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 anymouschina/TapCanvas --skill tapcanvas-video-promptinggit clone --depth 1 https://github.com/anymouschina/TapCanvasWrote 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/anymouschina/tapcanvas/tapcanvas-video-prompting)<a href="https://agentmods.dev/skills/anymouschina/tapcanvas/tapcanvas-video-prompting"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-video-prompting/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/anymouschina/tapcanvas/tapcanvas-video-prompting"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-video-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00045 | $0.00368 |
| Opus 5 | $0.00023 | $0.00184 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
tapcanvas-video-prompting 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 12d 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.
What it actually says
TapCanvas Video Prompting
何时使用
- 用户要从关键帧、分镜或章节正文生成短视频
- 用户只要视频提示词，或要把视频节点落到画布
- 主代理需要判断是否应该拆段、是否需要节奏审查
输入证据
- 选中节点、关键帧、参考图、章节正文、连续性与素材工具结果
- 已验证的角色、环境、动作、对白、时长与禁止项
执行原则
- 先确认事实，再组织 video prompt
- 是否先补关键帧、是否直接进视频、是否拆段，属于主代理基于证据的判断
- 长度、节奏、转场密度属于方法论，不属于后端硬编码
- 若需要专门提示词产出，可按需调用
video_prompt_specialist - 若需要感知节奏审查，可按需调用
pacing_reviewer - 若缺关键事实，显式说明缺口，不要用模板句硬填
输出契约
- 若输出视频提示词，最小结果应包含：
prompt
- 若需要保留拍点拆解，可附带
storyBeatPlan，但真实生成只消费prompt - 若需要拆段建议，应明确指出拆分原因与建议边界
- 若证据不足，返回缺失事实，而不是伪造完整 prompt
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.
- 12d ago First seen · 35 lines · 45 tokens per session scan A e42552df61ba
tapcanvas-video-prompting is a skill published in the GitHub repository anymouschina/TapCanvas (603 stars, last pushed yesterday), licensed MIT. It adds 45 tokens to every session and 368 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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