OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wangjiaocheng/knowledge-explainer (version 1.0.0)

--- name: knowledge-explainer author: 王教成 Wang Jiaocheng (波动几何) description: >- 知识点原子化拆解与专业讲解技能。将任意知识点拆解为不可再分的原子概念，按依赖关系排序， 逐个进行包含定义、公式、推导、定量分析、常见误区辨析的专业讲解，最终串联回原始问题给出完整回答。 适用于：学科教学、概念梳理、备课、自学、知识体系构建。 Triggers: 讲解、解释、拆解概念、原子概念、知识体系、知识点分析、explain this、break down、teach、knowledge。 --- # Knowledge Explainer — 知识点原子化讲解 ## 目的 将任意知识点拆解为不可再分的原子概念，按认知依赖关系排序，对每个原子概念进行专业级讲解（定义、公式、推导、定量分析、误区辨析），最终从原子概念推导回原始问题的完整解答。 ## 工作流程 ### Step 1: 确定知识点与边界 - 明确要讲解的知识点 - 确定学科领域（物理、数学、化学、经济、计算机等） - 确定讲解深度：入门 / 大学 / 研究生 / 专业 - 确定是否需要前置知识（如有，先简要说明） ### Step 2: 原子概念拆解 将知识点分解为不可再分的最小概念单元。每个原子概念必须满足： - **不可再分性**：无法在不丢失含义的前提下进一步拆分 - **独立性**：每个概念自身含义完整 - **必要性**：缺少任何一个都会导致知识点不完整 输出为原子概念表： ``` | # | 原子概念 | 定义（一句话精确表述） | |---|---------|----------------------| | ① | ... | ... | ``` 标注概念间的依赖关系链：① → ② → ③...（必须按顺序理解） ### Step 3: 逐个原子概念专业讲解 对每个原子概念按以下结构讲解： #### 3.1 定义 - 精确的形式化定义 - 国际单位制单位和量纲 - 适用条件和边界 #### 3.2 数学表达 - 核心公式（使用 LaTeX 格式） - 变量说明表（符号、名称、单位、取值范围） - 公式的推导过程（非直接给出，要从更基础的原理推导） #### 3.3 物理直觉 / 逻辑直觉 - 用类比或直觉解释为什么公式长这样 - 量级估算：给出具体数值帮助建立直觉 - 极端情况分析：公式在某些极限下是否退化到已知结论 #### 3.4 常见误区 - 列出 2-3 个最常见的误解 - 给出正确表述和错误表述的对比 - 解释误区产生的原因 ### Step 4: 概念间关联 - 明确原子概念之间的逻辑关系（推导、并列、因果） - 用公式推导链连接相邻概念 - 标注哪些概念可以推广到更广泛的领域 ### Step 5: 串联回原始问题 从第一个原子概念出发，沿依赖链逐步推导，最终回到原始问题并给出完整、严谨的解答。 **要求：** - 推导链完整，不得跳跃 - 最终结论用一段精确的总结陈述 - 可选：给出该知识点的延伸方向（更高阶的相关理论） ## 输出格式 整个讲解输出为结构化 Markdown 文档： ```markdown # [知识点标题] ## 1. 原子概念拆解 [原子概念表 + 依赖关系] ## 2. 逐个原子概念讲解 ### ① [概念名] [定义 + 数学表达 + 直觉 + 误区] ### ② [概念名] ... ## 3. 串联：从原子概念回到完整问题 [推导链 + 最终结论] ## 4. 延伸（可选） [更高阶理论方向] ``` ## 质量标准 - **定义精确**：不使用模糊表述，优先使用形式化语言 - **公式完备**：关键结论必须有公式支撑，不是定性描述 - **推导可验证**：每一步推导都可以独立验证 - **误区有根有据**：误区不是臆造的，必须基于真实的学习痛点 - **量级具体**：给出具体数值而非"很大""很小" - **边界明确**：说明公式/理论的适用范围和失效条件 ## 跨领域适配 不同学科领域的讲解侧重点调整： | 领域 | 侧重 | |------|------| | 物理 | 公式推导 + 极端情况 + 物理直觉 | | 数学 | 严格证明 + 反例 + 推广 | | 化学 | 分子机制 + 反应方程 + 实验现象 | | 经济 | 模型假设 + 均衡分析 + 现实偏离 | | 计算机 | 算法复杂度 + 正确性证明 + 工程权衡 | | 生物 | 机制 + 实验证据 + 进化逻辑 | ## 不适用场景 - 纯观点讨论（无明确知识点） - 超出当前学科共识的前沿问题 - 需要实时数据或实验验证的问题


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: andyrenxu7255/industry-scenario-generator (version 1.0.3)

--- name: industry-scenario-generator description: > 行业场景方案自动生成。输入行业+产品方向，自动输出行业痛点分析+典型场景用例+PPT素材MD+交流话术。 触发词：行业场景、场景方案、给客户讲场景、行业方案、场景用例。 version: 1.0.2 author: Dick Dunkel license: MIT-0 tags: [sales, scenarios, industry, ppt] category: sales created_by: agent --- # 行业场景方案自动生成 ## 触发条件 用户提到以下意图时触发： - "给我一些XX行业的场景" - "我要见XX行业的客户，准备一些场景方案" - "帮我做一份XX行业的方案" - "XX行业+ONN/IOC/EA 怎么讲" ## 必需输入 | 输入项 | 必需 | 说明 | |--------|------|------| | 行业 | ✅ | 公安/金融/高校/运营商/交通/制造/能源等 | | 产品方向 | ✅ | ONN（本体问数）/ IOC（运营中心）/ EA（企业架构）/ 数字孪生 | | 客户角色 | 推荐 | 拜访的是什么部门（科信处/数据团队/IT管理层等） | | 深度要求 | 可选 | 概览（1-2页PPT素材）/ 详细（完整方案） | ## 输出规范 ### 输出结构 #### 一、行业痛点分析（3-5个痛点） 每个痛点包含：痛点描述 / 具体表现 / 对业务的影响 #### 二、产品方案定位（1段） - 一句话价值主张 + 核心差异化 #### 三、典型场景用例（4-6个场景） 每个场景包含：场景名称 / 用户角色 / 使用场景 / 解决的问题 / 价值 #### 四、差异化优势对比表 #### 五、交流话术 - 开场白（30秒版）/ 价值锚点（3个）/ 引导性问题（3-5个） #### 六、PPT素材（如要求） - 按页输出标题、要点、配图建议 ### 输出格式 - 默认 MD 文本；如要求 PPT 则调用 document-pro 技能；如要求精简仅输出场景+话术 ## 已沉淀的行业场景库 优先引用以下已有知识文档，在此基础上补充和定制： - 公安行业：`knowleadge/02-onn/11-ps-scenarios.md` - 金融行业：`knowleadge/02-onn/10-finance-scenarios.md` - 高校/校园：`knowleadge/01-thingjs/08-ioc-phase2.md` - EA方向：`knowleadge/01-thingjs/07-ea-ai-capability.md` 对于未覆盖的行业，基于产品通用能力 + 联网搜索行业信息进行生成。 ## 质量标准 - ❌ 不输出与行业无关的通用场景 - ❌ 不编造不存在的客户案例 - ✅ 每个场景必须贴合该行业的真实业务流程 - ✅ 痛点必须是该行业特有的，不是万金油 - ✅ 话术必须可直接使用 - ✅ 如有真实案例（如友邦POC），优先引用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: githubxiaohei/openclaw-memory-fix-skill (version 1.0.0)

# OpenClaw Memory Fix Skill > 专门解决 OpenClaw 记忆问题，让 AI 像人一样思考和成长 ## 简介 这个 Skill 为 OpenClaw 提供完整的记忆系统解决方案，解决 AI "失忆" 问题，让 AI 能够： - 记住用户偏好 - 从错误中学习 - 持续进化成长 ## 解决的问题 | 问题 | 解决方案 | |------|----------| | 每次对话都是全新开始 | 三层记忆架构 | | 重复犯同样的错误 | FEEDBACK-LOG | | 不知道用户偏好 | USER.md + 语义记忆 | | 不会主动学习 | 主动学习机制 | | 效率与智能不平衡 | 分层缓存 + 动态推理 | ## 核心功能 ### 1. 四层记忆架构 - **短期**: 当前会话任务 - **情景**: 每日工作日志 - **语义**: 知识图谱 - **长期**: 精选记忆 ### 2. 11大增强功能 - 情绪识别 - 置信度评估 - 自我反思 - 任务规划 - 动态知识整合 - 隐私治理 - 可解释性 - 弹性自适应 - 持续进化 - 协作共享 - 元认知 ### 3. 7大性能优化 - 分层缓存 - 动态推理 - 上下文压缩 - 技能懒加载 - 预测性交互 - 效能监控 - 知识图谱 ## 快速安装 ```bash # 1. 复制配置到 workspace cp -r openclaw-memory-fix/config-files/* ~/.openclaw/workspace/ # 2. 重启 OpenClaw ``` ## 文件结构 ``` openclaw-memory-fix/ ├── SKILL.md # 说明文档 ├── memory-fix.json # Skill 配置 ├── config-files/ # 核心配置（19个文件） │ ├── SOUL.md # AI 身份 │ ├── USER.md # 用户信息 │ ├── MEMORY.md # 长期记忆 │ ├── NOW.md # 当前任务 │ ├── AGENTS.md # Agent 规则 │ └── ... └── scripts/ └── auto-memlog.sh # 自动记录工具 ``` ## 配置文件说明 | 文件 | 用途 | |------|------| | SOUL.md | 定义 AI 是谁、性格、行为准则 | | USER.md | 记录用户基本信息、偏好 | | MEMORY.md | 长期精选记忆 | | NOW.md | 当前任务看板 | | AGENTS.md | 多代理分工、记忆规则 | | FEEDBACK-LOG.md | 错误记录与改进 | | TRAINING.md | 边做边教机制 | ## 核心规则 ### 记忆触发规则 1. 首次对话 → 创建当日记忆 2. 完成任务 → 自动记录 3. 用户说"记住" → 立即写入 4. 每天 Heartbeat → 整理到 MEMORY ### 置信度标准 | 置信度 | 行动 | |--------|------| | 90-100% | 确定回答 | | 70-89% | 提供依据 | | 50-69% | 提供备选 | | <50% | 请求澄清 | ## 使用示例 ### 记录工作 ```bash ./auto-memlog.sh "完成任务A" "完成内容描述" ``` ### 查看记忆 ```bash cat memory/2026-03-07.md ``` ### 更新配置 编辑对应的 .md 文件即可 ## 版本 - v1.0: 基础记忆系统 - v2.0: 完整11大功能 + 7大优化 - v3.0: 元认知 + 主动学习 ## 作者 Odin（总舵主） ## 许可 免费使用


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: tonylnng/tonic-update-md (version 1.0.0)

--- name: update-md description: Create or update project documentation in Markdown. Use when building a new project doc set, updating existing docs after a deploy or feature change, or establishing a doc structure for a new project. Covers doc splitting, update rules, naming conventions, and the standard file map pattern used across all projects (TPMS, TonicLab, etc.). --- # update-md ## Core Principles 1. **Split, don't consolidate** — One file per concern. Never dump everything into one doc. 2. **Overview is always the entry point** — Every project has a `PROJECT-OVERVIEW.md` that's lean and links to everything else. 3. **按需讀取** — Detail files are only read when needed; keep them out of the overview. 4. **Update rules are mandatory** — Every doc set must define when each file should be updated. 5. **Deploy = Update docs** — Docs update immediately after every deploy. No exceptions. ## Standard Doc Set Structure Every project follows this pattern: ``` PROJECT-OVERVIEW.md ← 精簡總覽，每 session 必讀 PROJECT-DEPLOY.md ← 部署、Docker、Nginx、SSL、環境變數 PROJECT-APPS.md ← 功能規格（如有多個 app/module） PROJECT-DB.md ← 數據庫 schema 及連接設定 PROJECT-HISTORY.md ← 版本日誌，每次 deploy 後更新 ``` Add or remove files based on project complexity. See `references/doc-templates.md` for file templates. ## OVERVIEW.md Must-Haves - Project name, domain/URL, status, VM/hosting info - Feature/tool status table (✅ / 🔄 / ⏳ / ❌) - Doc map table (file → content) - **Update rules table** (change type → files to update) - Current version number ## Update Rules (include in every OVERVIEW.md) | 改動類型 | 需要更新 | |----------|----------| | 新功能 / 改功能 | OVERVIEW（狀態）+ APPS | | 新版本發布 | HISTORY + OVERVIEW（版本號）| | 部署/基礎設施改動 | DEPLOY | | DB schema 改動 | DB | ## Status Symbols Use consistently across all docs: - ✅ 完成 - 🔄 進行中 - ⏳ 待開始 - ❌ 取消 / 廢棄 ## When Updating Docs 1. Identify which files are affected by the change (use the update rules table) 2. Update only those files — don't touch unrelated docs 3. Update version number in OVERVIEW if it's a release 4. Add entry to HISTORY with date, version, and bullet points ## Creating a New Doc Set 1. Create `PROJECT-OVERVIEW.md` first — establish the doc map before writing detail files 2. Add detail files as needed (DEPLOY, APPS, DB, HISTORY) 3. Add the doc map to the project's `AGENTS.md` so agents know which files exist 4. Reference `references/doc-templates.md` for boilerplate See `references/doc-templates.md` for ready-to-use file templates.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chao-eng/cloudflare-r2-s3 (version 1.0.0)

--- name: cloudflare-r2-s3 description: Cloudflare R2 S3 兼容存储工具，支持配置 API 密钥、上传文件并获取公开访问地址。使用当需要上传文件到 Cloudflare R2 获取公开链接时。 --- # Cloudflare R2 S3 存储工具 Cloudflare R2 提供 10GB 免费存储额度，兼容 S3 API，可以用来存储文件并生成公开访问地址。 ## 功能特性 - ✅ 支持配置 S3 API 端点、访问密钥和存储桶 - ✅ 上传本地文件到 R2 存储桶 - ✅ 自动生成公开访问 URL - ✅ 支持列出存储桶中的文件 - ✅ 支持删除文件 - ✅ 兼容其他 S3 兼容存储服务（如 MinIO） ## 配置方式 在使用前需要配置以下环境变量或配置文件： ```bash # ~/.openclaw/config/cloudflare-r2.env CLOUDFLARE_R2_ACCOUNT_ID=your-account-id CLOUDFLARE_R2_ACCESS_KEY_ID=your-access-key CLOUDFLARE_R2_SECRET_ACCESS_KEY=your-secret-key CLOUDFLARE_R2_BUCKET_NAME=your-bucket-name CLOUDFLARE_R2_PUBLIC_DOMAIN=your-public-domain # 可选，自定义公开域名 ``` 或者在调用时传入参数。 ## 使用方法 ### 上传文件 ```python from scripts.r2_uploader import R2Uploader uploader = R2Uploader() result = uploader.upload_file(local_path, object_name=None, public=True) print(result['public_url']) # 获取公开访问地址 ``` ### 命令行使用 ```bash # 上传文件 python skills/cloudflare-r2-s3/scripts/r2_uploader.py upload /path/to/file.jpg [object-name] # 列出文件 python skills/cloudflare-r2-s3/scripts/r2_uploader.py list # 删除文件 python skills/cloudflare-r2-s3/scripts/r2_uploader.py delete object-name ``` ## Cloudflare R2 配置步骤 1. **创建 R2 存储桶** - 登录 Cloudflare 控制台 → R2 → 创建存储桶 - 记住存储桶名称 2. **创建 API 令牌** - R2 → 管理 R2 API 令牌 → 创建 API 令牌 - 授予 `Object Read + Write` 权限 - 保存 Access Key ID 和 Secret Access Key 3. **配置公开访问**（可选但推荐） - 在存储桶设置 → "Public Access" → 启用 - 可以使用 Cloudflare 提供的 `pub-${bucket-name}.r2.dev` 域名 - 或者绑定自定义域名 ## 依赖 需要安装 boto3: ```bash pip install boto3 ``` ## 脚本说明 - `scripts/r2_uploader.py` - 主上传工具，支持命令行和 API 调用 - `references/config_example.env` - 配置文件示例


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: oliviapp8/video-script-generator (version 1.0.0)

--- name: video-script-generator description: 生成短视频脚本和分镜。输入产品/主题描述、目标受众、时长，输出完整脚本+分镜结构（JSON/YAML）。支持多种模板：痛点-解决、反转剧情、before-after。触发词：视频脚本、短视频文案、分镜、storyboard、video script。 --- # 短视频脚本+分镜生成 ## 工作流程 1. 收集输入信息 2. 选择模板风格 3. 生成脚本+分镜结构 4. 输出结构化数据供下游消费 ## 输入参数 | 参数 | 必填 | 说明 | |------|------|------| | topic | ✓ | 产品/主题描述 | | audience | ✓ | 目标受众 | | duration | ✓ | 时长：15s / 30s / 60s / 90s | | template | - | 模板：pain-solution / before-after / plot-twist | | platform | - | 平台：douyin / xiaohongshu / youtube-shorts | | tone | - | 语气：professional / casual / humorous / dramatic | ## 模板说明 ### pain-solution（默认） 痛点引入 → 解决方案 → 效果展示 → CTA ### before-after 对比前的状态 → 转折点 → 对比后的状态 → CTA ### plot-twist 正常叙事 → 意外反转 → 揭示真相 → CTA 详见 [templates/](templates/) 目录。 ## 输出格式 ```yaml meta: title: "脚本标题" duration: 30s platform: douyin template: pain-solution script: hook: "开场钩子文案" scenes: - id: 1 type: hook # hook/pain/solution/demo/cta duration: 3s narration: "旁白/台词" shot_description: "镜头描述：特写/中景/全景，动作，表情" visual_style: "画面风格提示" demo_insert: false - id: 2 type: pain duration: 5s narration: "..." shot_description: "..." demo_insert: false - id: 3 type: solution duration: 8s narration: "..." shot_description: "产品界面展示" demo_insert: true demo_action: "点击生成按钮，展示结果" - id: 4 type: cta duration: 3s narration: "链接在评论区" shot_description: "logo + 二维码" demo_insert: false cta: "链接在评论区 / 点击头像私信" music_suggestion: "轻快节奏 / 紧张悬疑 / 温馨治愈" ``` ## 场景类型 | type | 用途 | 典型时长 | |------|------|----------| | hook | 开场吸引 | 2-3s | | pain | 痛点展示 | 3-5s | | solution | 解决方案 | 5-10s | | demo | 产品演示 | 5-15s | | result | 效果展示 | 3-5s | | twist | 反转 | 3-5s | | cta | 行动号召 | 2-3s | ## 时长分配参考 | 总时长 | 场景数 | 分配建议 | |--------|--------|----------| | 15s | 3-4 | hook(2) + core(10) + cta(3) | | 30s | 4-6 | hook(3) + pain(5) + solution(15) + cta(3) | | 60s | 6-8 | hook(3) + pain(8) + solution(35) + result(10) + cta(4) | ## 使用示例 用户：帮我写一个30秒的短视频脚本，推广我的AI写作工具，目标用户是自媒体博主 执行： 1. 确认参数：topic=AI写作工具, audience=自媒体博主, duration=30s 2. 选择模板：pain-solution（默认） 3. 生成脚本+分镜 4. 输出 YAML 结构 ## 下游对接 输出的结构化数据可直接用于： - `digital-avatar` skill：提取 narration 生成口播 - `scene-video-generator` skill：按 shot_description 生成画面 - `video-stitcher` skill：按 duration 拼接


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gabriel-kaufman/skill-traction-notifier (version 1.0.0)

--- name: Skill Surge Notifier slug: skill-surge-notifier version: 0.1.0 author: "@gkauf_gm" tags: [monitoring, clawhub, acceleration, meta] homepage: https://github.com/Gabriel-Kaufman/skill-scraper/tree/main metadata: {"clawdbot":{"emoji":"📈","requires":{"bins":["node"]}}} --- # Skill Surge Notifier Monitors ClawHub for skills gaining traction. Tracks download and star counts over time and alerts when a skill is surging. A surge is triggered when any of these are true: - Download growth >30% since last check - Total downloads >50,000 - Stars >200 - A new skill appeared in the top 10 --- ## Setup Requires Node.js 18+. No install needed — runs directly from the skill bundle. --- ## Commands | Command | What it does | |---|---| | `node {baseDir}/cli.js fetch` | Show top 20 skills by downloads | | `node {baseDir}/cli.js check` | Run surge detection, top movers, and update state | | `node {baseDir}/cli.js status` | Last check, thresholds, notification status | | `node {baseDir}/cli.js profile` | Show current agent profile | | `node {baseDir}/cli.js profile set "description" "kw1,kw2"` | Set profile for relevance scoring | | `node {baseDir}/cli.js config movers=5` | Set number of top movers shown | | `node {baseDir}/cli.js config movers-off` | Disable top movers | | `node {baseDir}/cli.js config growth=30 downloads=50000 stars=200` | Update surge thresholds | Every `check` run always shows top N movers (by download delta) regardless of thresholds, so there's always something to look at. When a profile is set, surges are scored 0-10 for relevance and sorted accordingly — most relevant first. --- ## Notes - State is stored in `~/.skill-surge-notifier/state.json`. The first run seeds the baseline; growth % appears from the second run onward. - All output is printed to stdout. When used inside an agent, the agent must capture the CLI output to surface alerts in chat. - To run automatically, add to crontab (`crontab -e`): ```bash 0 */4 * * * node {baseDir}/cli.js check >> ~/.skill-surge-notifier/surge.log 2>&1 ``` ## Environment Variables These optional env vars override default paths and behavior: | Variable | Default | Description | |---|---|---| | `SURGE_DIR` | `~/.skill-surge-notifier` | Base directory for all state and config files | | `STATE_PATH` | `$SURGE_DIR/state.json` | Path to the skill state file | | `CONFIG_PATH` | `$SURGE_DIR/config.json` | Path to the config file | | `SCHEDULED` | `false` | Set to `true` when running from a scheduler; adds a random 1–5 min delay before fetching to spread API load |

path: types.js
content: export {};


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: honestqiao/performance-profiler (version 1.0.0)

# Performance Profiler 分析代码性能，识别瓶颈并提供优化建议。 ## 功能 - 循环性能分析 - 重复计算检测 - 同步阻塞识别 - 复杂度计算 - 优化建议生成 ## 触发词 - "性能分析" - "性能瓶颈" - "performance" - "优化建议" ## 检测问题 - 循环内数组操作 - 重复的函数调用 - 同步阻塞操作 - 内存泄漏风险 ## 输出示例 ```json { "issues": [ { "type": "loop_push", "location": "line 42", "suggestion": "使用数组推导式或预先分配" } ], "complexity": "O(n²)", "score": 75 } ```

path: skill.yaml
content: # Skill: performance_profiler

analyze_performance(code: string) => object

## Trigger Patterns
- 性能分析
- 性能瓶颈
- performance
- 优化建议

## Implementation

分析代码性能，识别瓶颈并提供优化建议。

```javascript
function analyzePerformance(code) {
  const issues = [];
  
  // 检测循环内操作
  if (/for\s*\(.*\[\]\.push|while.*\.push/.test(code)) {
    issues.push({ type: 'loop_push', suggestion: '使用数组推导式或预先分配' });
  }
  
  // 检测重复计算
  if (/\.length\s*\(.*\)\.length/.test(code)) {
    issues.push({ type: 'redundant_calc', suggestion: '缓存长度值' });
  }
  
  // 检测同步阻塞
  if (/fs\.readFileSync|await.*sleep/.test(code)) {
    issues.push({ type: 'sync_block', suggestion: '考虑异步或流处理' });
  }
  
  return { issues, complexity: calculateComplexity(code) };
}
```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: brucegutman/pipeworx-swapi (version 1.0.0)

# Star Wars API A long time ago in a galaxy far, far away... all the data from the Star Wars universe became available via MCP. Search for characters, look up planets, inspect starships, and get film details from the original six films. ## Tools **search_people** -- Search characters by name. Try "Luke", "Darth", or "Leia". Returns physical attributes, birth year, gender, and homeworld. **get_planet** -- Planet by ID. Tatooine is 1. Returns climate, terrain, population, gravity, orbital data. **get_starship** -- Starship by ID. The Millennium Falcon is 10, Death Star is 9. Returns model, manufacturer, crew capacity, cargo, hyperdrive rating, and speed. **get_film** -- Film by ID. A New Hope is 1. Returns title, episode number, director, producer, release date, and the full opening crawl. ## Example: look up Tatooine ```bash curl -X POST https://gateway.pipeworx.io/swapi/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_planet","arguments":{"id":1}}}' ``` ## Quick reference | Thing | ID | |-------|-----| | Luke Skywalker | search "Luke" | | Tatooine | Planet 1 | | Millennium Falcon | Starship 10 | | Death Star | Starship 9 | | A New Hope | Film 1 | | The Empire Strikes Back | Film 2 | ```json { "mcpServers": { "swapi": { "url": "https://gateway.pipeworx.io/swapi/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangifonly/clawnote (version 0.1.1)

--- name: clawnote description: Review-first Xiaohongshu/Rednote content ops for OpenClaw. Use when creating a repeatable workflow for topic research, draft generation, Feishu review, exact-title approval, structured publish packages, and optional approved publishing after explicit confirmation. --- # Clawnote Use this skill when the goal is to build or run a Xiaohongshu content workflow inside OpenClaw. This skill is for: - daily AI news + OpenClaw practice drafts - Feishu-first review flow - Xiaohongshu-style rewriting - exact-title approval before publish - memory + cron driven content ops ## What To Read Start with: - `workspace-template/AGENTS.md` - `workspace-template/SOUL.md` - `workspace-template/TOOLS.md` Read these when needed: - `workspace-template/PUBLISH_ASSIST.md` for publish rules - `workspace-template/FEISHU_COMMANDS.md` for user command patterns - `docs/SAFETY.md` for risk boundaries ## Workflow Follow this order: 1. `xhs-research` 2. `xhs-draft` 3. `xhs-publish-assist` Default mode is review-first, not auto-publish. ## Bundled Scripts Use these scripts instead of rewriting them: - `workspace-template/scripts/save_publish_package.py` - `workspace-template/scripts/build_publish_package.py` - `workspace-template/scripts/publish_approved_note.py` - `workspace-template/scripts/write_memory_entry.py` ## Important Safety Rules - Do not publish without explicit user approval. - Treat deletion as a separate high-risk action. - Do not assume archive-to-draft works unless it has been re-verified for the current Xiaohongshu UI. - Prefer exact-match title confirmation over fuzzy matching.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ohmaxy/mxy-web-search (version 1.0.0)

--- name: ddg-search description: Web search without an API key using DuckDuckGo Lite via web_fetch. Use as a fallback when web_search fails with missing_brave_api_key error, or whenever you need to search the web and no search API is configured. Provides titles, URLs, and snippets for research queries. Zero dependencies — works with just the built-in web_fetch tool. --- # DuckDuckGo Search via web_fetch Search the web using DuckDuckGo Lite's HTML interface, parsed via `web_fetch`. No API key or package install required. ## How to Search ``` web_fetch(url="https://lite.duckduckgo.com/lite/?q=QUERY", extractMode="text", maxChars=8000) ``` - URL-encode the query — use `+` for spaces - Use `extractMode="text"` (not markdown) for clean results - Increase `maxChars` for more results ## Region Filtering Append `&kl=REGION` for regional results: - `au-en` — Australia - `us-en` — United States - `uk-en` — United Kingdom - `de-de` — Germany - `fr-fr` — France Full list: https://duckduckgo.com/params ### Example — Australian search ``` web_fetch(url="https://lite.duckduckgo.com/lite/?q=best+coffee+melbourne&kl=au-en", extractMode="text", maxChars=8000) ``` ## Reading Results Results appear as numbered items with title, snippet, and URL. Skip entries marked "Sponsored link" (ads) — organic results follow. ## Search-then-Fetch Pattern 1. **Search** — query DDG Lite for a list of results 2. **Pick** — identify the most relevant URLs 3. **Fetch** — use `web_fetch` on those URLs to read full content ## Tips - First 1-2 results may be ads — skip to organic results - For exact phrases, wrap in quotes: `q=%22exact+phrase%22` - Add specific terms to narrow results (site name, year, location) ## Limitations - No time/date filtering (DDG Lite doesn't support `&df=` reliably via fetch) - Text results only — no images or videos - Results sourced from Bing (may differ from Google) - Google search does NOT work via web_fetch (captcha blocked)

path: _meta.json
content: ownerId: kn74ca7cqvsfmq67r6x9m1nc45816qe9
slug: mxy-web-search
version: 1.0.0
publishedAt: 1771148054829

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: drivenbymyai-max/weather-demand (version 1.0.0)

--- name: weather-demand description: Weather-driven demand forecasting — correlate temperature with energy/commodity trade flows, Holt-Winters predictions user-invocable: true metadata: {"openclaw":{"requires":{"env":[]}},"homepage":"https://sputnikx.xyz","author":"SputnikX","version":"1.0.0","tags":["weather","forecast","demand","energy","commodities"]} --- # Weather-Demand Forecasting Correlate Baltic weather data with commodity trade flows. Holt-Winters time series forecasting. Temperature → energy demand predictions. ## Base URL `https://sputnikx.xyz/api/v1/agent` ## Demand Forecast ($0.10 x402) ```bash curl "https://sputnikx.xyz/api/v1/agent/trade/demand-forecast?reporter=LV&hs2=27&months=6" ``` Returns: Holt-Winters forecast with confidence intervals for commodity imports. ## Weather + Trade Correlation ```bash curl "https://sputnikx.xyz/api/v1/agent/trade/seasonality?reporter=LV&hs2=27" ``` Returns: Monthly patterns showing temperature-trade correlation. ## Use Cases - Predict heating fuel demand based on weather forecast - Anticipate commodity import spikes before they happen - Plan inventory based on seasonal weather patterns - Analyze climate impact on Baltic trade flows


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