Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zenstory-ai/oh-story-dsh --skill video-understandinggit clone --depth 1 https://github.com/zenstory-ai/oh-story-dshWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zenstory-ai/oh-story-dsh/video-understanding)<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-dsh/video-understanding"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/video-understanding/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-dsh/video-understanding"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/video-understanding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.01140 |
| Opus 5 | $0.00063 | $0.00570 |
| Sonnet 5 | $0.00025 | $0.00228 |
| Haiku 4.5 | $0.00013 | $0.00114 |
Grade A, and why
video-understanding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to video-understanding — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
1. 定位
本技能把源视频转成 Agent 与下游阶段可读取的理解索引。它的创作角色是素材观察员 / 场记，不是导演：
- 先观察，再解释；事实与推断分开。
- 除了“发生了什么”，还要让下游看见知识、权力、目标、关系或情绪在哪一刻变化。
- 标出由谁的 POV 承载变化、哪个反应或表演不可替代，以及哪里存在完整台词/动作的自然剪辑边界。
- 证据不足时保留不确定性，不制造戏剧结论。
2. 处理阶段
- 场景检测：写
scenes.json，包含切点、时长和废片段过滤结果。 - 抽帧：为视觉分析提取代表帧。
- ASR：通过
mimo-v2.5-asr写时间戳对白asr_result.json。 - 静音检测：写
silence_periods.json，标注安静窗口与has_speech。 - VLM 观察：写
vlm_analysis.json，包含场景描述、深层分析和frame_facts。 - 时间线融合与创作 brief：写
timeline_fusion.json、asr_writing_chunks.json和agent_narration_brief.md。
各阶段只有在输出产物与 provenance sidecar 同时匹配当前视频及影响结果的设置时才会复用；--force 强制重算。
3. 环境要求
# ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg
export MIMO_API_KEY=***
ASR 使用 mimo-v2.5-asr；VLM 使用 mimo-v2.5。--skip-asr 可跳过对白转写，但完整理解仍需要 MIMO_API_KEY 运行 VLM。--mimo-video-overview 可开启按场景块的视频概览。
若 work_dir/background_research.json 存在，本技能会把剧情梗概和角色名折入 VLM 上下文；--context 可补充一条简短提示。
下面的 scripts/... 均相对于本技能目录。若执行器从仓库根目录启动，请给脚本路径加上本技能的绝对目录。脚本不从其他技能目录读取文件；外部输入仅限命令显式传入的视频、参数与 work_dir 产物。
4. 运行命令
python3 scripts/understand.py <video> --work-dir <work_dir> \
[--context "节目名/角色名"] [--scene-threshold 0.1] [--skip-asr] [--mimo-video-overview] [--force]
5. 输出契约
| 文件 | 内容 |
|---|---|
scenes.json |
场景切点、起止时间与时长 |
asr_result.json |
[{start, end, text}] 时间戳对白 |
vlm_analysis.json |
逐场景描述、深层分析与 frame_facts |
silence_periods.json |
[{start, end, duration, has_speech}] 安静窗口 |
timeline_fusion.json |
VLM、ASR 与静音信息的统一时间线 |
asr_writing_chunks.json |
按句界和场景切分的 ASR 写作块 |
agent_narration_brief.md |
Agent 首先阅读的创作简报 |
后续写作阶段根据创作简报与索引制定方案并写 narration.json。
6. 参考资料
- 背景调研：
references/research-guide.md，产出background_research.json。 - JSON 结构：
references/data-schema.md。
7. 能力边界
- 不写解说词，也不做解说评分；只负责生成理解索引与创作简报。
- 不剪辑、不配音、不合成视频。
- 不编造信号无法支持的剧情；当 ASR / VLM 过薄时输出素材警告。
- 不发布、不调度，只向
work_dir写产物并停止。
What ships with it
25 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/data-schema.md 11 KB
- references/prompt-templates.md 1.7 KB
- references/research-guide.md 2.8 KB
- scripts/agent_brief.py 26 KB runs code
- scripts/agent_text.py 11 KB runs code
- scripts/asr.py 10 KB runs code
- scripts/brief_context.py 14 KB runs code
- scripts/brief_inputs.py 9.8 KB runs code
- scripts/brief_timeline.py 13 KB runs code
- scripts/brief.py 370 B runs code
- scripts/consolidate.py 24 KB runs code
- scripts/deslop_qc.py 10 KB runs code
- scripts/detect.py 21 KB runs code
- scripts/extract.py 2.5 KB runs code
- scripts/lib.py 24 KB runs code
- scripts/narration_lint.py 27 KB runs code
- scripts/speech_ownership.py 4.9 KB runs code
- scripts/storyboard.py 21 KB runs code
- scripts/timeline_fusion.py 4.2 KB runs code
- scripts/understand.py 192 B runs code
- scripts/understanding_brief.py 8.1 KB runs code
- scripts/understanding_cache.py 11 KB runs code
- scripts/understanding_runner.py 14 KB runs code
- scripts/understanding_storyboard.py 6.8 KB runs code
- scripts/vlm.py 29 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 76 lines · 126 tokens per session scan A 86c395f032d9
video-understanding is a skill published in the GitHub repository zenstory-ai/oh-story-dsh (338 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 1,140 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to video-understanding, differing in 0 lines, and is treated as a copy.
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