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agents-team-book-metadata
// 启用 agents-team 协作,双代理完成小说逐章元数据抽取与完整性审校(parser + checker),并写入可续跑的记忆索引。
// 启用 agents-team 协作,双代理完成小说逐章元数据抽取与完整性审校(parser + checker),并写入可续跑的记忆索引。
商业级视觉资产与网页(详情页/落地页)的审美与UX视觉审查:输出P0/P1/P2问题清单、可落地的样式tokens与改稿建议;可结合截图、URL与源码进行定位与修改。
设计并构建各类 AI 智能体/助手。适用于用户: (1) 询问“创建 agent / 助手 / 智能体系统” (2) 想理解 agent 架构、agentic 模式或自治式 AI (3) 需要能力设计、子代理、规划或 skills 机制建议 (4) 询问 Claude Code、Cursor 等智能体内部实现 (5) 想为业务/研究/创作/运营等场景构建 agent 关键词:agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Enable general multi-agent team mode via spawn_agent/wait tools. Supports orchestrator, worker, reviewer, research, writer, and editor roles.
进行全面代码审查,覆盖安全、正确性、性能与可维护性;适用于用户要求 review、排查潜在 bug 或审计代码库。
Agents-CLI 认知记忆系统。用于管理长期记忆(core/episodic/semantic/procedural/vault)、可检索回忆、归档遗忘、以及多代理写入治理。
A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.
| name | agents-team-book-metadata |
| description | 启用 agents-team 协作,双代理完成小说逐章元数据抽取与完整性审校(parser + checker),并写入可续跑的记忆索引。 |
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。
agents-team。cognitive-memory。必须使用 agents team 工具并显式分工:
parser(优先 agent_type: research)checker(优先 agent_type: reviewer,必要时再补一个 editor)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.jsonindex.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" }
}
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。write_file 写入四个 metadata 文件。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memory_search 复查写入结果可检索(至少 1 次)。最终回复给用户时:
{
"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]
}
]
}
}
chapters.length 必须与输入章节数量一致。title、summary、keywords、coreConflict、characters、props、scenes、locations。name,不区分大小写)。characterGraph.nodes 至少覆盖 main/supporting。edges 去重、无自环,relation 只能是 coappear 或 conflict。unknown/null/待补充。当用户说“继续”时,先做:
read_file 读取 progress.json + index.json。memory_search 查询 BookSlug + metadata + characterGraph。