How do I install the Broll Pick skill?

Run npx skillmds@latest add jperrello/broll-pick in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Broll Pick safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Broll Pick?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Broll Pick free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Broll Pick?

jperrello (@jperrello) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Broll Pick by jperrello — Agent Skill

Name: Broll Pick
Author: jperrello

Broll Pick

Claude picks CONTEXTUAL/scene-level anchors from a clip transcript (literal objects, establishing shots, evocative concept footage matched to the story's tone — not just keyword nouns) and sources full-bleed B-roll cutaways via keyless yt-dlp YouTube search + mcptube frame-sampling + Claude vision verify. The verify step gets the spoken context and rejects literal-but-wrong matches (e.g. a cat laser toy for a tense "red dot" beat). Aims dense (8-12 windows, targeting cutaway footage on ~60%+ of the runtime; emits cutaway_coverage in the plan and WARNs below BROLL_MIN_COVER, default 0.6). Emits broll_plan.json with per-cutaway placement [t0,t1] and source metadata; clip files are namespaced per-clip. Windows snap to whole chunk-captions boundaries; vision calls capped by BROLL_VISION_CAP (default 16).

jperrello 5201c22 10 files · 38.7 KB Updated Sep 17, 2026

File contents

broll-pick

Selects B-roll cutaways for one short. Claude reads the clip transcript + chunks.json (from chunk-captions) + the source ingest.json, picks the 3-5 strongest visualizable nouns/topics (NOUN/VERB/EMOTION/PIVOT), and spawns multiple short cutaway windows per topic — each intercut with the speaker, each preferring distinct footage. Windows are expressed as whole chunk-index ranges so no cut lands mid-word.

Per window: keyless YouTube discovery via the mcptube-bundled yt-dlp (ytsearchN:<query> — mcptube discover is NOT used, it requires an LLM API key the stack doesn't have), then mcptube add the candidate, sample 3 frames via mcptube frame, and show Claude the frame grid (claude -p vision) to pick the best on-subject shot or reject. On reject, the query is rewritten once (literal↔metaphorical) and retried; a second miss drops the window — no fallback footage. Chosen segments download via yt-dlp --download-sections into work/<id>/broll/broll_NN.mp4.

Invoke

.claude/skills/broll-pick/broll-pick.sh <clip_transcript.json> <chunks.json> <ingest.json> <out_broll_plan.json>

clip_transcript: per-clip transcript with words[] (clip-local times)
chunks: chunk-captions output (chunks[] with t0/t1/text)
ingest: work/<id>/ingest.json (title + source url)
out: path for broll_plan.json; b-roll clips land in <ingest_dir>/broll/

Env

BROLL_VISION_CAP (default 10) — max Claude vision calls per clip.
BROLL_PICK=0 — disable; emits an empty plan.
MCPTUBE_BIN, MCPTUBE_YTDLP — override binary paths.

Output (broll_plan.json)

{
  "picks": [
    {"t0": 4.31, "t1": 6.02, "topic": "hippopotamus", "anchor_word": "hippo",
     "query": "hippo swimming underwater", "clip_path": "/abs/work/<id>/broll/broll_03.mp4",
     "source": {"video_id": "abc123XYZ", "title": "...", "url": "https://youtu.be/abc123XYZ",
                "t0_src": 12.4, "t1_src": 14.1},
     "verified": true}
  ],
  "ingested_video_ids": ["abc123XYZ"],
  "vision_calls_used": 4,
  "vision_cap": 10,
  "chunks_mtime": 1746000000.0
}

No anchors / all queries miss → {"picks": [], "ingested_video_ids": []}, exit 0.

Idempotency

Caches on transcript+chunks+ingest mtimes via <out>.pickmeta.

Notes

Records every mcptube add-ed video_id in ingested_video_ids for broll-cleanup. Never the podcast source (filtered by ingest url id).
CPU: 3 frames per candidate, reuses _lib/encode.sh thread caps downstream.

---name: broll-pickdescription: Claude picks CONTEXTUAL/scene-level anchors from a clip transcript (literal objects, establishing shots, evocative concept footage matched to the story's tone — not just keyword nouns) and sources full-bleed B-roll cutaways via keyless yt-dlp YouTube search + mcptube frame-sampling + Claude vision verify. The verify step gets the spoken context and rejects literal-but-wrong matches (e.g. a cat laser toy for a tense "red dot" beat). Aims dense (8-12 windows, targeting cutaway footage on ~60%+ of the runtime; emits cutaway_coverage in the plan and WARNs below BROLL_MIN_COVER, default 0.6). Emits broll_plan.json with per-cutaway placement [t0,t1] and source metadata; clip files are namespaced per-clip. Windows snap to whole chunk-captions boundaries; vision calls capped by BROLL_VISION_CAP (default 16).---# broll-pickSelects B-roll cutaways for one short. Claude reads the clip transcript +`chunks.json` (from chunk-captions) + the source `ingest.json`, picks the 3-5strongest visualizable nouns/topics (NOUN/VERB/EMOTION/PIVOT), and spawnsmultiple short cutaway windows per topic — each intercut with the speaker, eachpreferring distinct footage. Windows are expressed as whole chunk-index rangesso no cut lands mid-word.Per window: keyless YouTube discovery via the mcptube-bundled `yt-dlp`(`ytsearchN:<query>` — `mcptube discover` is NOT used, it requires an LLM APIkey the stack doesn't have), then `mcptube add` the candidate, sample 3 framesvia `mcptube frame`, and show Claude the frame grid (`claude -p` vision) to pickthe best on-subject shot or reject. On reject, the query is rewritten once(literal↔metaphorical) and retried; a second miss drops the window — no fallbackfootage. Chosen segments download via `yt-dlp --download-sections` into`work/<id>/broll/broll_NN.mp4`.## Invoke```.claude/skills/broll-pick/broll-pick.sh <clip_transcript.json> <chunks.json> <ingest.json> <out_broll_plan.json>```- `clip_transcript`: per-clip transcript with `words[]` (clip-local times)- `chunks`: chunk-captions output (`chunks[]` with `t0/t1/text`)- `ingest`: `work/<id>/ingest.json` (title + source url)- `out`: path for `broll_plan.json`; b-roll clips land in `<ingest_dir>/broll/`## Env- `BROLL_VISION_CAP` (default 10) — max Claude vision calls per clip.- `BROLL_PICK=0` — disable; emits an empty plan.- `MCPTUBE_BIN`, `MCPTUBE_YTDLP` — override binary paths.## Output (broll_plan.json)```json{ "picks": [ {"t0": 4.31, "t1": 6.02, "topic": "hippopotamus", "anchor_word": "hippo", "query": "hippo swimming underwater", "clip_path": "/abs/work/<id>/broll/broll_03.mp4", "source": {"video_id": "abc123XYZ", "title": "...", "url": "https://youtu.be/abc123XYZ", "t0_src": 12.4, "t1_src": 14.1}, "verified": true} ], "ingested_video_ids": ["abc123XYZ"], "vision_calls_used": 4, "vision_cap": 10, "chunks_mtime": 1746000000.0}```No anchors / all queries miss → `{"picks": [], "ingested_video_ids": []}`, exit 0.## IdempotencyCaches on transcript+chunks+ingest mtimes via `<out>.pickmeta`.## Notes- Records every `mcptube add`-ed `video_id` in `ingested_video_ids` for broll-cleanup. Never the podcast source (filtered by ingest url id).- CPU: 3 frames per candidate, reuses `_lib/encode.sh` thread caps downstream.

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