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 agents-team-book-metadatagit 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/agents-team-book-metadata)<a href="https://agentmods.dev/skills/anymouschina/tapcanvas/agents-team-book-metadata"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/agents-team-book-metadata/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/agents-team-book-metadata"><img src="https://agentmods.dev/badge/skills/anymouschina/tapcanvas/agents-team-book-metadata.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 | $0.00046 | $0.01252 |
| Opus 5 | $0.00023 | $0.00626 |
| Sonnet 5 | $0.00009 | $0.00250 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
agents-team-book-metadata 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agents-team-book-metadata
目标:在小说章节级输入上,生成完整且可落库的章节元数据 JSON,并将关键结果写入 .agents/memory 形成可检索、可续跑的记忆资产。
必要前置
- 必须先加载
agents-team。 - 必须先加载
cognitive-memory。 - 若任一步骤缺失,直接报错并停止,不允许静默降级。
Team 角色
必须使用 agents team 工具并显式分工:
parser(优先agent_type: research)
- 负责逐章抽取元数据。
- 输出必须覆盖每个 chapter。
checker(优先agent_type: reviewer,必要时再补一个editor)
- 负责完整性检查、缺失补全、重复清理、字段标准化。
- 保证最终输出结构稳定并校验关系网可用性。
目录与记忆布局(强制)
先生成 BookSlug(lower-kebab-case),然后创建目录:
.agents/memory/books/<BookSlug>/metadata/progress.json.agents/memory/books/<BookSlug>/metadata/chapters.json.agents/memory/books/<BookSlug>/metadata/character-graph.json.agents/memory/books/<BookSlug>/metadata/index.json
index.json 至少包含:
{
"book": { "slug": "my-book", "title": "..." },
"updatedAt": "2026-02-27T00:00:00.000Z",
"chapters": { "total": 12, "path": "chapters.json" },
"characterGraph": { "path": "character-graph.json", "nodeCount": 10, "edgeCount": 18 },
"checkpoint": { "phase": "done", "next": "ready-for-storyboard" }
}
执行流程
spawn_agent启动orchestrator或主代理自己先做输入切分。spawn_agent启动 parser。wait等 parser 完成,再把 parser 结果传给 checker。spawn_agent或send_input启动 checker。wait等待 checker 完成。- 主代理汇总 checker 结果,形成最终 JSON。
- 用
write_file写入四个 metadata 文件。 - 用
memory_save写入长期记忆:
semantic: 角色关系网、角色主特征、章节核心冲突摘要procedural: 本次抽取规则、去重策略、命名策略episodic: 本次运行的输入范围、完成时间、异常与修复
- 用
memory_search复查写入结果可检索(至少 1 次)。
输出约束(严格)
最终回复给用户时:
- 只输出 JSON,不要 markdown、不要解释文本。
- 顶层必须包含:
{
"book": { "slug": "my-book", "title": "..." },
"chapters": [
{
"chapter": 1,
"title": "...",
"summary": "...",
"keywords": ["..."],
"coreConflict": "...",
"characters": [{ "name": "...", "description": "..." }],
"props": [{ "name": "...", "description": "..." }],
"scenes": [{ "name": "...", "description": "..." }],
"locations": [{ "name": "...", "description": "..." }]
}
],
"characterGraph": {
"nodes": [
{
"id": "role_a",
"name": "角色A",
"importance": "main|supporting|minor",
"firstChapter": 1,
"lastChapter": 20,
"chapterSpan": [1, 2],
"unlockChapter": 1
}
],
"edges": [
{
"sourceId": "role_a",
"targetId": "role_b",
"relation": "coappear|conflict",
"weight": 3,
"chapterHints": [1, 2]
}
]
}
}
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.
- 11d ago First seen · 129 lines · 46 tokens per session scan A a7290387e010
agents-team-book-metadata is a skill published in the GitHub repository anymouschina/TapCanvas (603 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,252 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.
Other skills, from other repositories
dramake
A production workflow for turning an idea, story, script, storyboard, or existing footage into a short AI drama or film. It covers planning, generation, voice, editing, and quality checks for formats such as vertical and horizontal video.
last30Days
Resolve "last30Days" to a concrete ISO date range relative to your run time — a rolling 30-day window ending today. Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a "last 30 days" / trailing-month task…
thisWeek
Resolve "thisWeek" to a concrete ISO date range relative to your run time — this week so far (Monday → today). Returns inclusive civil dates plus exact UTC instants so you have temporal context without computing dates by hand. Read-only: no writes, no network. Use before a week-to-date task (this week's activity…
short-drama-storyboard
A workflow for turning a Chinese short-drama script and its visual facts into a shot-by-shot storyboard with frozen starting-frame prompts.
vchart-development-assistant
A coding assistant for VChart, a JavaScript chart library used to build visualizations such as bar, line, and pie charts.
md-fetch-summarize
Fetch a URL and return a concise markdown summary of its content. Read-only: no files are written; the summary is returned as output only. Use when asked to "fetch and summarize", "summarize this URL", "what does this page say", or "get the content of ". Proactively suggest when the user pastes a URL and asks what it…