agents-team-book-metadata

agents-team-book-metadata is a skill for Claude Code, Codex from anymouschina/TapCanvas. It costs 46 tokens per session (1,252 once invoked), scanned A, original, MIT.

A Chinese-language workflow for extracting chapter-level metadata from novels with multiple collaborating agents. It requires one agent to extract information and another to check completeness, duplicates, and formatting.

In plain words
What is it for?
Use it to create structured chapter metadata, character relationships, progress files, and searchable memory records for a novel.
Why use it?
It provides a repeatable process for handling large books and saving progress so the work can continue after interruption. It also requires prerequisite memory and team skills.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create structured chapter metadata, character relationships, progress files, and searchable memory records for a novel.

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Install with agentmods
npx agentmods add skills/anymouschina/tapcanvas/agents-team-book-metadata
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 agents-team-book-metadata
Clone the repo
git clone --depth 1 https://github.com/anymouschina/TapCanvas

Made for: Claude Code, 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.

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README.md
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Your own site · 80×15
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Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,252 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 $0.00046 $0.01252
Opus 5 $0.00023 $0.00626
Sonnet 5 $0.00009 $0.00250
Haiku 4.5 $0.00005 $0.00125

Measured 11d ago against content hash a7290387e010, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

apps/agents-cli/skills/agents-team-book-metadata/SKILL.md · 129 lines

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

  1. 必须先加载 agents-team
  2. 必须先加载 cognitive-memory
  3. 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:

  1. parser(优先 agent_type: research
  • 负责逐章抽取元数据。
  • 输出必须覆盖每个 chapter。
  1. 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 BookSluglower-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

  1. spawn_agent 启动 orchestrator 或主代理自己先做输入切分。
  2. spawn_agent 启动 parser。
  3. wait 等 parser 完成,再把 parser 结果传给 checker。
  4. spawn_agentsend_input 启动 checker。
  5. wait 等待 checker 完成。
  6. 主代理汇总 checker 结果,形成最终 JSON。
  7. write_file 写入四个 metadata 文件。
  8. 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
  1. 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]
      }
    ]
  }
}

Read the full file on GitHub · 129 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. 11d ago First seen · 129 lines · 46 tokens per session scan A a7290387e010

Subscribe to this mod's changes

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.

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