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lordmaculordmacu/nexo-rs/skills/video-frames/SKILL.md

Video Frames

Skill8 starsChanged 7 months agoApache-2.0View on GitHub

Extract frames or the audio track from a local video file via ffmpeg. Pairs with openai-whisper for audio→text.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use this skill whenever a local video file must become text (via audio + Whisper) or selected JPG frames.

    line 13
  2. Distinctive:

    No arguments. Returns ffmpeg + ffprobe versions, sandbox root, input and frame limits.

    line 35
  3. Very specific:

    Use probe first when the user's ask depends on duration (e.g.

    line 65
  4. Very specific:

    Keep count modest (≤ 24 for most overview tasks).

    line 67

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Video Frames

Use this skill whenever a local video file must become text (via audio + Whisper) or selected JPG frames. The extension wraps ffmpeg/ffprobe subprocesses with a path sandbox and per-call timeout.

Use when

  • "Transcribe this video"
  • "Grab 12 frames from this clip"
  • "What does this video say?" — chain through audio + whisper + summarize
  • Prepare material from bible_videos/ for agent kate to study

Do not use when

  • The file is not locally accessible (fetch first with fetch_url if allowed; operator decides the save path inside the sandbox)
  • The clip is > 500 MB — split or downscale upstream
  • You need real-time / streaming — this is batch only
  • You need OCR on frames — that's a separate pipeline

Tools

status

No arguments. Returns ffmpeg + ffprobe versions, sandbox root, input and frame limits.

probe

  • path (string, required) — local video file

Returns {duration_secs, format, streams} as JSON.

extract_frames

  • path (string, required)
  • output_dir (string, required) — must lie under the sandbox root
  • count (integer, optional, 1–1000, default 10) — evenly spaced over the whole clip
  • fps (number, optional, 0.01–60) — overrides count with a fixed sample rate
  • width (integer, optional, 16–4096) — resize keeping aspect ratio

Returns {count_written, frames: [path, ...]}.

extract_audio

  • path (string, required)
  • output_path (string, required) — full path under the sandbox
  • codec (string, optional, mp3 default | wav)
  • mono (boolean, optional, default true) — recommended for Whisper
  • sample_rate (integer, optional, 8000–48000, default 16000) — matches Whisper

Returns {output_path, bytes, codec, mono, sample_rate}.

Execution guidance

  • For audio destined for openai-whisper, prefer WAV mono 16 kHz — this is the Whisper-native layout and avoids re-encoding downstream.
  • Use probe first when the user's ask depends on duration (e.g. "every 30 seconds").
  • Keep count modest (≤ 24 for most overview tasks). Thousand-frame dumps are rarely useful to an LLM.
  • Error codes:
    • -32030 ffmpeg missing → alert the operator
    • -32032 ffmpeg failed → include the stderr preview in the reply so the user knows why (e.g. "no audio stream" for silent clips)
    • -32033 timeout → suggest a shorter clip or a higher VIDEO_FRAMES_TIMEOUT_SECS
    • -32034 io/sandbox → path was outside VIDEO_FRAMES_OUTPUT_ROOT; ask operator to use a sandboxed directory
  • Wrap extract_audio → transcribe_file → summarize_text in a TaskFlow so a restart does not re-extract audio you already have.

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