tapcanvas-storyboard-expert

tapcanvas-storyboard-expert is a skill for Claude Code from anymouschina/TapCanvas. It costs 164 tokens per session (8,419 once invoked), scanned A, original, MIT.

A chapter-based storyboard and video-planning guide for turning stories or scripts into production-ready shot plans. It covers characters, locations, props, camera direction, dialogue, voice-over, subtitles, and short video timelines.

In plain words
What is it for?
Use it to break a chapter into shots, plan visual assets, write image or video prompts, create 15-second Seedance timelines, and manage dialogue, flashbacks, subtitles, and scene continuity.
Why use it?
It turns prose into a structured plan that different people and video tools can follow. It also helps keep scenes consistent between connected video segments.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Good fit Use it to break a chapter into shots, plan visual assets, write image or video prompts, create 15-second Seedance timelines, and manage dialogue, flashbacks, subtitles, and scene continuity.

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Install with agentmods
npx agentmods add skills/anymouschina/tapcanvas/tapcanvas-storyboard-expert
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-storyboard-expert
Clone the repo
git clone --depth 1 https://github.com/anymouschina/TapCanvas

Made for: Claude Code.

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-storyboard-expert

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/anymouschina/tapcanvas/tapcanvas-storyboard-expert"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/tapcanvas-storyboard-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 164 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,419 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00164 $0.08419
Opus 5.5 $0.00066 $0.03368
Sonnet 5.5 $0.00033 $0.01684
Haiku 4.5 $0.00016 $0.00842

Measured 20d ago against content hash f70ae11ad220, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tapcanvas-storyboard-expert 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 20d 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.

apps/agents-cli/skills/tapcanvas-storyboard-expert/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TapCanvas Storyboard Expert

场景空间身份、状态版本和灯光设计只使用 tapcanvas-scene-card。本 skill 消费已验真的 scene-card/v1 ID 与 scene-lighting/v1 连续性事实来做调度和镜头设计,不另写场景卡 prompt、固定光型或世界观模板。

会被拿取、交接、操作、损坏、变形或跨镜追踪的可复用道具只使用 tapcanvas-prop-card。本 skill 消费已验真的 prop-card/v1、prop-board/v1、prop-function/v1 和精确状态版本来设计持物关系、受力与连续性,不自行重写道具结构、固定视图模板或把单镜动作焊进 canonical 道具卡。

复现角色身份包(循环角色默认前置资产)

角色身份包的设计、生成、反趋同、状态派生和去模板化只使用 tapcanvas-character-card。本 skill 只消费已经落地并经 ID 验真的 character-card/v3 资产,不再维护平行的视图数量、体型、介质、面部 DNA、negative prompt 或母版裂变规则。

循环出场角色不能用头像、姿态图、表情图、群像图或某场戏剧照冒充 canonical identity anchor。镜头只继承身份卡授权的五官、身体结构、发型轮廓、肤色/材质、基准服装与身份物件,不继承卡面背景、排版、偶然姿势或机位;状态变化使用同名精确状态版本。

图片引用 ID 协议(硬切)

  • 主 agents、分镜 specialist 与最终提示词都不得读取、复制、输出图片存储 URL。画面身份只以资产名称、nodeId、assetId、assetRefId 表达。
  • 用 tapcanvas_flow_get / tapcanvas_flow_search 找画布图片节点,用素材工具找资产/版本 ID;需要确认真实可执行时调用 tapcanvas_image_refs_get({nodeIds?,assetIds?})。需要看图时调用 tapcanvas_analyze_image({nodeId}) 或 tapcanvas_analyze_image({assetId})。
  • 创建生图/视频节点时只写 referenceImageNodeIds / referenceAssetIds,不得写 referenceImages、styleImages、assetInputs[].url、imageUrl 或 lastFrameUrl。项目全局画风由服务端自动注入,不需要逐镜复制。
  • 任一引用 ID 无法解析时必须在付费提交前显式失败。禁止把 URL-only、planned metadata、节点连线或提示词里的“参考某图”当作真实媒体证据。

何时使用

当用户要你根据小说/剧本章节生成可执行提示词,并明确要求:

  • 分多个镜头输出
  • 每个镜头要素齐全
  • 可直接用于图像/视频模型生成

忠实快节奏分镜分支

用户要求“不改变剧情,只丰富镜头”、3 秒内切镜、长台词换机位、自然主义表演、多特写/反应镜/空镜、每集 90–120 秒或表格拍摄脚本时,必须读取并服从 references/忠实快节奏分镜.md。该分支锁定剧情事实、台词顺序、总时长和连续性,只允许丰富可见表演与镜头覆盖;它的时长、镜头数、声音和表格规则优先于本文件的通用章节镜头数量与 Seedance 节奏建议。

边界(重要):本 skill 产出文本分镜 / storyboard-director v1.2 JSON / 人读镜头说明 / 单张合成故事板图,主要服务 text 节点的故事板/脚本生成——这些产物不是供应商可执行视频提示词,也不进 orchestrate 出片。用户要求“可直接生成/写入视频节点/真实出片”时,必须把本 skill 的分镜事实交给 tapcanvas-video-workflow;其正式 video-prompt-writer 按唯一结构化 shots 合同编译,并在同一上下文使用 tapcanvas-video-reviewer 复盘后修订。旧八段文本 clipPrompt 路径已下线,服务端拒收;不得拿 v1.2 JSON、13 列展示表或本 skill 的 Seedance 示例直接绕过 writer 提交,也不得在本 skill 里自行串多镜出视频+拼接。

若用户明确选择“故事板做视频”或要求先看、编辑故事板再出片,主 agents 必须将这里的文本分镜进一步执行为画布上的真实 storyboardImage 资产;每个用于视频的板都要有 productionLayer=design_board、creationStage=beat_keyframe,且其节点 ID 必须经 tapcanvas_image_refs_get 验证为 ready,再由 tapcanvas-video-workflow 的 storyPlan.visualPreproduction={kind:"storyboard",requiredClipIndexes:[...],requiredAssetNodeIdsByClip:[...]} 和对应 clip 的 storyboardImageNodeId 精确绑定。若图片先于 BeatSheet/clip 计划生成,必须在节点 data 中同时写入同一视频的 clipRunId 与绝对 clipIndex,并写 storyboardScope="clip";服务端只按这些精确字段回填消费关系,不按标题、prompt、位置或连线猜测。每个命名人物、场景、道具或 VFX 都要由 agents 结构化列入该镜的真实资产输入清单,既作为故事板生图输入,也列进视频 clip 的 referenceImageNodeIds;不能扫描 prompt 猜名词,更不能创建了卡却不实际使用。文本 JSON、角色卡、场景卡和计划 metadata 都不能替代该视觉前置。

Read the full file on GitHub · 249 lines

Files

What ships with it

32 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.

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. 20d ago Changed · -265 lines · +75 tokens per session f70ae11ad220
  2. 1mo ago First seen · 514 lines · 89 tokens per session scan A 89de30d33de0

Subscribe to this mod's changes

tapcanvas-storyboard-expert is a skill published in the GitHub repository anymouschina/TapCanvas (686 stars, last pushed today), licensed MIT. It adds 164 tokens to every session and 8,419 once invoked, about $0.0007 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 2026-08-30.

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