Agent skill

Generate Assets

by ArcReel in ArcReel/ArcReel

统一资产生成 skill:接受 --type=character|scene|prop|product,或不传自动扫所有 pending(缺 sheet)资源并按类型分发。当用户说“生成角色图”/“生成场景图”/“生成道具图”/“生成商品图”、想为新资产创建参考图、或有资产缺少 sheet 时使用。

AGPL-3.0Auto-check passedMedia & Creative

Install Generate Assets

skills CLI
$ npx skills add ArcReel/ArcReel --skill generate-assets -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ArcReel/ArcReel generate-assets --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-assets .claude/skills/generate-assets && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
generate-assets
GitHub stars
5.4k
Token cost
~902 tokens
SKILL.md length
229 words
Files
1
Skills in repo
18
Repo updated
First seen
Licence
AGPL-3.0

At a glance

统一资产生成 skill:接受 --type=character|scene|prop|product,或不传自动扫所有 pending(缺 sheet)资源并按类型分发。当用户说“生成角色图”/“生成场景图”/“生成道具图”/“生成商品图”、想为新资产创建参考图、或有资产缺少 sheet 时使用。

  • Works in 4 steps: 加载项目元数据 — 从 Artifact Manifest 找出资产图状态为… → 入队生成任务 — 只提交资产名;server 执行时按项目里存储的… → 审核检查点 — 展示每张资产图,用户可批准、要求重新生成,或要求编辑 → …
  • Tasks that involve AI video generation
  • SKILL.md covers 共同约定, 角色(character), 场景(scene) and 道具(prop), plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Generate Assets is an agent skill from ArcReel/ArcReel. 统一资产生成 skill:接受 --type=character|scene|prop|product,或不传自动扫所有 pending(缺 sheet)资源并按类型分发。当用户说“生成角色图”/“生成场景图”/“生成道具图”/“生成商品图”、想为新资产创建参考图、或有资产缺少 sheet 时使用。

Its SKILL.md is about 900 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering AI video generation. It works with Model Context Protocol. The repository describes itself as: AI Agent 驱动的开源可自部署视频工作台:将小说与剧本转为角色、场景、道具资产、分镜、视频和剪映草稿,支持跨镜头一致性、多供应商与费用追踪 | Self-hosted AI video workspace for stories, storyboards and short-form video production. The licence is AGPL-3.0.

When your agent uses it

  • Tasks that involve AI video generation

Example prompts

  • “/generate-assets”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. 加载项目元数据 — 从 Artifact Manifest 找出资产图状态为 missing 的资产
  2. 入队生成任务 — 只提交资产名;server 执行时按项目里存储的 description 渲染(lib.prompts.prompt_builders 注入布局 / 防崩 /…
  3. 审核检查点 — 展示每张资产图,用户可批准、要求重新生成,或要求编辑
  4. 更新 project.json — 更新 character_sheet / scene_sheet / prop_sheet / product_sheet 路径

What it can do on your machine

Read from SKILL.md and the folder at commit 08ab3b3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Generate Assets loads about 902 tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 229 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~902

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ArcReel/ArcReel at commit 08ab3b3, republished under its AGPL-3.0 licence (© ArcReel). 229 words, ~902 tokens.

Download SKILL.mdSave it as .claude/skills/generate-assets/SKILL.md (or your agent's skills folder).
name
generate-assets
description
统一资产生成 skill:接受 `--type=character|scene|prop|product`,或不传自动扫所有 pending(缺 sheet)资源并按类型分发。当用户说“生成角色图”/“生成场景图”/“生成道具图”/“生成商品图”、想为新资产创建参考图、或有资产缺少 *_sheet 时使用。

生成资产图

为项目的角色、场景、道具、商品创建资产图,保证整个视频中视觉元素的一致性。 图像供应商由项目设置选择(不锁定具体 backend)。

Prompt 编写原则详见 .claude/references/generation-modes.md 的"Prompt 语言"章节。

共同约定

  • 所有资产 description 用叙事式段落,而不是关键词列表。
  • 用户只需在 project.json 中维护 description;最终交给图像 backend 的完整 prompt (含布局 / 防崩短语 / 反向提示词)由 lib/prompts/prompt_builders.py 在 server 端拼好, WebUI 与 Skill 走同一份真相源。
  • Pending 判定:Artifact Manifest 中该资产图状态为 missing(含登记了文件却读不到);stale 产物复用,不计入待生成。
  • 衍生(本体/衍生)与本体同批:本体也待生成时,本体图成功后衍生才提交;本体失败则衍生报 generation_dependency_failed,不提交。

角色(character)

description 编写指南

用连贯段落描述外貌、服装、气质,包含年龄、体态、面部特征、服饰细节。

示例:

"二十出头的女子,身材纤细,鹅蛋脸上有一双清澈的杏眼,柳叶眉微蹙时带着几分忧郁。身着淡青色绣花罗裙,腰间系着同色丝带,显得端庄而不失灵动。"

输出布局

横版 16:9 三视图,纯白背景:正面 / 正侧(90° 侧视图)/ 背面水平排列。 三个面板中角色面部、发型、服装、配饰需保持完全一致。

用户填写 description 时只需关心外貌 / 服装等内容;布局由 builder 注入。


场景(scene)

description 编写指南

用连贯段落描述形态、光线、氛围,突出能跨场景识别的独特特征。

示例:

"村口的百年老槐树,树干粗壮需三人合抱,树皮龟裂沧桑。主干上有一道明显的雷击焦痕,从顶部蜿蜒而下。树冠茂密,夏日里洒下斑驳的树影。"

输出布局

横版 16:9 单张环境全景建立镜头。


道具(prop)

description 编写指南

用连贯段落描述形态、质感、细节,突出能跨场景识别的独特特征。

示例:

"一块翠绿色的祖传玉佩,约拇指大小,玉质温润透亮。表面雕刻着精致的莲花纹样,花瓣层层舒展。玉佩上系着一根红色丝绳,打着传统的中国结。"

输出布局

横版 16:9 单张道具资产图,纯净浅灰背景。


商品(product)

description 编写指南

用连贯段落描述商品外观、品牌文字、配色、材质、比例与结构。

输出布局

横版 16:9 单张商品资产图,纯净浅灰背景、均匀棚拍布光。


工具调用

入队走 MCP 工具:

操作工具
列出所有/某类 pendingmcp__arcreel__list_pending_assets({"type": "character"})(type 可省略)
列出本集引用的 pendingmcp__arcreel__list_pending_assets({"episode_id": 3})
生成本集引用的全部 pending(含商品与衍生)mcp__arcreel__generate_assets({"episode_id": 3})
生成所有 pending(四类各一轮)mcp__arcreel__generate_assets({})
生成某类全部 pendingmcp__arcreel__generate_assets({"type": "character"})
生成指定多个mcp__arcreel__generate_assets({"type": "prop", "names": ["玉佩", "密信"]})
生成单个mcp__arcreel__generate_assets({"type": "scene", "names": ["村口老槐树"]})
生成单个商品mcp__arcreel__generate_assets({"type": "product", "names": ["保温杯"]})
生成衍生mcp__arcreel__generate_assets({"type": "character", "names": ["张三/战损"]})

episode_id 不与 type / names 同用;「本集引用了哪些资产」由服务端计算,与 Web 集层同一份名单。

结果按 requested / succeeded / failed / blocked / skipped 逐 ID 返回,ID 形如 character/张三(衍生为 character/张三/战损); 已失效但可复用的旧图进入 skipped,不会自动重生; 按每一项自带的 problem.code 与 problem.action 决定下一步,不要解析文本。 结构详见 .claude/references/generation-results.md。

工作流程

  1. 加载项目元数据 — 从 Artifact Manifest 找出资产图状态为 missing 的资产
  2. 入队生成任务 — 只提交资产名;server 执行时按项目里存储的 description 渲染(lib.prompts.prompt_builders 注入布局 / 防崩 / 反向)。声明了原图却读不到时生成被拒(asset_original_missing),让用户重新上传原图或清掉原图字段
  3. 审核检查点 — 展示每张资产图,用户可批准、要求重新生成,或要求编辑
  4. 更新 project.json — 更新 character_sheet / scene_sheet / prop_sheet / product_sheet 路径

审核检查点:编辑 vs 重新生成

用户对资产图提意见时先判断诉求类型,选错路径会推翻已满意的部分或丢掉预期外的改动:

  • 只想改局部(换发色、去掉杂物、调整光线氛围等),且构图和整体设计满意 → 用 mcp__arcreel__edit_images({"resource_type": "character", "edits": [{"id": "张三", "instruction": "把头发改成红色"}]}) 保底图微调,一次可对同类型多个资产批量下发
  • 想推翻构图/整体设计重来,或本来就要改 description(进而改变后续按 description 重新生成的结果)→ 用 generate_assets 按更新后的 description 重新生成整图
  • 编辑不会更新 description / prompt——编辑后再触发 generate_assets 仍按原 description 重画,编辑效果只能从版本历史找回

质量检查

  • 角色:三个面板(正面 / 正侧 / 背面)的面部、发型、服装、配饰完全一致
  • 场景:整体构图和标志性特征突出、光线氛围合适
  • 道具:主体清晰、细节符合描述、特殊纹理清晰可见
  • 商品:参考图中的品牌、文字、配色、材质、比例与结构保持一致,不保留手部、模特及其他出镜人物

© ArcReel, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent_runtime_profile/.claude/skills/generate-assets of ArcReel/ArcReel.

Open the folder on GitHubat commit 08ab3b3

Compare with similar skills

Generate Assets next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Generate Assets compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Generate Assets this skillArcReel/ArcReel5.4k—~902Automated safety check: PassAGPL-3.0
Clipmivo VideoBarneyD66/clipmivo-tools142—~945Automated safety check: PassMIT
ComfyUI Local DriverSlavaSexton/ComfyUI-Agent-Kit105—~12kAutomated safety check: PassApache-2.0
Image Edit Workbenchhenjicc/Henji-AI254—~502Automated safety check: PassApache-2.0
Checkffroliva/gflow-cli266—~2.6kAutomated safety check: PassMIT
OrchestrationOrkas-AI/Orkas-VideoStudio498—~3.4kAutomated safety check: PassMIT

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Questions about Generate Assets

What does Generate Assets do?

统一资产生成 skill:接受 --type=character|scene|prop|product,或不传自动扫所有 pending(缺 sheet)资源并按类型分发。当用户说“生成角色图”/“生成场景图”/“生成道具图”/“生成商品图”、想为新资产创建参考图、或有资产缺少 sheet 时使用。. Generate Assets is an agent skill from ArcReel/ArcReel.

When should I use Generate Assets?

Generate Assets fits situations like: tasks that involve AI video generation.

How do I install Generate Assets in Claude Code?

Run `npx skills add ArcReel/ArcReel --skill generate-assets -a claude-code`. Or copy the skill folder (agent_runtime_profile/.claude/skills/generate-assets in ArcReel/ArcReel) into .claude/skills/generate-assets in your project. Claude Code loads it when a task matches its description.

How do I install Generate Assets in Codex?

Run `npx skills add ArcReel/ArcReel --skill generate-assets -a codex`. Or copy the skill folder (agent_runtime_profile/.claude/skills/generate-assets in ArcReel/ArcReel) into .agents/skills/generate-assets in your project. Codex loads it when a task matches its description.

Can I use Generate Assets in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArcReel/ArcReel --skill generate-assets -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generate-assets, .gemini/skills/generate-assets, .github/skills/generate-assets and .opencode/skills/generate-assets in your project.

What does Generate Assets need to run?

SKILL.md names no scripts, command-line tools or credentials: Generate Assets is instructions for the agent only.

Does Generate Assets access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Generate Assets safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Generate Assets use?

Generate Assets is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Generate Assets use?

About 902 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Generate Assets?

Skills that share tags, products or a category with Generate Assets: Clipmivo Video (BarneyD66/clipmivo-tools, 142 stars), ComfyUI Local Driver (SlavaSexton/ComfyUI-Agent-Kit, 105 stars), Image Edit Workbench (henjicc/Henji-AI, 254 stars) and Check (ffroliva/gflow-cli, 266 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Generate Assets?

ArcReel (a GitHub organization) maintains it in ArcReel/ArcReel, which has 5,399 GitHub stars. The repository holds 18 skills in this directory. The repository was last updated on October 9, 2026.

Source: ArcReel/ArcReel on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.