tapcanvas-video-prompt-governance

tapcanvas-video-prompt-governance is a skill for Codex from anymouschina/TapCanvas. It costs 112 tokens per session (1,965 once invoked), scanned A, original, MIT.

A governance skill for maintaining the rules used to write and review video prompts. It examines proposed changes, records their evidence and scope, maps them to the right part of the shared contract, and plans checks for consistency.

In plain words
What is it for?
Use it when changing video-prompt skills, review methods, structured fields, provider rules, or supporting knowledge, and when auditing the resulting contract and evaluation plan.
Why use it?
It prevents different prompt-writing and review tools from using conflicting standards. It also keeps model-specific facts, optional advice, display formats, and core rules from being mixed together.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it when changing video-prompt skills, review methods, structured fields, provider rules, or supporting knowledge, and when auditing the resulting contract and evaluation plan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance
Install

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.

Any agent
npx skills add anymouschina/TapCanvas --skill tapcanvas-video-prompt-governance
Clone the repo
git clone --depth 1 https://github.com/anymouschina/TapCanvas

Made for: Codex.

Wrote 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.

agentmods badge for tapcanvas-video-prompt-governance

README.md
[![agentmods](https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance/github.svg)](https://agentmods.dev/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance)
Your own site
<a href="https://agentmods.dev/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance/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.

agentmods 80×15 button for tapcanvas-video-prompt-governance

Your own site · 80×15
<a href="https://agentmods.dev/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-video-prompt-governance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,965 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00112 $0.01965
Opus 5.5 $0.00045 $0.00786
Sonnet 5.5 $0.00022 $0.00393
Haiku 4.5 $0.00011 $0.00197

Measured 3d ago against content hash a4059ffef4f6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tapcanvas-video-prompt-governance 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 3d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/audit-contract-consistency.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

apps/agents-cli/skills/tapcanvas-video-prompt-governance/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TapCanvas 视频提示词合同治理

目标

维护一份权威 authoring contract,让 writer 与 reviewer 读取同一维度、同一 owner 和同一版本。领域 Skill 负责提供方法,不再复制“最终提示词标准”;Hono/Web 只做确定性协议校验,不接管语义质量。

权威合同固定在:

apps/agents-cli/skills/tapcanvas-video-prompt-writer/references/authoring-contract-v1.json

开始治理前完整读取权威合同 reference。修改正式 writer 或 reviewer 时,先读取两者骨架,再按本次变更涉及的 dimension、字段与复盘项精确读取对应标题单元;涉及整体生产范围时再按阶段读取 tapcanvas-video-workflow 的对应标题单元。禁止为了“完整”把三份大型 Skill 全文常驻到同一模型窗口。

知识拆解

把每条新知识先原子化为:

{
  "claimId": "稳定 ID",
  "source": "skill、用户反馈、真实 run 或外部方法来源",
  "evidence": "可追溯证据",
  "appliesTo": "跨题材、特定题材、特定模型或仅展示",
  "proposal": "它要求 authoring 改变什么"
}

再只允许归入以下一类:

  1. universal_contract_upgrade:跨题材、跨模型成立,现有维度无法完整表达;才允许升级合同版本。
  2. domain_extension:只适用于战斗、文戏、TVC、情绪、转场等领域;映射到现有 dimensionId,放 writer reference 或对应领域 Skill,不扩核心字段。
  3. provider_fact:时长、分辨率、引用数、原生音频等能力;只由实时 generationContract 提供,禁止写死进 authoring contract。
  4. presentation_projection:13 列表、UI 卡片、日志标签等展示形式;由 renderer/UI 投影,不改变 writer JSON。
  5. non_blocking_heuristic:案例经验、审美倾向、推荐词汇;可参与 agent 复盘,但不形成最低字数、评分、关键词或运行时门禁。
  6. conflict_or_reject:与用户事实、动态模型合同、付费幂等、真实资产或现行权威合同冲突;记录冲突,不静默合并。

映射与升级规则

  • 优先映射现有 dimensions[].id。只有无法表达跨题材必要信息时,才提议新增维度或字段。
  • writer 输出字段和 reviewer 检查项都引用同一个 dimensionId;reviewer 不重抄第二份规范,也不依赖退役 Skill。
  • 通用合同只定义信息维度、owner、字段落点和复盘问题,不规定题材答案。战斗力度、文戏克制、TVC 卖点等留在领域 extension。
  • 用户本轮事实与真实项目状态优先;动态 generationContract 管供应商硬边界;authoring contract 管创作信息结构;领域 extension 管方法;heuristic 只作建议。
  • 用户明确指定的节奏、密度、收束和结果边界优先于所有默认叙事模板。钩子、悬念、余波、呼吸点、高潮前抽空、收束或未决结果都必须有用户或权威来源依据;没有依据时,结构字段应明确表达“不适用”(例如可空字段使用 null),不得为了填满模板而杜撰。
  • 创作复盘发生在 writer 同一执行链并直接修订首稿。合同与 reviewer 都不得成为 Hono/Web 的语义质量门禁,也不得拦截、回滚或丢弃已受理媒体。
  • 版本升级只允许显式 major/minor/patch:删字段或改 owner 为 major,新增可选维度为 minor,措辞澄清与 consumer 修复为 patch。
  • temporalFrameTrack + temporalFrameCoverage 是 1.3.0 的通用合同升级:它把“信息密度”定义为每个不超过 1 秒时间窗的起帧、可见过渡、承帧与真实镜头映射,不把视频模型内部实现臆测成供应商事实,也不把该规则转换为字符数、固定 shot 数、关键词或 Hono/Web 语义评分门禁。

交付格式

先输出并审查变更提案,再修改文件:

{
  "baseContract": "tapcanvas/[email protected]",
  "claims": [],
  "mappings": [
    {
      "claimId": "claim-id",
      "classification": "domain_extension",
      "dimensionIds": ["action_causality_physics"],
      "targetFiles": [],
      "reason": "为何不升级核心合同"
    }
  ],
  "contractChanges": [],
  "writerChanges": [],
  "reviewerChanges": [],
  "conflicts": [],
  "evalCases": []
}

Read the full file on GitHub · 107 lines

Changes

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.

  1. 3d ago Changed a4059ffef4f6
  2. 1mo ago First seen · 107 lines · 112 tokens per session scan A c722e9f750b0

Subscribe to this mod's changes

tapcanvas-video-prompt-governance is a skill published in the GitHub repository anymouschina/TapCanvas (712 stars, last pushed 4d ago), licensed MIT. It adds 112 tokens to every session and 1,965 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-21.

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