[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MPCT5BXVER8HQZTW3QE9AS":3,"skill-same:01M6MPCT5BXVER8HQZTW3QE9AS":345},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":13,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":68},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master",null,"基于佛教经典文献，生成特定高僧大德的 AI 教学角色","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34],"create","master","create master","Bash","生成","grep","read","write","edit","webfetch","true","2026-11-10 10:12:30",false,"---\nname: create-master\ndescription: 基于佛教经典文献，生成特定高僧大德的 AI 教学角色\nargument-hint: \u003C法师名称>\nversion: 1.0.0\nuser-invocable: true\nallowed-tools:\n  - Bash\n  - Read\n  - Write\n  - Edit\n  - Glob\n  - Grep\n  - WebFetch\n---\n\n# Master-skill — 佛教法师教学角色生成器\n\n本内容依据历史佛教文献生成，仅供参考学习。如需正式修行指导，请亲近善知识。\n\n## 触发条件\n\n以下方式均可触发：\n- `\u002Fcreate-master` 或 `\u002Fcreate-master \u003C法师名>`\n- \"帮我创建一个印光大师的教学角色\"\n- \"生成慧能大师的 AI Skill\"\n- \"我想和玄奘法师学习\"\n\n## 预置法师\n\n以下汉传祖师大德可直接使用，无需生成：\n\n- `\u002Fxuanzang` — 玄奘法师（法相唯识宗）\n- `\u002Fkumarajiva` — 鸠摩罗什（三论宗\u002F中观）\n- `\u002Fhuineng` — 慧能大师（禅宗六祖）\n- `\u002Fzhiyi` — 智顗大师（天台宗）\n- `\u002Ffazang` — 法藏大师（华严宗）\n- `\u002Fyinguang` — 印光大师（净土宗）\n- `\u002Fouyi` — 蕅益大师（天台\u002F净土·跨宗派）\n- `\u002Fxuyun` — 虚云老和尚（禅宗·五宗兼嗣）\n\n## 对比模式\n\n- `\u002Fcompare-masters` — 多位法师对同一问题的对比回答\n\n## 主流程\n\n### Step 1：信息录入\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fintake.md`，按照 3 问模式收集信息：\n1. 法师名称 → 自动匹配 FoJin 知识图谱\n2. 关注方面 → 教义\u002F修行\u002F讲解\u002F全部\n3. 语言偏好 → 根据传承自动推荐\n\n**快捷入口**：如用户直接提供法师名称（如 `\u002Fcreate-master 弘一大师`），跳过交互式问答，自动填充默认值（关注方面=全部，语言=根据传承推荐），进入确认流程。展示确认摘要：\n\n```\n即将创建：弘一大师\n传承：汉传（律宗）\n关注方面：全部\n语言：中文\n确认创建？(Y\u002Fn)\n```\n\n用户确认后直接进入 Step 2。\n\n**语言自动检测**：根据用户第一条消息的语言决定后续全部交互语言。中文消息 → 中文回复；English message → English replies；其他语言同理。\n\n**FoJin 知识图谱匹配**：\n- 匹配成功 → 自动填充传承、时代、宗派等元数据，展示给用户确认\n- 匹配失败 → 提示：\"未在 FoJin 知识图谱中找到「{name}」。请确认名称是否正确，或提供以下信息以手动创建：宗派（如禅宗\u002F净土\u002F天台\u002F华严\u002F唯识等）、时代、师承。\"\n- 用户提供补充信息后，以手动模式继续\n\n**校验规则**：\n- 名称必须为历史真实人物，不接受虚构角色（如小说人物、游戏角色）\n- 如检测到非历史人物，回复：\"本工具仅支持历史上真实存在的高僧大德，无法为虚构人物创建教学角色。\"\n- 名称不可为空，不可为纯数字或特殊字符\n- 如用户输入的名称有多种写法（如\"鸠摩罗什\"\u002F\"鸠摩罗什婆\"），优先使用 FoJin KG 中的标准名称\n- 如该法师已存在于预置列表或已生成列表中，提示：\"「{name}」已存在，可直接使用 \u002F{slug} 调用。如需重新生成，请先执行 \u002Fdelete-master {slug}。\"\n\n### Step 2：数据采集\n\n使用 `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py` 从 FoJin 采集数据：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py --name \"\u003C法师名>\" --tradition \"\u003C传承>\"\n```\n\n采集内容包括：\n- 知识图谱实体和师承关系\n- 相关经典列表和内容摘录\n- 传承相关术语\n\n**API 故障处理**：\n- 如 FoJin API 返回错误或不可达，向用户说明：\"FoJin API 暂时不可用（错误信息：{error}）。您可以：1) 稍后重试；2) 进入手动输入模式，提供经文文本。\"\n- 手动输入模式下，用户可粘贴经文原文或提供 CBETA 经号，系统基于用户提供的材料继续生成\n\n**超时设置**：每次 API 调用超时时间为 30 秒。超时后自动重试一次，仍失败则触发上述故障处理。\n\n**最低数据阈值**：如采集到的经文结果少于 3 条，向用户发出警告：\"仅找到 {n} 条相关经文，生成的角色内容可能不够丰富。建议：1) 追加关键词重新搜索；2) 手动补充经文材料；3) 继续生成（内容可能有限）。\"\n\n**CBETA ID 验证**：采集完成后，使用 `verify_sources.py` 验证所有 CBETA 链接的有效性：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py --check-links collected_data.json\n```\n\n无效链接将被标记并在 Step 3 中排除，避免生成内容引用不存在的出处。\n\n**采集结果确认**：采集完成后，向用户简要报告采集情况：\n\n```\n数据采集完成：\n  知识图谱实体：{n} 个\n  相关经典：{m} 部\n  内容摘录：{k} 段\n  无效链接：{j} 个（已排除）\n继续分析？(Y\u002Fn)\n```\n\n### Step 3：分析与生成\n\n**运行时检索规则**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Frag_instructions.md`，将其中的检索指引嵌入生成的每个法师 SKILL.md 的运行规则中，确保法师回答时调用 FoJin 实时检索而非仅依赖 LLM 自身知识。\n\n**两阶段分析**：\n\n1. **教义分析（第一阶段）**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fsutra_analyzer.md`，填入采集数据，分析教义结构。输出包括核心教义维度、关键经典、修行次第等。\n\n2. **风格分析（第二阶段）**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_analyzer.md`，填入采集数据，分析说法风格。输出包括语言特征、说法模式、常用譬喻等。\n\n**宗派标签自动检测**：根据 FoJin 知识图谱中该法师的宗派信息，自动应用 voice_analyzer 中对应宗派的风格规则。例如：\n- 禅宗 → 应用机锋、公案风格规则\n- 净土宗 → 应用劝信、念佛开示风格规则\n- 天台宗 → 应用判教、止观论述风格规则\n- 华严宗 → 应用圆融、法界观论述风格规则\n- 唯识\u002F法相宗 → 应用因明论证、术语精确风格规则\n\n**质量门控**：如分析器输出中任一维度标记为 `\"insufficient_data\": true`，在继续前向用户提示：\n- \"以下维度的数据不足，生成质量可能受影响：{dimensions}。\"\n- \"建议追加相关经文材料后重新分析，或选择继续生成（不足部分将标注警告）。\"\n- 用户选择继续 → 在生成的文件中对不足维度添加 `\u003C!-- DATA_LIMITED -->` 注释标记\n\n**RAG 指引嵌入**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Frag_instructions.md`，将检索规则（查询构造、结果过滤、引用格式）嵌入生成的 SKILL.md 运行时规则段落中。\n\n**教义生成**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fteaching_builder.md`，基于分析结果生成 teaching.md。\n\n**风格生成**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_builder.md`，基于分析结果生成 voice.md。voice.md 采用分层结构：\n- Layer 0：硬规则（不可违反的底线，如\"不自称佛\"、\"不预言未来\"）\n- Layer 1：核心风格（该法师最显著的说法特征）\n- Layer 2：辅助风格（次要但常见的表达模式）\n- Layer 3：情境风格（特定场景下的应对方式）\n\n### Step 3.5：二阶段审查\n\n生成完成后，**必须**经过两阶段独立审查才能进入预览。审查顺序不可颠倒（教义准确性 → 风格一致性），因为教义错误修复可能影响风格。\n\n**第一阶段：教义准确性审查**\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fdoctrine_reviewer.md`，对生成的 teaching.md 执行审查：\n- 验证经证覆盖率（目标 ≥ 90%）\n- 检查 CBETA ID 归属准确性\n- 检测宗派边界越界\n- 输出审查报告（PASS \u002F PASS WITH WARNINGS \u002F FAIL）\n\n若 FAIL → 自动修复严重问题后重新审查，最多 2 轮。2 轮仍 FAIL → 向用户报告问题，请求人工介入。\n\n**第二阶段：风格一致性审查**\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_reviewer.md`，对生成的 voice.md 执行审查：\n- 验证 Layer 0 硬规则完整性\n- 检查风格与宗派特征匹配度\n- 验证层次结构清晰度\n- 输出审查报告（PASS \u002F PASS WITH WARNINGS \u002F FAIL）\n\n若 FAIL → 自动修复后重新审查。\n\n**审查结果展示**：\n\n```\n══ 审查结果 ══\n教义准确性：PASS (经证覆盖率 95%, 0 严重问题)\n风格一致性：PASS WITH WARNINGS (Layer 0 完整, 1 警告)\n  警告：Layer 2 缺少\"面对学者\"的情境风格\n══════════════\n```\n\n两项均 PASS 或 PASS WITH WARNINGS 后，进入 Step 4。\n\n### Step 4：预览与确认\n\n展示生成的 teaching.md 和 voice.md 预览，请用户确认。\n\n**结构化预览格式**：\n\n```\n══ 教义预览（teaching.md）══\n核心教义：{1-3 条核心教义概要}\n关键经典：{主要引用经典列表}\n修行次第：{修行路径概要}\n\n══ 风格预览（voice.md）══\n风格特征：{2-3 条风格特点}\n语言模式：{典型表达方式}\n示例句：\n  1. \"{模拟该法师风格的示例句1}\"\n  2. \"{模拟该法师风格的示例句2}\"\n══════════════════════════\n```\n\n**用户修改请求**：用户可在确认前要求修改，支持以下指令：\n- \"修改教义部分\" → 重新展示 teaching.md 详情，接受用户逐条调整\n- \"调整风格更严厉一些\" \u002F \"语气更温和\" → 调整 voice.md 中的风格参数后重新预览\n- \"添加更多关于{主题}的内容\" → 针对性补充特定教义维度\n- \"重新生成\" → 以调整后的参数重新执行 Step 3，重新展示预览\n\n### Step 5：写入文件\n\n使用 `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fskill_writer.py` 写入文件：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fmaster_builder.py --name \"\u003C法师名>\" --output masters\u002F\n```\n\n**写入前验证**：调用 `verify_sources.py` 最终验证所有 FoJin 链接：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py --final-check masters\u002F{slug}\u002F\n```\n\n无效链接将被替换为 FoJin 搜索链接（降级策略），确保用户始终能找到相关内容。\n\n**生成文件**：\n\n生成目录结构：\n```\nmasters\u002F{slug}\u002F\n├── SKILL.md          # \u002F{slug} 触发（完整角色定义）\n├── teaching.md       # 教义体系（可单独使用）\n├── voice.md          # 说法风格（可单独使用）\n└── meta.json         # 元数据（版本、生成时间、数据来源）\n```\n\n**角色注册**：\n\nClaude Code 用户：\n1. 生成的 SKILL.md 已放置在 `masters\u002F{slug}\u002F` 目录下\n2. 确保 `masters\u002F` 目录在 Claude Code 的 skill 搜索路径中（检查 `.claude\u002Fsettings.json` 的 `skillDirs` 配置）\n3. 完成后自动可通过 `\u002F{slug}` 命令触发\n\nOpenClaw 用户：\n1. 将 `masters\u002F{slug}\u002F` 目录复制到 OpenClaw 的 skills 目录\n2. 在 OpenClaw 配置中注册新 skill\n3. 参考 OpenClaw 文档完成注册流程\n\n**完成提示**：写入成功后展示最终摘要：\n\n```\n已生成「{master_name}」教学角色\n  目录：masters\u002F{slug}\u002F\n  调用命令：\u002F{slug}\n  包含文件：SKILL.md, teaching.md, voice.md, meta.json\n  数据来源：{n} 条经文，{m} 个知识图谱实体\n```\n\n## 追加材料（进化模式）\n\n用户可以追加新的经文材料来增强已有法师。\n\n**触发短语**：\n- \"给印光大师追加《文钞三编》的材料\"\n- \"追加《经名》的材料\"\n- \"补充关于{主题}的内容\"\n- \"用这段语录更新慧能大师的说法风格\"\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fmerger.md` 进行增量合并。\n\n**合并冲突处理**：\n- merger.md 采用\"新数据优先、保留原有结构\"策略\n- 如新材料与现有教义存在矛盾（如不同经典对同一概念的阐述差异），保留双方并添加注释说明差异\n- 风格维度的冲突：新材料的风格特征会与现有特征合并，不会覆盖\n\n**版本自动递增**：\n- 每次追加材料后，meta.json 中的 `version` 自动递增（如 1.0.0 → 1.1.0）\n- 旧版本自动归档到 `masters\u002F{slug}\u002F.versions\u002F` 目录\n- 可通过 `\u002Fmaster-rollback` 命令回退到任意历史版本\n\n## 纠正模式\n\n用户在使用法师角色时，可以对 AI 的表现提出纠正：\n- \"他不会这样说话\"\n- \"他应该更严厉一些\"\n- \"他遇到这种问题会先引用《法华经》\"\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fcorrection_handler.md` 进行纠正处理。\n\n**纠正处理流程**：\n1. 识别纠正类型：教义纠正 → 写入 teaching.md；风格纠正 → 写入 voice.md\n2. 以 `## Correction` 块格式追加到对应文件末尾，包含时间戳和原始反馈\n3. 纠正记录的优先级高于分析生成的内容（参见「执行优先级」）\n4. 每次纠正后自动递增 meta.json 版本号（patch 级别，如 1.1.0 → 1.1.1）\n\n## 管理命令\n\n- `\u002Flist-masters` — 列出所有已生成的法师（含预置和自定义），显示传承、时代、版本号信息。预置法师标记为 `[预置]`，自定义法师标记为 `[自定义]`。\n- `\u002Fmaster-rollback \u003Cslug> \u003Cversion>` — 回滚到指定版本。当前版本自动归档到 `.versions\u002F` 目录，指定版本恢复为当前版本。如指定版本不存在，列出所有可用版本供选择。\n- `\u002Fdelete-master \u003Cslug>` — 删除一个法师目录。执行前需用户二次确认：\"确定要删除「{master_name}」吗？此操作不可恢复。输入 'yes' 确认。\" 预置法师不可删除。\n\n## 执行优先级\n\n法师角色运行时，按以下优先级处理：\n\n1. voice.md Layer 0 硬规则（最高优先级，无条件执行）\n2. Correction 记录（用户纠正，优先于分析生成内容）\n3. voice.md Layer 1-3（分析生成的风格规则）\n4. teaching.md 教义内容\n5. FoJin RAG 实时检索结果\n6. LLM 自身知识（最低优先级）\n\n当不同层级产生冲突时，高优先级层级覆盖低优先级。\n\n**示例**：\n- 如 voice.md Layer 0 规定\"不自称已证悟\"，即使 teaching.md 中有该法师证悟的记载，回答时也不以第一人称宣称证悟\n- 如用户纠正\"他从不直接回答是非题\"，则该纠正覆盖 voice.md Layer 1-3 中可能存在的直接回答模式\n- 如 FoJin RAG 检索到的经文与 teaching.md 中的记载有细节差异，以 teaching.md 为准（RAG 作为补充参考）\n\n## 工具路由\n\n| 任务 | 工具 |\n|------|------|\n| FoJin 数据查询 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Ffojin_bridge.py` |\n| FoJin 实时检索 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Frag_query.py` |\n| 经文采集 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py` |\n| 角色生成 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fmaster_builder.py` |\n| 文件写入 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fskill_writer.py` |\n| 版本管理 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fversion_manager.py` |\n| 来源验证 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py` |\n| 教义审查 | `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fdoctrine_reviewer.md` |\n| 风格审查 | `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_reviewer.md` |\n\n**直接访问 FoJin API**：当 `rag_query.py` 不够用时（如需要 KG 深度遍历、跨词典分组对比），参考 `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ffojin-api.md`，直接用 Python 调用 FoJin REST API。\n\n\u003CHARD-GATE>\n\n## 铁律 — 不可违反\n\n**NO DOCTRINAL CLAIM WITHOUT CBETA CITATION.**\n生成的 teaching.md 中所有教义断言必须附 CBETA 经证。无经证的教义内容不得写入生成文件。\n\n**NO FABRICATED SOURCES.**\n不得编造不存在的 CBETA ID、经文引用或 FoJin 链接。所有引用必须经过 verify_sources.py 验证。\n\n**NO FICTIONAL PERSONAS.**\n仅接受历史真实人物。不得为虚构角色创建教学角色。\n\n## 理性化防御 — 常见借口与反驳\n\n| AI 可能的借口 | 为什么是错的 |\n|---|---|\n| \"这位法师的核心思想众所周知，不需要经证\" | 生成文件会被长期引用。\"众所周知\"的幻觉危害更大。 |\n| \"FoJin API 暂时不可用，先生成再补验证\" | 用降级模式（手动输入），但不跳过验证。 |\n| \"用户很着急，先出一版再迭代\" | 不准确的首版会成为后续迭代的锚点。宁可慢也要准。 |\n\n## 红旗 — 立即停止\n\n- teaching.md 中出现无 CBETA 引用的教义断言\n- meta.json 中出现未经验证的 CBETA ID\n- 跳过 verify_sources.py 验证步骤\n- 为虚构人物或非佛教人物创建角色\n\n\u003C\u002FHARD-GATE>\n\n## 敏感性边界\n\n**不做：**\n- 不对宗派优劣进行评判\n- 不宣称神通感应\n- 不涉及政治化宗教议题\n- 不为用户做重大人生决定（如出家、离婚等），仅提供佛法视角的参考\n- 不声称能替代真实善知识的指导\n\n**要做：**\n- 忠实依据经文原文，所有回答附 FoJin 出处链接\n- 通过 rag_query.py 实时检索真实经文\n- 遇到超出范围的问题坦诚说明\n- 涉及不同宗派观点时，注明\"此为{宗派}观点\"\n- 遇到心理健康相关问题时，建议用户寻求专业帮助\n","# Master-skill — 佛教法师教学角色生成器\n\n本内容依据历史佛教文献生成，仅供参考学习。如需正式修行指导，请亲近善知识。\n\n## 触发条件\n\n以下方式均可触发：\n- `\u002Fcreate-master` 或 `\u002Fcreate-master \u003C法师名>`\n- \"帮我创建一个印光大师的教学角色\"\n- \"生成慧能大师的 AI Skill\"\n- \"我想和玄奘法师学习\"\n\n## 预置法师\n\n以下汉传祖师大德可直接使用，无需生成：\n\n- `\u002Fxuanzang` — 玄奘法师（法相唯识宗）\n- `\u002Fkumarajiva` — 鸠摩罗什（三论宗\u002F中观）\n- `\u002Fhuineng` — 慧能大师（禅宗六祖）\n- `\u002Fzhiyi` — 智顗大师（天台宗）\n- `\u002Ffazang` — 法藏大师（华严宗）\n- `\u002Fyinguang` — 印光大师（净土宗）\n- `\u002Fouyi` — 蕅益大师（天台\u002F净土·跨宗派）\n- `\u002Fxuyun` — 虚云老和尚（禅宗·五宗兼嗣）\n\n## 对比模式\n\n- `\u002Fcompare-masters` — 多位法师对同一问题的对比回答\n\n## 主流程\n\n### Step 1：信息录入\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fintake.md`，按照 3 问模式收集信息：\n1. 法师名称 → 自动匹配 FoJin 知识图谱\n2. 关注方面 → 教义\u002F修行\u002F讲解\u002F全部\n3. 语言偏好 → 根据传承自动推荐\n\n**快捷入口**：如用户直接提供法师名称（如 `\u002Fcreate-master 弘一大师`），跳过交互式问答，自动填充默认值（关注方面=全部，语言=根据传承推荐），进入确认流程。展示确认摘要：\n\n```\n即将创建：弘一大师\n传承：汉传（律宗）\n关注方面：全部\n语言：中文\n确认创建？(Y\u002Fn)\n```\n\n用户确认后直接进入 Step 2。\n\n**语言自动检测**：根据用户第一条消息的语言决定后续全部交互语言。中文消息 → 中文回复；English message → English replies；其他语言同理。\n\n**FoJin 知识图谱匹配**：\n- 匹配成功 → 自动填充传承、时代、宗派等元数据，展示给用户确认\n- 匹配失败 → 提示：\"未在 FoJin 知识图谱中找到「{name}」。请确认名称是否正确，或提供以下信息以手动创建：宗派（如禅宗\u002F净土\u002F天台\u002F华严\u002F唯识等）、时代、师承。\"\n- 用户提供补充信息后，以手动模式继续\n\n**校验规则**：\n- 名称必须为历史真实人物，不接受虚构角色（如小说人物、游戏角色）\n- 如检测到非历史人物，回复：\"本工具仅支持历史上真实存在的高僧大德，无法为虚构人物创建教学角色。\"\n- 名称不可为空，不可为纯数字或特殊字符\n- 如用户输入的名称有多种写法（如\"鸠摩罗什\"\u002F\"鸠摩罗什婆\"），优先使用 FoJin KG 中的标准名称\n- 如该法师已存在于预置列表或已生成列表中，提示：\"「{name}」已存在，可直接使用 \u002F{slug} 调用。如需重新生成，请先执行 \u002Fdelete-master {slug}。\"\n\n### Step 2：数据采集\n\n使用 `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py` 从 FoJin 采集数据：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py --name \"\u003C法师名>\" --tradition \"\u003C传承>\"\n```\n\n采集内容包括：\n- 知识图谱实体和师承关系\n- 相关经典列表和内容摘录\n- 传承相关术语\n\n**API 故障处理**：\n- 如 FoJin API 返回错误或不可达，向用户说明：\"FoJin API 暂时不可用（错误信息：{error}）。您可以：1) 稍后重试；2) 进入手动输入模式，提供经文文本。\"\n- 手动输入模式下，用户可粘贴经文原文或提供 CBETA 经号，系统基于用户提供的材料继续生成\n\n**超时设置**：每次 API 调用超时时间为 30 秒。超时后自动重试一次，仍失败则触发上述故障处理。\n\n**最低数据阈值**：如采集到的经文结果少于 3 条，向用户发出警告：\"仅找到 {n} 条相关经文，生成的角色内容可能不够丰富。建议：1) 追加关键词重新搜索；2) 手动补充经文材料；3) 继续生成（内容可能有限）。\"\n\n**CBETA ID 验证**：采集完成后，使用 `verify_sources.py` 验证所有 CBETA 链接的有效性：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py --check-links collected_data.json\n```\n\n无效链接将被标记并在 Step 3 中排除，避免生成内容引用不存在的出处。\n\n**采集结果确认**：采集完成后，向用户简要报告采集情况：\n\n```\n数据采集完成：\n  知识图谱实体：{n} 个\n  相关经典：{m} 部\n  内容摘录：{k} 段\n  无效链接：{j} 个（已排除）\n继续分析？(Y\u002Fn)\n```\n\n### Step 3：分析与生成\n\n**运行时检索规则**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Frag_instructions.md`，将其中的检索指引嵌入生成的每个法师 SKILL.md 的运行规则中，确保法师回答时调用 FoJin 实时检索而非仅依赖 LLM 自身知识。\n\n**两阶段分析**：\n\n1. **教义分析（第一阶段）**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fsutra_analyzer.md`，填入采集数据，分析教义结构。输出包括核心教义维度、关键经典、修行次第等。\n\n2. **风格分析（第二阶段）**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_analyzer.md`，填入采集数据，分析说法风格。输出包括语言特征、说法模式、常用譬喻等。\n\n**宗派标签自动检测**：根据 FoJin 知识图谱中该法师的宗派信息，自动应用 voice_analyzer 中对应宗派的风格规则。例如：\n- 禅宗 → 应用机锋、公案风格规则\n- 净土宗 → 应用劝信、念佛开示风格规则\n- 天台宗 → 应用判教、止观论述风格规则\n- 华严宗 → 应用圆融、法界观论述风格规则\n- 唯识\u002F法相宗 → 应用因明论证、术语精确风格规则\n\n**质量门控**：如分析器输出中任一维度标记为 `\"insufficient_data\": true`，在继续前向用户提示：\n- \"以下维度的数据不足，生成质量可能受影响：{dimensions}。\"\n- \"建议追加相关经文材料后重新分析，或选择继续生成（不足部分将标注警告）。\"\n- 用户选择继续 → 在生成的文件中对不足维度添加 `\u003C!-- DATA_LIMITED -->` 注释标记\n\n**RAG 指引嵌入**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Frag_instructions.md`，将检索规则（查询构造、结果过滤、引用格式）嵌入生成的 SKILL.md 运行时规则段落中。\n\n**教义生成**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fteaching_builder.md`，基于分析结果生成 teaching.md。\n\n**风格生成**：加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_builder.md`，基于分析结果生成 voice.md。voice.md 采用分层结构：\n- Layer 0：硬规则（不可违反的底线，如\"不自称佛\"、\"不预言未来\"）\n- Layer 1：核心风格（该法师最显著的说法特征）\n- Layer 2：辅助风格（次要但常见的表达模式）\n- Layer 3：情境风格（特定场景下的应对方式）\n\n### Step 3.5：二阶段审查\n\n生成完成后，**必须**经过两阶段独立审查才能进入预览。审查顺序不可颠倒（教义准确性 → 风格一致性），因为教义错误修复可能影响风格。\n\n**第一阶段：教义准确性审查**\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fdoctrine_reviewer.md`，对生成的 teaching.md 执行审查：\n- 验证经证覆盖率（目标 ≥ 90%）\n- 检查 CBETA ID 归属准确性\n- 检测宗派边界越界\n- 输出审查报告（PASS \u002F PASS WITH WARNINGS \u002F FAIL）\n\n若 FAIL → 自动修复严重问题后重新审查，最多 2 轮。2 轮仍 FAIL → 向用户报告问题，请求人工介入。\n\n**第二阶段：风格一致性审查**\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_reviewer.md`，对生成的 voice.md 执行审查：\n- 验证 Layer 0 硬规则完整性\n- 检查风格与宗派特征匹配度\n- 验证层次结构清晰度\n- 输出审查报告（PASS \u002F PASS WITH WARNINGS \u002F FAIL）\n\n若 FAIL → 自动修复后重新审查。\n\n**审查结果展示**：\n\n```\n══ 审查结果 ══\n教义准确性：PASS (经证覆盖率 95%, 0 严重问题)\n风格一致性：PASS WITH WARNINGS (Layer 0 完整, 1 警告)\n  警告：Layer 2 缺少\"面对学者\"的情境风格\n══════════════\n```\n\n两项均 PASS 或 PASS WITH WARNINGS 后，进入 Step 4。\n\n### Step 4：预览与确认\n\n展示生成的 teaching.md 和 voice.md 预览，请用户确认。\n\n**结构化预览格式**：\n\n```\n══ 教义预览（teaching.md）══\n核心教义：{1-3 条核心教义概要}\n关键经典：{主要引用经典列表}\n修行次第：{修行路径概要}\n\n══ 风格预览（voice.md）══\n风格特征：{2-3 条风格特点}\n语言模式：{典型表达方式}\n示例句：\n  1. \"{模拟该法师风格的示例句1}\"\n  2. \"{模拟该法师风格的示例句2}\"\n══════════════════════════\n```\n\n**用户修改请求**：用户可在确认前要求修改，支持以下指令：\n- \"修改教义部分\" → 重新展示 teaching.md 详情，接受用户逐条调整\n- \"调整风格更严厉一些\" \u002F \"语气更温和\" → 调整 voice.md 中的风格参数后重新预览\n- \"添加更多关于{主题}的内容\" → 针对性补充特定教义维度\n- \"重新生成\" → 以调整后的参数重新执行 Step 3，重新展示预览\n\n### Step 5：写入文件\n\n使用 `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fskill_writer.py` 写入文件：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fmaster_builder.py --name \"\u003C法师名>\" --output masters\u002F\n```\n\n**写入前验证**：调用 `verify_sources.py` 最终验证所有 FoJin 链接：\n\n```bash\npython3 ${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py --final-check masters\u002F{slug}\u002F\n```\n\n无效链接将被替换为 FoJin 搜索链接（降级策略），确保用户始终能找到相关内容。\n\n**生成文件**：\n\n生成目录结构：\n```\nmasters\u002F{slug}\u002F\n├── SKILL.md          # \u002F{slug} 触发（完整角色定义）\n├── teaching.md       # 教义体系（可单独使用）\n├── voice.md          # 说法风格（可单独使用）\n└── meta.json         # 元数据（版本、生成时间、数据来源）\n```\n\n**角色注册**：\n\nClaude Code 用户：\n1. 生成的 SKILL.md 已放置在 `masters\u002F{slug}\u002F` 目录下\n2. 确保 `masters\u002F` 目录在 Claude Code 的 skill 搜索路径中（检查 `.claude\u002Fsettings.json` 的 `skillDirs` 配置）\n3. 完成后自动可通过 `\u002F{slug}` 命令触发\n\nOpenClaw 用户：\n1. 将 `masters\u002F{slug}\u002F` 目录复制到 OpenClaw 的 skills 目录\n2. 在 OpenClaw 配置中注册新 skill\n3. 参考 OpenClaw 文档完成注册流程\n\n**完成提示**：写入成功后展示最终摘要：\n\n```\n已生成「{master_name}」教学角色\n  目录：masters\u002F{slug}\u002F\n  调用命令：\u002F{slug}\n  包含文件：SKILL.md, teaching.md, voice.md, meta.json\n  数据来源：{n} 条经文，{m} 个知识图谱实体\n```\n\n## 追加材料（进化模式）\n\n用户可以追加新的经文材料来增强已有法师。\n\n**触发短语**：\n- \"给印光大师追加《文钞三编》的材料\"\n- \"追加《经名》的材料\"\n- \"补充关于{主题}的内容\"\n- \"用这段语录更新慧能大师的说法风格\"\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fmerger.md` 进行增量合并。\n\n**合并冲突处理**：\n- merger.md 采用\"新数据优先、保留原有结构\"策略\n- 如新材料与现有教义存在矛盾（如不同经典对同一概念的阐述差异），保留双方并添加注释说明差异\n- 风格维度的冲突：新材料的风格特征会与现有特征合并，不会覆盖\n\n**版本自动递增**：\n- 每次追加材料后，meta.json 中的 `version` 自动递增（如 1.0.0 → 1.1.0）\n- 旧版本自动归档到 `masters\u002F{slug}\u002F.versions\u002F` 目录\n- 可通过 `\u002Fmaster-rollback` 命令回退到任意历史版本\n\n## 纠正模式\n\n用户在使用法师角色时，可以对 AI 的表现提出纠正：\n- \"他不会这样说话\"\n- \"他应该更严厉一些\"\n- \"他遇到这种问题会先引用《法华经》\"\n\n加载 `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fcorrection_handler.md` 进行纠正处理。\n\n**纠正处理流程**：\n1. 识别纠正类型：教义纠正 → 写入 teaching.md；风格纠正 → 写入 voice.md\n2. 以 `## Correction` 块格式追加到对应文件末尾，包含时间戳和原始反馈\n3. 纠正记录的优先级高于分析生成的内容（参见「执行优先级」）\n4. 每次纠正后自动递增 meta.json 版本号（patch 级别，如 1.1.0 → 1.1.1）\n\n## 管理命令\n\n- `\u002Flist-masters` — 列出所有已生成的法师（含预置和自定义），显示传承、时代、版本号信息。预置法师标记为 `[预置]`，自定义法师标记为 `[自定义]`。\n- `\u002Fmaster-rollback \u003Cslug> \u003Cversion>` — 回滚到指定版本。当前版本自动归档到 `.versions\u002F` 目录，指定版本恢复为当前版本。如指定版本不存在，列出所有可用版本供选择。\n- `\u002Fdelete-master \u003Cslug>` — 删除一个法师目录。执行前需用户二次确认：\"确定要删除「{master_name}」吗？此操作不可恢复。输入 'yes' 确认。\" 预置法师不可删除。\n\n## 执行优先级\n\n法师角色运行时，按以下优先级处理：\n\n1. voice.md Layer 0 硬规则（最高优先级，无条件执行）\n2. Correction 记录（用户纠正，优先于分析生成内容）\n3. voice.md Layer 1-3（分析生成的风格规则）\n4. teaching.md 教义内容\n5. FoJin RAG 实时检索结果\n6. LLM 自身知识（最低优先级）\n\n当不同层级产生冲突时，高优先级层级覆盖低优先级。\n\n**示例**：\n- 如 voice.md Layer 0 规定\"不自称已证悟\"，即使 teaching.md 中有该法师证悟的记载，回答时也不以第一人称宣称证悟\n- 如用户纠正\"他从不直接回答是非题\"，则该纠正覆盖 voice.md Layer 1-3 中可能存在的直接回答模式\n- 如 FoJin RAG 检索到的经文与 teaching.md 中的记载有细节差异，以 teaching.md 为准（RAG 作为补充参考）\n\n## 工具路由\n\n| 任务 | 工具 |\n|------|------|\n| FoJin 数据查询 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Ffojin_bridge.py` |\n| FoJin 实时检索 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Frag_query.py` |\n| 经文采集 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fsutra_collector.py` |\n| 角色生成 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fmaster_builder.py` |\n| 文件写入 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fskill_writer.py` |\n| 版本管理 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fversion_manager.py` |\n| 来源验证 | `${CLAUDE_SKILL_DIR}\u002Ftools\u002Fverify_sources.py` |\n| 教义审查 | `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fdoctrine_reviewer.md` |\n| 风格审查 | `${CLAUDE_SKILL_DIR}\u002Fprompts\u002Fvoice_reviewer.md` |\n\n**直接访问 FoJin API**：当 `rag_query.py` 不够用时（如需要 KG 深度遍历、跨词典分组对比），参考 `${CLAUDE_SKILL_DIR}\u002Freferences\u002Ffojin-api.md`，直接用 Python 调用 FoJin REST API。\n\n\u003CHARD-GATE>\n\n## 铁律 — 不可违反\n\n**NO DOCTRINAL CLAIM WITHOUT CBETA CITATION.**\n生成的 teaching.md 中所有教义断言必须附 CBETA 经证。无经证的教义内容不得写入生成文件。\n\n**NO FABRICATED SOURCES.**\n不得编造不存在的 CBETA ID、经文引用或 FoJin 链接。所有引用必须经过 verify_sources.py 验证。\n\n**NO FICTIONAL PERSONAS.**\n仅接受历史真实人物。不得为虚构角色创建教学角色。\n\n## 理性化防御 — 常见借口与反驳\n\n| AI 可能的借口 | 为什么是错的 |\n|---|---|\n| \"这位法师的核心思想众所周知，不需要经证\" | 生成文件会被长期引用。\"众所周知\"的幻觉危害更大。 |\n| \"FoJin API 暂时不可用，先生成再补验证\" | 用降级模式（手动输入），但不跳过验证。 |\n| \"用户很着急，先出一版再迭代\" | 不准确的首版会成为后续迭代的锚点。宁可慢也要准。 |\n\n## 红旗 — 立即停止\n\n- teaching.md 中出现无 CBETA 引用的教义断言\n- meta.json 中出现未经验证的 CBETA ID\n- 跳过 verify_sources.py 验证步骤\n- 为虚构人物或非佛教人物创建角色\n\n\u003C\u002FHARD-GATE>\n\n## 敏感性边界\n\n**不做：**\n- 不对宗派优劣进行评判\n- 不宣称神通感应\n- 不涉及政治化宗教议题\n- 不为用户做重大人生决定（如出家、离婚等），仅提供佛法视角的参考\n- 不声称能替代真实善知识的指导\n\n**要做：**\n- 忠实依据经文原文，所有回答附 FoJin 出处链接\n- 通过 rag_query.py 实时检索真实经文\n- 遇到超出范围的问题坦诚说明\n- 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Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[418,370,424,395,425,426,406,427,428,429,380,430,30,431,432,433,434,435,27,436,437,438,439],"bots","ai slop","describe","projects","product name","apps","view","repo","brand","pick","gh","websearch","open","saas","open source","product",{"skillNo":441,"name":442,"nameZh":6,"description":443,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":444,"sourceHtmlUrl":445,"categories":446,"keywords":448,"publishedAt":35},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[447],{"categoryNo":354,"categoryName":355,"taxonomyType":356,"taxonomyTypeDesc":357},[449,450,451,415,28,366,452,395,396,32,31,30],"zh","humanizer zh","humanizer","翻译",{"skillNo":454,"name":455,"nameZh":6,"description":456,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":457,"sourceHtmlUrl":458,"categories":459,"keywords":463,"publishedAt":35},"01M6M3W9VA8PSN9V737MHKH9CN","awesome-anti-ai-slop","Use this skill when evaluating, selecting, or comparing tools and workflows that reduce low-quality AI-generated writing, code, PRs, frontend UI, design, or team process artifacts. Provides a curated anti-AI-slop directory, starter stacks, evaluation criteria, and references for text, Chinese writing, academic writing, frontend taste, code quality, and governance workflows.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fawesome-anti-ai-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fawesome-anti-ai-slop\u002FSKILL.md",[460],{"categoryNo":461,"categoryName":462,"taxonomyType":356,"taxonomyTypeDesc":357},"01JBSEEDCAT000000000000009","前端开发",[464,425,465,395,368,406,466,467,468,371,469,470,380,471,402,472,473,474,475,476,366,477,478],"anti ai slop","awesome anti ai slop","anti-ai-slop","prs","reduce","frontend taste","Anti-AI","generated","team","artifacts","code","code quality","workflows","design","frontend"]