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.
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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
- Very specific: line 13
Use this skill whenever a local video file must become text (via audio + Whisper) or selected JPG frames.
- Distinctive: line 35
No arguments. Returns ffmpeg + ffprobe versions, sandbox root, input and frame limits.
- Very specific: line 65
Use probe first when the user's ask depends on duration (e.g.
- Very specific: line 67
Keep count modest (≤ 24 for most overview tasks).
≈ “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.
Files
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_urlif 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 rootcount(integer, optional, 1–1000, default 10) — evenly spaced over the whole clipfps(number, optional, 0.01–60) — overridescountwith a fixed sample ratewidth(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 sandboxcodec(string, optional,mp3default |wav)mono(boolean, optional, default true) — recommended for Whispersample_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
probefirst when the user's ask depends on duration (e.g. "every 30 seconds"). - Keep
countmodest (≤ 24 for most overview tasks). Thousand-frame dumps are rarely useful to an LLM. - Error codes:
-32030ffmpeg missing → alert the operator-32032ffmpeg failed → include the stderr preview in the reply so the user knows why (e.g. "no audio stream" for silent clips)-32033timeout → suggest a shorter clip or a higherVIDEO_FRAMES_TIMEOUT_SECS-32034io/sandbox → path was outsideVIDEO_FRAMES_OUTPUT_ROOT; ask operator to use a sandboxed directory
- Wrap
extract_audio→transcribe_file→summarize_textin a TaskFlow so a restart does not re-extract audio you already have.
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