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Video Skill

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 vide...
运行 video-skill 流程,将带旁白的视频转换为结构化的步骤数据和可立即用于时间线的富集输出。当用户要求处理视频时使用。
michaelgold
内容创作 clawhub v0.1.2 1 版本 100000 Key: 需要
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概述

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.json

Then run commands with uv run, for example:

uv run video-skill --help

Then 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/models

Standard 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.jsonl when any errors occur

If runs fail unexpectedly:

  1. re-run providers-ping
  2. inspect *.errors.jsonl by stage (sampling_plan, vlm_judge, vlm_select_frames, vlm_signal_pass, reasoning_finalize)
  3. verify endpoint DNS/host reachability

Validation gate before claiming success

Always run:

video-skill --help

Use make verify only when working from the source repo.

版本历史

共 1 个版本

  • v0.1.2 当前
    2026-03-30 02:10 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

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