- name
- video-skill
- description
- Run the video-skill pipeline to convert narrated videos into structured step data and enriched timeline-ready outputs. Use when a user asks to process a video into steps, run transcription/chunking/extraction/enrichment, debug provider connectivity, or generate markdown from extracted skills.
- metadata
- { "openclaw": { "requires": { "bins": ["uv", "ffmpeg", "python3"] } } }
Video Skill
Use this skill to run video-skill end-to-end or stage-by-stage.
First-time setup (no repo clone required)
Use one of these setup paths:
A) Run from local source repo (recommended while iterating):
cd /path/to/videoskill
uv sync --dev
cp config.example.json config.jsonThen run commands with uv run, for example:
uv run video-skill --helpThen run video-skill ... directly from your working directory.
Verify providers before processing:
video-skill config-validate --config config.json
video-skill providers-ping --config config.json --path /v1/modelsStandard workflow (recommended)
Run from your working directory where config.json and data paths are valid.
video-skill transcribe --video <video.mp4> --out <name>.whisper.json --config config.json
video-skill transcript-parse --input <name>.whisper.json --out <name>.segments.jsonl
video-skill transcript-chunk --segments <name>.segments.jsonl --out <name>.chunks.jsonl --window-s 120 --overlap-s 15
video-skill steps-extract --segments <name>.segments.jsonl --clips-manifest <clips>.jsonl --chunks <name>.chunks.jsonl --mode ai --config config.json --out <name>.steps.ai.jsonl
video-skill frames-extract --video <video.mp4> --steps <name>.steps.ai.jsonl --out-dir <frames_dir> --manifest-out <name>.frames_manifest.jsonl --sample-count 2
video-skill steps-enrich --steps <name>.steps.ai.jsonl --frames-manifest <name>.frames_manifest.jsonl --out <name>.steps.enriched.ai.jsonl --mode ai --config config.json
video-skill markdown-render --steps <name>.steps.enriched.ai.jsonl --out <name>.md --title "<Title>"Modes
--mode heuristic: deterministic, no model calls--mode ai-direct: VLM-centric enrichment--mode ai: reasoning + VLM orchestration (default for quality)
Prefer --mode ai unless user asks for debugging or reduced model usage.
Reliability and diagnostics
steps-enrich emits:
- per-step progress logs
- summary metrics:
parse_errors,transient_recovered,unresolved_final - detailed
*.errors.jsonlwhen any errors occur
If runs fail unexpectedly:
- re-run
providers-ping - inspect
*.errors.jsonlby stage (sampling_plan,vlm_judge,vlm_select_frames,vlm_signal_pass,reasoning_finalize) - verify endpoint DNS/host reachability
Validation gate before claiming success
Always run:
video-skill --helpUse make verify only when working from the source repo.