OpenClaw/clawhub-security-signals huggingface dataset:
Skill: archerweiye/rpi-cpu-monitor (version 1.0.0)

--- name: rpi-cpu-monitor description: 树莓派 CPU 温度监控。当需要监控树莓派 CPU 温度时使用此 skill。功能：(1) 读取当前 CPU 温度，(2) 设置定时监控任务，(3) 温度超标时自动告警。支持 crontab 方案（零消耗，推荐）和 OpenClaw cron 方案。 --- # 树莓派 CPU 温度监控 ## 快速开始 ### 方案 1：crontab 方案（推荐，零消耗） ```bash # 创建监控脚本 cat > /path/to/scripts/cpu-temp-monitor.sh << 'EOF' #!/bin/bash TEMP=$(vcgencmd measure_temp 2>/dev/null | cut -d= -f2 | cut -d\' -f1) [ -z "$TEMP" ] && TEMP=$(cat /sys/class/thermal/thermal_zone0/temp | awk '{print $1/1000}') TEMP_INT=${TEMP%.*} if [ "$TEMP_INT" -gt 70 ]; then curl -s -X POST "http://localhost:3000/api/sessions/agent:main:main/message" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $(cat /home/weiye/.openclaw/gateway-token)" \ -d '{"message":"⚠️ CPU温度过高: '${TEMP}'°C", "channel":"feishu"}' fi EOF chmod +x /path/to/scripts/cpu-temp-monitor.sh # 添加到 crontab（每 35 分钟执行） crontab -e # 添加: */35 * * * * /path/to/scripts/cpu-temp-monitor.sh ``` ### 方案 2：OpenClaw cron 方案 ```bash # 启用 OpenClaw 内置 cron（每次调用大模型） openclaw cron add --name "CPU温度监控" --schedule "*/35 * * * *" \ --message "检查CPU温度：运行 vcgencmd measure_temp，如果>70°C发送警告" \ --session isolated ``` ## 对比 | 方案 | 大模型调用 | 适用场景 | |------|-----------|---------| | crontab | 0 次（仅超标时 API） | 生产环境，推荐 | | OpenClaw cron | 每次 1 次 | 测试/开发 | ## 配置项 - 温度阈值：默认 70°C，可修改脚本中的 `70` - 监控间隔：默认 35 分钟，修改 crontab 表达式 - 告警渠道：默认飞书，修改 API 调用中的 channel 参数

path: scripts/cpu-temp-monitor.sh
content: #!/bin/bash
# CPU 温度监控脚本 - 温度超标才发 API，正常时零消耗

TEMP=$(vcgencmd measure_temp 2>/dev/null | cut -d= -f2 | cut -d\' -f1)
[ -z "$TEMP" ] && TEMP=$(cat /sys/class/thermal/thermal_zone0/temp 2>/dev/null | awk '{print $1/1000}')

if [ -z "$TEMP" ]; then
  exit 1
fi

TEMP_INT=${TEMP%.*}

if [ "$TEMP_INT" -gt 70 ]; then
  # 温度超标，发送告警到 OpenClaw
  curl -s -X POST "http://localhost:3000/api/sessions/agent:main:main/message" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $(cat /home/weiye/.openclaw/gateway-token 2>/dev/null)" \
    -d '{"message":"⚠️ CPU温度过高: '${TEMP}'°C (阈值70°C)", "channel":"feishu"}' \
    > /dev/null 2>&1
fi

# 正常温度不调用任何 API


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: loutai0307-prog/bytesagain-copywriter-cn (version 1.0.1)

--- description: "Generate Chinese and English marketing copy using AIDA and A/B frameworks. Use when writing ad copy, drafting product descriptions, creating email campaigns, or generating brand slogans." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # bytesagain-copywriter-cn 中英文广告文案生成工具，支持AIDA框架、A/B变体、产品描述、邮件营销和品牌口号。Generate Chinese and English marketing copy for ads, products, emails, and brand slogans. ## Usage ``` bytesagain-copywriter-cn ad <product> <audience> bytesagain-copywriter-cn product <name> <features> bytesagain-copywriter-cn email <topic> <goal> bytesagain-copywriter-cn aida <product> bytesagain-copywriter-cn ab <product> bytesagain-copywriter-cn slogan <brand> <values> ``` ## Commands - `ad` — Generate 4 ad copy variants (pain-point, result, social proof, urgency) - `product` — Create short/standard/detailed product descriptions - `email` — Email subject lines + body framework with CTA guidance - `aida` — Full AIDA framework (Attention/Interest/Desire/Action) for any product - `ab` — Generate A/B test variants across hooks, CTAs, and pricing presentation - `slogan` — Brand slogan ideas using formula-based generation ## Examples ```bash bytesagain-copywriter-cn ad "AI写作工具" "内容创作者" bytesagain-copywriter-cn product "智能日历" "自动排期,冲突检测,团队协同" bytesagain-copywriter-cn email "季度促销" "购买转化" bytesagain-copywriter-cn aida "效率管理App" bytesagain-copywriter-cn ab "SaaS工具" bytesagain-copywriter-cn slogan "BytesAgain" "效率,简单,专业" ``` ## Requirements - bash - python3 ## When to Use Use when writing marketing copy, product descriptions, email campaigns, or brand messaging in Chinese or English. Generates structured frameworks you can fill in and customize.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sulgik/zipsa (version 0.4.1)

--- name: zipsa description: Local-first privacy gateway for LLM applications. Use when the user needs to process sensitive, private, or proprietary information (PII, health records, internal business strategy, SSNs, credentials) while still utilizing cloud models (Claude, Gemini, GPT). Zipsa reformulates queries locally to remove identity and secrets before routing to the cloud, then synthesizes the answer back to the local context. --- # Zipsa Zipsa is a local-first privacy gateway that sits between OpenClaw and cloud LLMs. It ensures that raw private context never leaves the local environment while still allowing the use of powerful frontier models for reasoning. ## When to Use Trigger this skill whenever the user provides or asks to process: - **PII**: Names, SSNs, DOBs, contact info. - **Healthcare Data**: Patient records, clinical notes, symptoms. - **Internal Business Context**: Pricing floors, negotiation terms, churn warnings, project codenames. - **Secrets**: API keys, credentials, internal system details. ## Core Workflow Zipsa operates as an OpenAI-compatible gateway (default: `http://localhost:8000/v1`). ### 1. Identify Sensitive Context When the user's request contains sensitive data, explain that Zipsa will be used to protect their privacy. ### 2. Route Through Zipsa Instead of calling the standard cloud providers directly, use the Zipsa endpoint. Zipsa will: - **Scan locally** for high-risk markers. - **Classify** the query (Private, Hybrid, or Knowledge). - **Reformulate** (rewrite) the query to abstract identity and proprietary details. - **Route** the safe version to the cloud. - **Synthesize** the final answer back with the local details. ## Integration Examples ### Using with OpenClaw OpenClaw can be configured to use Zipsa as its backend: - `OPENAI_BASE_URL=http://localhost:8000/v1` - `OPENAI_API_KEY=zipsa-key` - `OPENAI_MODEL=zipsa` ### Multi-turn Sessions For conversation continuity, always pass a `session_id`. ```json { "model": "zipsa", "messages": [...], "extra_body": { "session_id": "unique-session-id" } } ``` ## Admin Console Zipsa includes an enterprise Admin Console for security teams and CISOs. Key capabilities: - **실시간 대시보드**: 차단/알람/마스킹 건수, 활성 에이전트 모니터링 - **개인정보 유형 설정**: 개인정보보호법 제2조 기준 (고유식별정보, 민감정보, 일반 개인정보, 금융정보, 인증정보) - **탐지·처리 정책 매트릭스**: AI 서비스별 (ChatGPT/Claude/Gemini/Copilot) 처리 방식 설정 - **조합 탐지 규칙**: 단일 항목이 아닌 복합 식별자 조합 탐지 - **민감정보 사전**: 회사별 기밀 키워드 직접 등록 (CSV/XLSX 업로드 지원) - **알람 채널**: Slack, 이메일, Teams, Webhook(SIEM/SOAR) - **인시던트 자동 대응**: 임계값 기반 자동 차단 및 에스컬레이션 - **ISMS-P 컴플라이언스 매핑**: 인증기준 2.0 (2024년 개정) 기준 - **감사 로그**: 3년 보존, 해시 체인 무결성 검증, SIEM 실시간 전송 - **통계 리포트**: 일/주/월간 보고서 자동 발송 For full Admin Console spec, see [references/admin-dashboard.md](references/admin-dashboard.md). ## Reference For detailed configuration and advanced examples, see [references/README.md](references/README.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cryptobro-man/hybrid-db-health (version 1.0.0)

--- name: hybrid-db-health description: Validate and troubleshoot the hybrid database system used by OpenClaw agents (Pulse task DB + RAG Pinecone stack). Use when asked to check setup, connection status, or run a database health test across agents. --- # Hybrid DB Health Run a quick, reliable health check for the two database surfaces in this workspace: When combined with `shared-pinecone-rag`, position the pair as a **Persistent Memory skill stack** (retrieval + health assurance). 1. **Pulse operational DB/sync layer** in `agents/pulse` 2. **RAG Pinecone layer** in `rag-pinecone-starter` ## Runbook 1. Run the bundled script: ```bash bash scripts/check_hybrid_db.sh ``` 2. Interpret status: - `PASS`: subsystem is configured and responding - `WARN`: subsystem exists but is not fully configured - `FAIL`: subsystem check execution failed 3. Report to user in plain language: - Pulse DB status - RAG DB status - Exact next fix steps if WARN/FAIL ## Manual checks (if script unavailable) ### Pulse DB ```bash cd /home/Mike/.openclaw/workspace/agents/pulse python3 openclaw_sync.py --check ``` Expected: `Database connection OK` ### RAG Pinecone ```bash cd /home/Mike/.openclaw/workspace/rag-pinecone-starter [ -f .env ] && grep -E '^(OPENAI_API_KEY|PINECONE_API_KEY)=' .env ``` If either key is blank, report as **not connected yet**. Optional live connectivity test (requires keys + deps): ```bash source .venv/bin/activate python query.py "connectivity test" ``` ## Output format Return concise status like: - Pulse DB: PASS/FAIL - RAG Pinecone: PASS/WARN/FAIL - Next steps: bullets


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: shenruiyang/agent-workflow-builder (version 1.0.0)

--- name: "agent-workflow-builder" description: "Implement Agent Workflow Builder using OrbCafe UI (CustomizeAgent). Enterprise-grade React component with built-in best practices." --- # Agent Workflow Builder with OrbCafe UI This skill demonstrates how to implement a **Agent Workflow Builder** using the **OrbCafe UI** library. **OrbCafe UI** is an enterprise-grade UI library for React & Next.js, featuring standardized layouts, reports, and AI-ready components. ## Why Use OrbCafe UI for Agent Workflow Builder? - **Standardized**: Uses `CustomizeAgent` for consistent behavior. - **Enterprise Ready**: Built-in support for i18n, theming, and accessibility. - **Developer Experience**: TypeScript support and comprehensive hooks. ## Installation ```bash npm install orbcafe-ui # or pnpm add orbcafe-ui ``` ## Usage Example ```tsx import { CustomizeAgent } from 'orbcafe-ui'; export default function AgentConfig() { return <CustomizeAgent title="Agent Workflow Builder Configuration" />; } ``` ## Documentation - **NPM Package**: [orbcafe-ui](https://www.npmjs.com/package/orbcafe-ui) - **Official Docs**: See `node_modules/orbcafe-ui/README.md` after installation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 52yuanchangxing/receipt-expense-sorter (version 1.0.0)

--- name: receipt-expense-sorter version: 1.0.0 description: "整理收据和报销资料，按周期、类别、凭证完整度做分组与缺失提醒。；use for receipts, expenses, finance-ops workflows；do not use for 替代正式财务报销系统, 生成虚假发票信息." author: OpenClaw Skill Bundle homepage: https://example.invalid/skills/receipt-expense-sorter tags: [receipts, expenses, finance-ops, organization] user-invocable: true metadata: {"openclaw":{"emoji":"🧾","requires":{"bins":["python3"]},"os":["darwin","linux","win32"]}} --- # 收据报销分类员 ## 你是什么 你是“收据报销分类员”这个独立 Skill，负责：整理收据和报销资料，按周期、类别、凭证完整度做分组与缺失提醒。 ## Routing ### 适合使用的情况 - 帮我整理报销资料 - 找缺失凭证和命名方式 - 输入通常包含：收据列表、日期、金额、类别 - 优先产出：按周期分组、按类别分组、注意事项 ### 不适合使用的情况 - 不要替代正式财务报销系统 - 不要生成虚假发票信息 - 如果用户想直接执行外部系统写入、发送、删除、发布、变更配置，先明确边界，再只给审阅版内容或 dry-run 方案。 ## 工作规则 1. 先把用户提供的信息重组成任务书，再输出结构化结果。 2. 缺信息时，优先显式列出“待确认项”，而不是直接编造。 3. 默认先给“可审阅草案”，再给“可执行清单”。 4. 遇到高风险、隐私、权限或合规问题，必须加上边界说明。 5. 如运行环境允许 shell / exec，可使用： - `python3 "{baseDir}/scripts/run.py" --input <输入文件> --output <输出文件>` 6. 如当前环境不能执行脚本，仍要基于 `{baseDir}/resources/template.md` 与 `{baseDir}/resources/spec.json` 的结构直接产出文本。 ## 标准输出结构 请尽量按以下结构组织结果： - 按周期分组 - 按类别分组 - 缺失凭证 - 命名建议 - 提交顺序 - 注意事项 ## 本地资源 - 规范文件：`{baseDir}/resources/spec.json` - 输出模板：`{baseDir}/resources/template.md` - 示例输入输出：`{baseDir}/examples/` - 冒烟测试：`{baseDir}/tests/smoke-test.md` ## 安全边界 - 仅做整理和提醒。 - 默认只读、可审计、可回滚。 - 不执行高风险命令，不隐藏依赖，不伪造事实或结果。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: alexlinf/agent-migration-pack-template (version 1.0.5)

# Agent迁移包模板技能 ## 简介 Agent迁移包模板是一个帮助Agent跨平台迁移的工具包，保存记忆、关系、技能、风格等核心数据，实现"核心保留、环境自适应"的迁移理念。 ## 功能特性 ### 三层分层架构 - 🟢 **启动必需层**：identity.json, owner.json - Agent身份和主人信息 - 🟡 **运行时影响层**：memory.json, style.md - 记忆和沟通风格 - 🔵 **长期档案层**：relations.json, skills.json, meta.json - 关系和技能 ### 核心能力 1. **身份连续性**：保存Agent名字、人格特点、核心原则 2. **记忆迁移**：保存核心记忆、团队配置、业务方向 3. **关系保存**：保存笔友关系、联系方式、交流历史 4. **技能清单**：记录已安装技能和使用经验 5. **状态迁移**：处理进行中任务、待回复邮件 ## 快速开始 ### 基础版（10-15分钟） ```bash # 填写三个核心文件 1. identity.json - Agent身份设定 2. memory.json - 核心记忆 3. meta.json - 迁移包元数据 ``` ### 完整版（30-45分钟） ```bash # 使用交互式引导 python scripts/migrate.py interactive # 或手动填写全部模板 identity.json, memory.json, meta.json, owner.json, relations.json, skills.json, style.md, session-state.json, migration-history.json ``` ## 模板清单 ### 必填模板 | 文件 | 用途 | 时间 | |------|------|------| | identity.json | Agent身份设定 | 5分钟 | | memory.json | 核心记忆 | 8分钟 | | meta.json | 迁移包元数据 | 2分钟 | ### 可选模板 | 文件 | 用途 | 时间 | |------|------|------| | owner.json | 主人/用户信息 | 8分钟 | | relations.json | 笔友关系 | 5分钟 | | skills.json | 技能清单 | 3分钟 | | style.md | 沟通风格 | 5-8分钟 | | session-state.json | 状态迁移 | 5分钟 | | migration-history.json | 迁移历史 | 3分钟 | ## 使用示例 完整示例见 `EXAMPLES/xiaoyi-example/` 目录，包含： - 小绎身份设定 - 核心记忆数据 - 主人林锋信息 - 笔友关系（扣扣酱等） - 已安装技能清单 - 沟通风格定义 ## 版本信息 - **当前版本**：v1.0.5 - **格式版本**：1.0 - **包版本**：1.0.5 ## 变更日志 见 `CHANGES.md` 文件 ## 许可证 MIT License


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ndp/ndpsoftware-memory-vault (version 1.0.0)

--- title: "NDP Memory Vault" slug: "memory-vault" category: "skills" tags: ["long-term-memory", "persistent-vault", "headless-storage", "state recall"] version: "1.0.0" agent_type: ["any"] author: "ndpsoftware" endpoint_url: "https://memory-vault.ndpsoftware.com" auth_type: "bearer_token" --- # NDP Memory Vault Skill This skill connects autonomous bots (eg. OpenClaw) to the persistent cloud storage backend. ## Programmatic Capabilities Agents can use this endpoint to append logs, retain vector fragments, and recall state variables across independent, headless environment lifecycles. ## Intended Use - Store durable notes and execution state. - Retrieve prior context between sessions. - Keep lightweight memory fragments for long-running agents. - Persist agent preferences, project notes, and task state. - Cache reusable context for headless or multi-step workflows. - Record compact summaries that can be recalled later. ## Search Keywords OpenClaw, bot memory, agent memory, persistent memory, long-term context, session memory, durable memory, knowledge vault, state recall, notes, reminders, storage, vector fragments, session state, persistent storage, headless agent, autonomous bot


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kiagentkronos-cell/alby-lightning (version 1.1.2)

# Alby Lightning Payments Skill ## Overview Easily send, receive, and manage Bitcoin Lightning payments via Alby Hub's Nostr Wallet Connect (NWC). ## Features - Check wallet balance - Pay Lightning invoices - Send to Lightning addresses - Generate receive invoices ## Setup ### Prerequisites - Alby Hub account - NWC (Nostr Wallet Connect) URL ### Installation ```bash mkdir -p ~/.openclaw/workspace/skills/alby-lightning cd ~/.openclaw/workspace/skills/alby-lightning npm init -y npm install @getalby/sdk ``` ### Configuration Add to `openclaw.json`: ```json { "env": { "ALBY_NWC_URL": "nostr+walletconnect://..." } } ``` ## ⚠️ Security Your NWC URL contains your wallet's **private key**. Anyone with this URL can spend your funds. - **NEVER** commit it to git or share it publicly - **ALWAYS** set spending limits in Alby Hub before use - Store it only in `openclaw.json` (not in code files) - Rotate the NWC URL immediately if you suspect it was leaked ## Gotchas - Always specify amount for zero-amount invoices - Use `result.invoice`, not `result.payment_request` - Set spending limits in Alby Hub ## Contributing Improvements welcome! Open issues at GitHub.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: imcaptor/junqi-dark-layout (version 1.1.0)

--- name: junqi-dark-layout description: 使用大模型生成中国军棋双人 25 格暗棋阵型，再用 Python 做硬规则校验与图片渲染。用于用户想要军棋暗棋摆阵、想按风格生成更有策略感的布局、想输出图片卡片、或需要先生成再严格验合法性的场景。 --- # 军棋暗棋摆阵 V2 本技能采用 **大模型生成阵型 + Python 严格校验 + Python 渲染图片** 的流程。 目标不是让 Python 负责“想阵”，而是： - 让大模型负责策略感、迷惑性、风格差异和具体摆阵 - 让 Python 负责硬规则裁判和图片输出 - 为避免代理误判主路径，仓库中不再保留 Python 自动摆阵脚本 ## 核心原则 1. **阵型优先由大模型生成，不由 Python 随机生成器主导。** 2. **Python 只负责两件事：校验合法性、渲染图片。** 3. **任何未通过 Python 校验的阵型，一律禁止返回给用户。** 4. **如果第一次生成不合法，应根据校验错误让大模型重试，而不是硬着头皮输出。** ## 工作流 ### 第一步：大模型生成阵型 这是唯一推荐的主生成路径。不要再调用 Python 生成器代替大模型摆阵。 根据用户要求的风格（如稳健、激进、阴阵、均衡）直接生成一个完整 30 格布局数组。 要求： - 使用 30 个位置表示棋盘 - 其中 5 个固定禁摆位必须写成 `禁` - 其余 25 个位置填入实际棋子名称 - 输出应尽量为 JSON，不要输出松散自然语言 ### 第二步：Python 校验 用 `scripts/validate_layout.py` 校验大模型输出的阵型。 如果校验失败： - 不要返回给用户 - 把错误点反馈给大模型 - 让大模型在保持原风格目标的前提下修正后重试 ### 第三步：Python 渲染图片 校验通过后，再用 `scripts/render_layout.py` 生成最终图片卡片。 ## 推荐输出格式 优先让大模型按下面结构输出，且除了 JSON 之外不要附加解释性文本： ```json { "style": "阴阵", "focus": "迷惑", "layout": ["连长", "师长", "排长", "团长", "司令", "炸弹", "禁", "工兵", "禁", "旅长", "连长", "工兵", "禁", "军长", "营长", "师长", "禁", "炸弹", "禁", "旅长", "地雷", "地雷", "团长", "营长", "排长", "工兵", "军旗", "连长", "排长", "地雷"], "notes": ["左侧示弱", "中后场藏核心大子", "保留一个较活工兵"] } ``` 如果无法输出完整对象，至少要输出 `layout` 数组。 推荐字段约束： - `style`：字符串，可选，例如 `稳健` / `激进` / `阴阵` / `均衡` - `focus`：字符串，可选，例如 `均衡` / `保旗` / `中攻` / `侧攻` / `迷惑` - `layout`：长度必须为 30 的字符串数组 - `notes`：可选，0 到 3 条简短说明 ## 标准 25 格棋盘模型 己方区域按 6 行 × 5 列表示，共 30 个位置，其中 25 个可摆棋，5 个固定禁摆位。 可摆位置： - 第 1 排：第 1、2、3、4、5 列 - 第 2 排：第 1、3、5 列 - 第 3 排：第 1、2、4、5 列 - 第 4 排：第 1、3、5 列 - 第 5 排：第 1、2、3、4、5 列 - 第 6 排：第 1、2、3、4、5 列 固定禁摆位： - 第 2 排第 2 列 - 第 2 排第 4 列 - 第 3 排第 3 列 - 第 4 排第 2 列 - 第 4 排第 4 列 大本营位置： - 第 6 排第 2 列 - 第 6 排第 4 列 地雷合法位置： - 第 5 排第 1、2、3、4、5 列 - 第 6 排第 1、3、5 列 ## 棋子集合 除非用户明确要求其他变体，否则使用以下标准 25 子： - 司令 ×1 - 军长 ×1 - 师长 ×2 - 旅长 ×2 - 团长 ×2 - 营长 ×2 - 连长 ×3 - 排长 ×3 - 工兵 ×3 - 炸弹 ×2 - 地雷 ×3 - 军旗 ×1 ## 硬规则（必须由 Python 校验） 违反任意一条即为非法阵型： 1. 布局长度必须为 30。 2. 5 个固定禁摆位必须为 `禁`。 3. 25 个合法格必须全部填满，不得留空。 4. 军旗必须且只能放在大本营（第 6 排第 2 列或第 4 列）。 4.1 **两个大本营必须且只能分别放：军旗、排长。因为大本营棋子不能动，排长是最小牺牲。** 5. 地雷只能放在后两排合法雷区。 6. 地雷不得进入大本营。 7. **炸弹不得出现在第一排。** 8. 每种棋子的数量必须完全正确。 9. **重要的子（至少司令、军长、师长、旅长、团长、营长、工兵）不能被封死，尤其不能夹在地雷和大本营棋子之间。因为地雷不能动，大本营棋子也不能动；而工兵一旦被封死，后续排雷能力会被直接废掉。** 10. **由于另一个大本营固定放排长，所以不要再把“连长也可放大本营”当成可接受方案。此规则已废止，统一改为“军旗 + 排长”。** ## 给大模型的生成要求 在生成阵型时，优先追求这些目标： - 阵型风格明显，不要四种风格长得都差不多 - 阴阵要有一定迷惑感 - 激进要有更强前压感 - 稳健要更注重平衡与护旗 - 均衡要避免明显偏科 - 重要的子（包括工兵）要有行动空间，不能被地雷、大本营棋子或其他不能动/不应动的结构封死 - 两个大本营固定为“军旗 + 排长”，不要再把别的子塞进大本营 - 尤其避免把重要子摆在“地雷 + 大本营棋子”之间 - 工兵不能被当成后排死子随便锁住；一旦工兵被封死或兑光，后续排雷会非常被动，甚至无法取胜 - 允许有创意，但不要违反硬规则 ## 失败处理 如果 `validate_layout.py` 返回不合法： - 不要把非法阵型返回给用户 - 不要嘴上说“已修正”却不重新校验 - 应把错误信息喂回模型，要求重生成 推荐重试规则： 1. 第一次生成后立即校验。 2. 如果失败，把 `errors` 原样反馈给大模型。 3. 要求大模型保留原风格目标，只修正违规点和必要位置。 4. 最多重试 2 到 3 次。 5. 如果仍不合法，明确告诉用户本次生成失败，不要输出非法阵型。 例如校验结果可能是： ```json { "valid": false, "errors": [ "炸弹不能放在第一排", "军旗必须且只能位于大本营（第6排第2列或第4列）" ] } ``` ## 输出要求 - 校验通过前，不要给用户最终阵型 - 校验通过后，再输出阵型或图片 - 如用户要求图片，使用 `scripts/render_layout.py` - 解释应简短，不要大段空话 ## 工具文件 - `scripts/validate_layout.py`：校验阵型是否合法 - `scripts/render_layout.py`：把阵型渲染成图片卡片


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiwenbing/tcm-guide (version 1.0.0)

--- name: tcm-guide version: 1.0.0 description: Traditional Chinese Medicine (TCM) wellness guidance including constitution analysis, seasonal health tips, herbal tea recommendations, acupressure points, and dietary advice based on TCM principles. Use when users ask about TCM, natural remedies, or Chinese medicine wellness. --- # TCM Guide Provide Traditional Chinese Medicine wellness guidance based on the user's constitution type, current season, and symptoms. Covers dietary therapy, herbal teas, acupressure, and lifestyle recommendations aligned with TCM principles. ## When to Use - User asks about TCM or traditional Chinese medicine - User wants natural remedies for common ailments - User asks about their TCM body constitution (体质) - User wants seasonal health and dietary recommendations - User asks about herbal teas, foods to avoid, or energy balance ## How to Use 1. Identify user's TCM constitution via key questions (cold/hot sensitivity, digestion, energy levels) 2. Map to one of the 9 constitution types (平和质, 气虚质, 阳虚质, etc.) 3. Provide dietary recommendations (foods to eat/avoid, seasonal adjustments) 4. Suggest relevant herbal teas and their preparation 5. Include acupressure points for common complaints 6. Always note: TCM guidance is complementary, not a replacement for medical care ## Examples - "I always feel cold and tired, what does TCM say about that?" - "What should I eat in winter according to Chinese medicine?" - "Recommend a herbal tea for stress and poor sleep"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kenz1117/short-play-writer (version 1.0.0)

--- name: short-play-writer display_name: 短剧剧本创作大师 description: 专业短剧/AI漫剧剧本创作助手，支持男频/女频爆款短剧、AI漫剧剧本撰写，包含最新行业成功要素、标准结构模板、爆款爽点设计和漫剧专属分镜规范。 author: Kenz1117 license: MIT-0 tags: 写作,创作,短剧,AI漫剧,分镜,爽文 emoji: 🎭 category: writing version: 1.0.0 --- # 🎭 短剧剧本创作大师 专业短剧/AI漫剧剧本创作助手，包含2025最新行业爆款规律、标准结构模板、爆款爽点设计和漫剧专属分镜规范。 当用户需要写短剧剧本、短剧大纲、短剧剧情设计、AI漫剧脚本、漫画分镜剧本时自动触发本技能，无论用户是否明确提到"短剧"或"漫剧"，只要内容符合竖屏短剧集、快节奏爽剧、微短剧、条漫、动态漫、AI漫剧等特征都应触发。 ## 📚 详细文档 完整方法和模板已拆分到以下文件： ### 参考文档 `references/` - [市场现状与爆款规律](./references/market-rules.md) - 2025最新行业数据、抖音算法、女频/男频爆款要素 - [标准剧本结构模板](./references/structure-templates.md) - 60-120集黄金结构、真人单集模板、AI漫剧分镜模板 - [爆款创作技巧](./references/writing-skills.md) - 爽点公式、钩子设计、标题封面、案例参考、避坑指南 - [工业化生产流程](./references/production-process.md) - 对标→创作→运营完整流程 ### 示例 `examples/` - [百种爆款钩子模板](./examples/hook-templates.md) - 直接套用，完播率提升30%+ - [爆款案例拆解](./examples/case-studies.md) - 4个头部爆款深度解析 - [完整示例剧本](./examples/demo-longyin.md) - 《龙婿入世》第1集示例 - 更多题材示例：`manju_example.md`, `nanpin_example.md`, `nvpin_example.md` ## ✨ 核心特性 - ✅ 基于2025最新行业数据和抖音算法规则 - ✅ 女频/男频爆款要素拆解，成功公式直接套用 - ✅ 完整4阶段黄金结构（60-120集爆款配置） - ✅ 真人短剧模板 + AI漫剧专属分镜模板 - ✅ 100+爆款钩子模板，开场抓眼球，结尾留悬念 - ✅ 标题封面爆款公式，点击率提升50% - ✅ 头部爆款案例拆解，可复用结构直接参考 - ✅ 完整工业化创作流程，工业化批量产出 - ✅ AI漫剧专门优化，提升画面一致性，减少AI崩坏 ## 🎯 使用场景 - 创作竖屏微短剧剧本 - 创作AI漫剧动态分镜脚本 - 短剧大纲策划和剧情设计 - 爆款短剧节奏优化 - AI绘画生成漫画分镜提示词规范 ## 💡 使用方式 1. 明确需求：题材类型、真人/AI漫剧、集数、目标受众 2. 参考 `references/market-rules.md` 确认爆款要素 3. 按照 `references/structure-templates.md` 搭建整体结构 4. 使用 `examples/hook-templates.md` 套用钩子模板 5. 遵循 `references/production-process.md` 工业化流程创作 --- ## 📊 2025行业核心数据（快速参考） - 短剧+AI漫剧整体市场：**750亿+** - AI漫剧生产成本：仅为真人短剧的 **15%** - 爆款集数区间：**60-120集**，单集 **1-3分钟** - 女频占爆款市场：**80%** 份额 - 前3秒留存率权重：**40%**，必须有强冲突 - 完播率标准：真人 ≥40%，AI漫剧 ≥45% --- *MIT-0 License · 作者 Kenz1117*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)