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Profile clip

Skill jperrello/C0BALT_CUT/.claude/skills/profile-clip

Investigation on using Claude Code to automatically generate profitable YouTube videos.

Install
npx -y skills add jperrello/C0BALT_CUT --skill profile-clip

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Sidecar-free style analyzer — reduce ANY finished vertical short (ours in output/ OR a downloaded Group C exemplar) into a versioned Style Profile JSON using ONLY pixels + audio, never our internal edit plans. Deterministic tier (ffmpeg scene-detect for cut rhythm, silencedetect + RMS for speech/SFX/music, MediaPipe face sampling for cutaway fraction, Canny caption-band density) fills every quantitative field; ONE optional Claude vision call on a director-pass contact sheet fills the enumerated judgment fields (broll_mode, caption_style, production_class, hook_device). The shared organ of the style-replication loop — style-corpus profiles Group C exemplars with it and style-gate profiles our span 0 with it, so comparison is fair (delivered pixels vs delivered pixels). Non-fatal, idempotent (.ppmeta). PROFILE_VISION=0 skips the vision call.

SKILL.md

2.2 KB, 356 tokens by cl100k_base, as published. Nobody here has run it

profile-clip

bash .claude/skills/profile-clip/profile-clip.sh <clip.mp4> <out.styleprofile.json> [--no-vision] [--pane <p>]

Output: <out>.styleprofile.json, style_profile_version: 1 per SPEC-style-replication.md — duration_sec, cuts{cuts_per_min, median_shot_sec, p90_shot_sec, longest_static_gap}, speech{words_per_min, max_silence_sec, speech_fraction} (words_per_min via local whisper when available, else absent), visual{face_fraction, cutaway_fraction, cutaway_count, opens_on_face}, captions{present_fraction, band}, audio{onsets_per_min, music_floor_db, music_present}, hook{first_cut_sec, title_overlay_open}, vision{broll_mode, caption_style, production_class, hook_device, confidence}, meta{needs_vision}.

Routing (UniRoute principle): the cheap tier decides everything it can and lists what it can't in meta.needs_vision; exactly one vision round-trip fires only when that list is non-empty and PROFILE_VISION=1. Vision failure → the deterministic profile ships with vision:{} (the match gate only reads deterministic levered fields, so the gate still works).

Env: PROFILE_VISION (1), PROFILE_FRAMES (12), PROFILE_SAMPLES (16 sampled frames for face/caption stats), PROFILE_SCENE (0.3), PROFILE_TRANSCRIPT (pre-existing transcript json to skip whisper), PROFILE_MUSIC_DB (-48).

Gives 0 of the 12 instructions most video audio skills give in 356 tokens

Counted across 622 of the 795 authors here whose files we hold, read 2026-08-07

  • read individual rule files for detailed explanationsin 21 of 622, across 10 files
  • render final videoin 13 of 622, across 6 files
  • Use WAV PCM 16kHz mono audio formatin 12 of 622, across 3 files
  • Use this skill when dealing with Remotion codein 11 of 622, across 4 files
  • save generated audio to a WAV filein 11 of 622, across 4 files
  • handle conversion errors gracefullyin 10 of 622, across 6 files
  • add captions to videos alwaysin 10 of 622, across 4 files
  • generate music from text descriptions using MusicGenin 9 of 622, across 2 files
  • do not skip pipeline layersin 9 of 622, across 3 files
  • do not make one tool do everythingin 9 of 622, across 3 files
  • use azure document intelligence for complex pdfsin 9 of 622, across 4 files
  • never ask the user to paste their full API keyin 9 of 622, across 3 files

Said here and by no other author read

  • output style profile json matching version one spec
  • fill quantitative fields deterministically using pixels and audio
  • skip whisper if transcript json is provided
  • skip vision call when profile vision is zero
  • fire one vision round trip only when needs vision is non-empty
  • ship profile with empty vision object on vision failure

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.