relevance before committing to downloads:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py metadata "<url>"

Download transcripts in parallel — always spawn one

Task

agent per video, even for 2:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/search-youtube/scripts/yt_research.py transcript "<url>" --save -t <topic>

Read each transcript and synthesize findings into a report. Use

WebSearch

or brave-cli to cross-reference claims or fill gaps when YouTube sources disagree or leave questions unanswered.

Research Report Format

Present the synthesis as a structured markdown report:

Title and one-paragraph summary of the research question
Key findings (3-7 bullet points of the most important takeaways across all sources)
Points of agreement between sources (what multiple videos confirm)
Points of disagreement (where sources contradict, with attribution)
Unique insights (notable points from individual videos not repeated elsewhere)
Gaps in coverage (what the sources collectively missed)
Sources table: video title, channel, duration, and URL for each video used

Attribute specific claims to their source video. Include timestamps when the transcript preserves them. Save all transcripts under the topic directory for future reference. See

examples/research-report.md

for a sample report structure.

Research Composability

See

references/cli-reference.md

for pipeline patterns that chain subcommands with standard Unix tools (search → jq → batch transcript).

Error Recovery

Exit Code	Meaning	Recovery Action
0	Success	—
1	Usage error	Check `--help` for correct syntax
2	yt-dlp not found	Tell user to install: `pip install yt-dlp`
3	Network/download error	Check URL validity; try `--cookies <browser>` for private/restricted content
4	No results	For transcripts: try `--lang all` to list available languages. For search: broaden query or remove filters

Platform Notes

Load

references/platforms.md

when processing a non-YouTube URL or when a yt-dlp command fails with exit code 3 on an unfamiliar platform. YouTube is the primary platform, but any yt-dlp-supported URL works (Vimeo, Twitter, Twitch, etc.).

After extracting a transcript, read the output and summarize key points for the user unless they asked for raw output only.