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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 video into steps, run transcription/chunking/extraction/enrichment, debug provider connectivity, or generate markdown from extracted skills.

作者: admin | 来源: ClawHub
源自
ClawHub
版本
V 0.1.2
安全检测
已通过
325
下载量
免费
免费
1
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概述
安装方式
版本历史

video-skill

# 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):** ```bash cd /path/to/videoskill uv sync --dev cp config.example.json config.json ``` Then run commands with `uv run`, for example: ```bash uv run video-skill --help ``` Then run `video-skill ...` directly from your working directory. Verify providers before processing: ```bash 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. ```bash 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: ```bash video-skill --help ``` Use `make verify` only when working from the source repo.

标签

skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

方式一:安装 SkillHub 和技能

帮我安装 SkillHub 和 video-skill-1776284823 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 video-skill-1776284823 技能

通过命令行安装

skillhub install video-skill-1776284823

下载

⬇ 下载 video-skill v0.1.2(免费)

文件大小: 51.33 KB | 发布时间: 2026-4-16 17:50

v0.1.2 最新 2026-4-16 17:50
fix: don't mention github

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