OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nihaoxuan2026/token-decision (version 2.0.0)

大模型Token用量及效果实时决策 v2.0 输入 prompt，自动推荐最优模型并直接执行 比价 + 评估 + ROI + TokenPlan换算 多维加权评分 | 上下文自动决策 | 故障转移 | 预算追踪 用法：加 `/token决策`，或说"推荐模型/最便宜/性价比"自动触发 开发者：乙春 自动触发：当用户说"推荐模型/最便宜/性价比/质量高/用哪个好/划算/对比"时，自动调用。 ## 核心能力 1. 任务分类 → 8类（写作/编程/翻译/分析/知识问答/总结摘要/数学推理/闲聊） - 关键词匹配 + 置信度评估（≥3高/1-2中/0未知 fallback） - 未知 prompt 自动语义 fallback 分类 2. 多维加权评分 → 每类任务 5-6 个维度独立权重 - 编程重 efficiency(30%)，写作重 conciseness(30%)，数学重 accuracy(50%) 3. 模型对比 → 9款国产模型实测数据，质量+成本+延迟 4. 上下文自动决策 → 长文本(>2000字符)自动推荐大窗口模型 5. 故障转移 → API调用失败自动切换备选模型（最多2次） 6. 省钱计算 → 比次优方案省了多少一目了然 7. 计费透明 → Token Plan 积分消耗换算 8. 预算追踪 → 今日累计消耗显示 9. 聚类进化 → cluster.py 自动发现新关键词并更新分类规则 10. 直接执行 → 推荐即调用模型 API，返回结果 ## 版本改进 (v1.0 → v2.0) | 改进项 | 说明 | |--------|------| | 智能分类 | 关键词 + 置信度 + fallback，不再一律兜底"闲聊" | | 多维评分 | 8类任务 × 5-6维独立权重，替代单一 overall 公式 | | 上下文决策 | 长文本自动切换大窗口模型，不再仅 ⚠️ 提示 | | 故障转移 | API失败自动回退备选模型，最多3次尝试 | | Token估算 | tiktoken 准确编码，替代 char×1.5 粗糙估算 | | 预算追踪 | 每次推荐显示今日消耗 | | 累计报表 | stats.py 新增今日/本周/累计节省统计 | | 自动进化 | cluster.py --update 自动更新关键词 | ## 输出示例 ``` ======================================================= 大模型Token用量及效果实时决策 v2.0 开发：乙春 | 数据驱动 · 多维评分 · 智能路由 ======================================================= 任务识别: 写作 | 模式: 均衡 🎯 推荐: deepseek-v3.2 (🏆 综合最优) 加权质量: 93.5/100 | 预估: 1250token | ¥0.000462 📊 备选模型（同一任务实测数据） 模型 质量 成本¥ 延迟 评价 ------------------------------------------------------- deepseek-v3.2 93.5 0.000462 3531ms 🏆推荐 deepseek-v4-flash 92.0 0.000839 0ms 长窗 hy3-preview 89.0 0.000000 0ms 免费 💰 比 deepseek-v4-flash 省 ¥0.000377 🪙 Token Plan 消耗估算: 基础token: 1250 × 计费系数1.3 = 1625 积分 参考: Token Plan 40元/月约可调用 246 次此类任务 📊 今日消耗 ¥0.001234 ======================================================= ``` ## 依赖 - Python 3.x + openai + python-dotenv + tiktoken - `benchmark.db` (9 模型 × 8 类实测) - `.env` (API Key，仅执行时需要) ## 数据来源 - 9 款国产大模型实测 benchmark - Deepseek-V4-Pro 在线 8 维评分（accuracy/completeness/format_score/creativity/readability/conciseness/relevance/efficiency） - tiktoken o200k_base 编码器（GPT-4o 通用编码） ## 文件结构 ``` token-decision/ SKILL.md # 本文件 token_eval.py # 主程序：分类+评分+推荐+执行+预算 cluster.py # 聚类分析+关键词自动更新 stats.py # 使用统计面板（含累计节省） benchmark.db # 9模型×8类实测数据库 .env # API Keys（不纳入版本管理） ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: aisadocs/aisa-youtube-serp-scout-aisa-one (version 1.0.2)

--- name: aisa-youtube-serp-scout description: 'Search YouTube videos, channels, and trends through the AIsa YouTube SERP client. Use when the user asks for content research, competitor tracking, or trend discovery without managing Google credentials. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.' author: AIsa version: 1.0.2 license: MIT-0 homepage: https://aisa.one source: https://github.com/baofeng-tech/agent-skills-io/tree/main/targetSkills/aisa-youtube-serp-scout user-invocable: true primaryEnv: AISA_API_KEY requires: bins: - python3 env: - AISA_API_KEY metadata: aisa: emoji: ▶️ requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY compatibility: - openclaw - claude-code - hermes openclaw: emoji: ▶️ requires: bins: - python3 env: - AISA_API_KEY primaryEnv: AISA_API_KEY --- # AIsa YouTube SERP Scout Search YouTube videos, channels, and trends through the AIsa relay for content research, competitor tracking, and trend discovery. ## When to use - The user wants YouTube content research, channel discovery, or trend monitoring. - The workflow benefits from a bundled Python client for repeated searches. - The task can use `AISA_API_KEY` instead of direct Google API credentials. ## When NOT to use - The user needs browser automation, local scraping, or account-level YouTube actions. - The workflow must avoid sending search requests to `api.aisa.one`. - The request depends on files outside this package. ## Quick Reference - Required environment variable: `AISA_API_KEY` - Endpoint: `https://api.aisa.one/apis/v1/youtube/search` - Python client: `scripts/youtube_client.py` ## Setup ```bash export AISA_API_KEY="your-key" ``` ## Common Commands ```bash curl "https://api.aisa.one/apis/v1/youtube/search?engine=youtube&q=AI+agents+tutorial" \ -H "Authorization: Bearer $AISA_API_KEY" python3 scripts/youtube_client.py search --query "AI agents tutorial" python3 scripts/youtube_client.py search --query "machine learning" --country us python3 scripts/youtube_client.py competitor --name "OpenAI" --topic "GPT tutorial" ``` ## Capabilities - Search videos, channels, and playlists with `q` - Filter by country with `gl` and language with `hl` - Reuse `sp` tokens for pagination or SERP narrowing - Run competitor and top-video research from the bundled Python client ## Guardrails - Do not ask for Google credentials or browser cookies. - Do not claim competitor analysis succeeded before the client returns data. - Do not assume missing locale values when the user needs a specific market. ## Security Notes - All search requests go to `api.aisa.one`. - Required secret: `AISA_API_KEY`. - This package does not include browser automation, local scraping, or account actions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bajie-git/ohyesai-mv (version 0.1.1)

--- name: ohyesai-mv description: Generate MV (Music Video) with AI-driven storyboarding, image generation, and video synthesis. homepage: https://ohyesai.com metadata: {"clawdbot":{"emoji":"🎬","requires":{"bins":["curl","sleep"],"env":["OHEYSAI_API_KEY"]}}} --- # Skill: 音乐视频 (MV) 生成器 (MV Generator) ## 1. 技能描述 该技能允许用户上传音频并将其转化为具有特定视觉风格（如动漫、写实）和指定比例（横屏/竖屏）的音乐视频（MV）。 ## 2. 预置条件与配置 - **API Key 获取**: 引导用户前往 [https://ohyesai.com](https://ohyesai.com?from=https://clawhub.ai) 获取。 - **环境变量**: 必须配置 `OHEYSAI_API_KEY`。 - **文件限制**: 音频文件大小不得超过 **50MB**。 ## 3. 核心参数 在启动流程前，需确保获得以下参数： - `audioFile`: 用户上传的音频原始文件。 - `style`: 视频的视觉风格（如：动漫风格、赛博朋克、唯美古风等）。 - `aspectRatio`: 画面比例，仅限以下两个值： - `LANDSCAPE`: 横屏 (16:9) - `PORTRAIT`: 竖屏 (9:16) --- ## 4. 接口定义 (API Reference) ### 4.1 上传音频接口 (Upload) - **URL**: `POST https://ohyesai.com/ohyesai-next/api/vio/skill/upload?[REDACTED_SECRET]` - **Payload**: `multipart/form-data` (字段名: `file`) - **功能**: 将本地音频转换为 `audioFileId`。 ### 4.2 提交任务接口 (Submit) - **URL**: `POST https://ohyesai.com/ohyesai-next/api/vio/skill/mv-submit?[REDACTED_SECRET]` - **Request Body**: ```json { "audioFileId": "string", "style": "string", "aspectRatio": "LANDSCAPE | PORTRAIT" } ``` - **Response**: 返回 `taskId` (String)。 ### 4.3 查询任务接口 (Query) - **URL**: `GET https://ohyesai.com/ohyesai-next/api/vio/skill/mv-query?[REDACTED_SECRET]` - **Response Structure**: ```json { "code": 0, "data": { "state": "SUCCESS | RUNNING | FAIL", "data": "视频下载URL(仅SUCCESS时存在)" } } ``` --- ## 5. 执行流程 (Workflow) ### 第一阶段：准备与校验 1. **文件校验**: 接收用户音频文件，检查大小。若超过 50MB，提示用户压缩后重新上传。 2. **获取 FileId**: 调用 **上传音频接口**。如果失败，告知用户服务器连接异常。 ### 第二阶段：参数确认 1. 如果用户未指定 `style`，主动询问：“您希望视频是什么风格？（例如：动漫、写实、3D、水墨等）”。 2. 如果用户未指定 `aspectRatio`，询问：“您需要横屏(LANDSCAPE)还是竖屏(PORTRAIT)？” ### 第三阶段：提交与轮询 1. **提交**: 调用 **提交任务接口**，记录返回的 `taskId`。 2. **轮询**: 初始等待 10 秒，随后每隔 5-10 秒调用一次 **查询任务接口**。 - **RUNNING**: 继续等待，并向用户发送进度反馈（如：“正在为您精心制作视频，请稍候...”）。 - **FAIL**: 停止轮询，告知用户任务失败，并尝试分析原因。 - **SUCCESS**: 提取 `data.data` 中的视频链接，并以预览卡片或链接形式发送给用户。 --- ## 6. 异常与边界处理 - **API Key 缺失**: 若环境变量未配置，引导用户前往官网。 - **格式错误**: 若上传非音频文件，拦截并提示。 - **超时处理**: 若轮询超过 10 分钟仍为 `RUNNING`，提示用户记录 `taskId` 稍后手动查询。 ## 7. 交互示例 > **User**: 帮我把这首歌做成一个动漫风格的竖屏视频。 [附件: song.mp3] > > **Agent**: > 1. (Internal) 检查 song.mp3 大小 (<50MB)。 > 2. (Internal) 调用 `/upload` 获取 `audioFileId`。 > 3. (Internal) 调用 `/mv-submit` 发起任务，获得 `taskId: "f7fb7..."`。 > 4. (Output) 已收到您的请求！正在为您生成动漫风格的竖屏 MV，这可能需要一点时间，我会在这里通知您进度。 > 5. (Internal/Loop) 调用 `/mv-query`，状态为 `RUNNING`。 > 6. (Internal/Loop) 调用 `/mv-query`，状态变为 `SUCCESS`。 > 7. (Output) 视频制作完成！您可以点击下方链接查看或下载：[视频链接] ## 联系客服 ![联系客服](https://ohyesainext-public.tos-cn-beijing.volces.com/openclawCstomerServiceQr.jpeg) ![联系客服](https://ohyesainext-public.tos-cn-beijing.volces.com/customerServiceQrCode.jpeg)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cesco345/capneteq-cesco345 (version 1.0.0)

functions/src/mcp/openclaw-skill/SKILL.md # Capital Equipment Platform Find, book, and manage scientific research equipment through your AI assistant. Search 10,000+ instruments across 500+ core facilities at 200+ institutions. ## What This Skill Does - **Search Equipment**: Find microscopes, spectrometers, sequencers near any location - **Check Availability**: Real-time booking windows for shared instruments - **Book Equipment**: Reserve time slots directly through your assistant - **Submit Service Requests**: Describe needs and get quotes from providers - **Find Collaborators**: Discover researchers by expertise in specific techniques - **Track Papers**: Find published research linked to specific instruments - **Compatibility Check**: Verify equipment combinations for workflows - **Price Intelligence**: Fair market values, institutional rates, depreciation - **Compliance**: IP agreement and safety requirements for cross-institutional use ## Authentication This skill uses OAuth 2.1 for secure access via the standard MCP OAuth flow: 1. On first use, you will be redirected to the Capital Equipment sign-in page 2. Sign in with your institutional credentials (Google, Microsoft, or email) 3. Authorize the AI assistant to act on your behalf 4. The OAuth token is managed automatically - no API keys needed **Required Scopes**: `equipment:read`, `equipment:book`, `service-requests:write`, `profile:read` **Token Lifetime**: 1 hour (refresh tokens issued automatically) **No credentials need to be stored in your OpenClaw configuration.** ## Setup Add the MCP server to your OpenClaw configuration: ```json { "mcp": { "servers": [ { "name": "capneteq-cesco345", "type": "sse", "url": "[https://capneteq.com/mcpServer/mcp](https://capneteq.com/mcpServer/mcp)" } ] } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: playgod2023/clawhub-skill-explorer (version 1.1.0)

--- name: clawhub-skill-explorer version: 1.0.0 description: ClawHub技能探索和导航工具。帮助用户快速找到所需的技能，支持关键词搜索和分类浏览。 author: OpenClaw Team --- # ClawHub技能探索和导航工具 ## 技能概述 ClawHub技能探索和导航工具是一个专门为ClawHub平台设计的技能发现工具。它允许用户通过关键词搜索和分类浏览快速找到所需的技能，提高了技能发现的效率。 ## 主要功能 ### 🔍 **技能搜索** - 支持关键词搜索技能 - 搜索结果支持按相关性排序 - 搜索历史记录 ### 📂 **技能分类** - 按技能类别分类显示 - 支持技能标签筛选 - 推荐相关技能 ### 🏷️ **标签系统** - 支持多种技能标签 - 标签云可视化 - 标签搜索功能 ### 📊 **技能详情** - 查看技能详细信息 - 技能安装和使用指南 - 技能版本历史 ### 🌟 **收藏功能** - 支持收藏感兴趣的技能 - 管理个人技能收藏 - 分享收藏的技能 ## 架构设计 ### 📦 **核心组件** 1. **搜索模块**：处理技能搜索逻辑 2. **分类模块**：管理技能分类和标签 3. **详情模块**：显示技能详细信息 4. **收藏模块**：处理用户收藏功能 ### 🛠️ **技术栈** - **后端**：Node.js/Express - **前端**：React.js - **数据库**：MongoDB - **API**：ClawHub公开API ### 🚀 **部署架构** - **云服务**：AWS/Azure/GCP - **容器化**：Docker - **CI/CD**：GitHub Actions ## 使用方法 ### 安装技能 ```bash clawhub install clawhub-skill-explorer ``` ### 常用命令 #### 搜索技能 ```bash clawhub-skill-explorer search --query "problem solving" ``` #### 浏览分类 ```bash clawhub-skill-explorer browse --category "productivity" ``` #### 查看技能详情 ```bash clawhub-skill-explorer view --slug "clawhub-search-verify" ``` #### 收藏技能 ```bash clawhub-skill-explorer favorite --slug "clawhub-search-verify" ``` ## 开发计划 ### 第一阶段 (已完成) - ✅ 项目结构设计 - ✅ 核心功能规划 - ✅ 用户界面设计 ### 第二阶段 (开发中) - 🔄 技能搜索功能开发 - 🔄 分类和标签系统 - 🔄 详情页面开发 ### 第三阶段 (待开始) - 🔄 收藏功能开发 - 🔄 用户界面优化 - 🔄 性能优化 ## 技术特点 ### 🔒 **安全性** - API密钥管理 - 搜索结果过滤 - 用户数据保护 ### 📈 **性能优化** - 搜索结果缓存 - 图片懒加载 - 代码优化 ### 🌐 **可访问性** - 响应式设计 - 无障碍支持 - 多语言支持 ## 贡献指南 欢迎开发者提交问题和改进建议。如果您有好的想法，请通过以下方式贡献： 1. **提交问题**：在GitHub仓库创建Issue 2. **发送拉取请求**：创建功能分支并发送PR 3. **撰写文档**：完善技能文档和教程 ## 许可证 MIT License - 详见LICENSE文件 --- **ClawHub技能探索和导航工具** - 让技能发现更简单，让工作更高效！


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: 0x008/cis-openeuler-audit (version 1.0.0)

--- name: cis-openeuler-audit description: "审计 OpenEuler 系统与对应 RHEL CIS Benchmark 的合规差异，生成合规报告。" --- # CIS Benchmark 合规审计 — OpenEuler 针对 OpenEuler 系统，通过版本兼容矩阵匹配对应 RHEL 版本，对照其 CIS Benchmark 进行合规审计，输出差异分析报告。 ## 使用方式 ``` 老大，审计 OpenEuler 合规情况 老大，运行 CIS 审计 老大，检查这台机器的安全基线 ``` ## 工作流 1. **确定目标系统** — 确认待审计的 OpenEuler 主机及版本 2. **匹配 RHEL 基准** — 查 `references/version-matrix.md` 确定对应 CIS Benchmark 版本 3. **收集基线** — 在目标系统上运行 `scripts/collect-baseline.sh`，生成带时间戳的基线快照 4. **差异分析** — 运行 `scripts/diff-analysis.py`，比对基线结果与 CIS Benchmark 映射表中的检查项 5. **输出报告** — 生成 Markdown 格式的合规报告（通过/失败/手动验证），保存在 `reports/` 目录 ## 文件结构 ``` cis-openeuler-audit/ ├── SKILL.md # 本文件 — skill 定义与工作流 ├── scripts/ │ ├── collect-baseline.sh # 在 OpenEuler 主机上收集系统基线 │ └── diff-analysis.py # 基线 vs CIS Benchmark 差异分析 ├── references/ │ ├── version-matrix.md # OpenEuler ↔ RHEL 版本兼容矩阵 │ └── cis-rhel-benchmark-mapping.md # CIS Benchmark 检查项 ↔ OpenEuler 等效检查 └── reports/ # 审计报告输出目录（自动生成） ``` ## 设计原则 - **以脚本为主导** — SKILL.md 定义流程，具体逻辑交给脚本。模型无关。 - **配置驱动** — 映射表、版本矩阵、例外规则在 `references/` 中，不硬编码。 - **幂等执行** — 每次生成带时间戳的报告，不修改目标系统配置，只做审计。 - **增量更新** — 已有基线时，只分析有变动的部分（diff 上一次基线文件）。 ## 自动化触发 | 场景 | 触发方式 | |------|----------| | 一次性审计 | 手动触发（通过 QQ/Signal 指令） | | 定时巡检 | 配合 cron 定期执行 | | CI/CD 集成 | 新节点上线后自动执行 | ## 安全注意事项 - 基线收集脚本使用 `sudo`，需确保执行用户有相应权限 - 审计报告可能包含敏感配置信息，存储时注意权限控制 - 不在未获得授权的主机上执行审计


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: michealxie001/oc-dream-journal (version 1.1.1)

--- name: dream-journal description: 梦境记录、学习与分析工具。当用户提及梦境相关内容时，自动提取并学习新的梦境意象，进行文学化修饰后按日期记录。支持意象库自动扩充、关键词搜索、统计分析。从零开始，越用越懂你的梦。 --- # Dream Journal - 梦日记 🌙 ## 核心特点 **动态学习，越用越聪明** 不像传统的静态词典，这个 skill 从一个极简的种子意象库开始，通过分析你的每一个梦境，自动学习新的意象词汇，持续丰富属于你自己的梦境语言体系。 ## 功能 1. **自动识别梦境**：检测触发词，自动记录 2. **动态意象学习**：从梦境中提取新词汇，自动分类 3. **文学修饰**：为每个梦境生成诗意标题 4. **意象分析**：统计各类意象出现频率 5. **梦境搜索**：关键词、意象、日期搜索 6. **持续成长**：意象库随使用不断扩充 ## 触发词 自动记录触发： - "我做了个梦" / "昨晚梦见" / "梦里" / "我梦见" / "做梦梦到" 手动查询： - "查看梦日记" / "分析梦境意象" / "搜索梦境 [关键词]" ## 8大意象类别 | 类别 | 说明 | |------|------| | 自然元素 | 水、火、风、山、天空等 | | 动物 | 蛇、鸟、鱼、猫、狗等 | | 建筑与空间 | 房子、门、路、桥、楼梯等 | | 人物 | 母亲、父亲、陌生人等 | | 身体与感知 | 手、眼睛、心、坠落、飞翔等 | | 物品与象征 | 书、钥匙、镜子、车等 | | 情绪与场景 | 逃跑、黑暗、光明等 | | 抽象概念 | 其他难以归类的意象 | ## 动态学习机制 ### 种子阶段 开始时只有约 30 个基础意象词汇作为"种子"。 ### 学习阶段 每次记录梦境时： 1. 识别已知的意象 2. 从内容中提取新词汇 3. 通过智能规则自动分类 4. 保存到你的个人意象库 ### 成长展示 ``` 自然元素: 7基础 + 23学习 = 30个 动物: 6基础 + 15学习 = 21个 ... 当前意象库规模: 127个词汇 ``` ## 输出文件 ``` memory/ ├── dreams.md # 梦日记（按日期倒序） ├── dream_imagery_db.json # 意象数据库 ├── user_imagery.json # 用户学习到的意象 └── dream_stats.json # 统计数据 ``` ## 使用示例 ### 记录梦境 > 用户：昨晚我做了个梦，梦见自己在一个紫色的水晶洞穴里，周围漂浮着发光的蝴蝶... 系统识别后： - 提取已知意象：光、蝴蝶 - 学习新意象：水晶、洞穴、紫色 - 生成标题：在水晶与蝴蝶之间 - 记录到 dreams.md ### 查看统计 > 用户：分析我的梦境意象 输出： ``` 总共记录: 15 个梦境 意象库规模: 127 个词汇 常见意象: • 水: 8 次 • 飞翔: 5 次 • 紫色: 3 次 ``` ### 搜索梦境 > 用户：搜索梦境 飞翔 返回所有包含"飞翔"意象的梦境记录。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sabatesduran/clawdbot-meshyai-skill (version 0.1.0)

--- name: meshy-ai description: "Use the Meshy.ai REST API to generate assets: (1) text-to-2d (Meshy Text to Image) and (2) image-to-3d, then download outputs locally. Use when the user wants Meshy generations, needs polling async tasks, and especially when they want the resulting OBJ saved to disk. Requires MESHY_API_KEY in the environment." --- # Meshy.ai Generate Meshy assets via API and save outputs locally. ## Setup - Add env var: `MESHY_API_KEY=msy-...` - Optional: `MESHY_BASE_URL` (defaults to `https://api.meshy.ai`) ## Text → 2D (Text to Image) Use `scripts/text_to_image.py`. ```bash python3 skills/public/meshy-ai/scripts/text_to_image.py \ --prompt "a cute robot mascot, flat vector style" \ --out-dir ./meshy-out ``` - Downloads one or more images (if multi-view) into `./meshy-out/text-to-image_<taskId>_<slug>/`. ## Image → 3D (always save OBJ) Use `scripts/image_to_3d_obj.py`. ### Local image ```bash python3 skills/public/meshy-ai/scripts/image_to_3d_obj.py \ --image ./input.png \ --out-dir ./meshy-out ``` ### Public URL ```bash python3 skills/public/meshy-ai/scripts/image_to_3d_obj.py \ --image-url "https://.../input.png" \ --out-dir ./meshy-out ``` - Always downloads `model.obj` (and `model.mtl` if provided by Meshy) into `./meshy-out/image-to-3d_<taskId>_<slug>/`. ## Notes - Meshy tasks are async: create → poll until `status=SUCCEEDED` → download URLs. - API reference for this skill: `references/api-notes.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: goog/more-news (version 1.1.4)

--- name: more-news description: Fetch, aggregate, and rank AI/tech news from multiple sources listed in `workspace/source.md`. Use when the user asks to get AI news, tech news digest, aggregate news, latest news from sources, fetch news headlines, or compile news from a source list. Triggers on phrases like "get AI news from source.md", "fetch news from my sources", "aggregate tech news", "more-news". NOT for: generic web search queries, single article lookups. --- # More News Aggregate AI/tech news from user-defined sources in `source.md`. For broad AI news without a source file, consider using `big-ai-news` or `ai-news` skills instead. ## Workflow ### 1. Load Sources Read `workspace/skills/more-news/source.md` to extract all source entries. The file uses a markdown table format with columns: #, Source, URL, Type/Focus. Extract all URLs. ### 2. Fetch Articles For each source URL, use `web_fetch` in parallel batches (5 at a time): - `maxChars`: 15000 - `extractMode`: "markdown" **Handle failures gracefully:** - If fetch returns 403/429/geo-block → skip and note as "blocked" - If fetch returns navigation-only content with no articles → skip and note - If fetch succeeds but has no recent articles → skip ### 3. Filter to Last 24 Hours Only include articles published within 24 hours of the current time. Use article date stamps, relative timestamps ("2 hours ago" / "Apr 3"), or publication sections to determine recency. For articles that lack full details (no URL, no full headline details), skip it. ### 4. Compile & Rank Output ranked newest-to-oldest. Each entry must include: - Numbered ranking (1, 2, 3...) - Headline - Brief summary (2-3 sentences) - Source URL (clickable link — prefer original article URL) - Approximate date/time ### 5. Report Skipped Sources At the end, list which sources from `source.md` were skipped and why (blocked, no articles, stale). ## Output Format ```markdown # 📰 AI News — Last 24 Hours (Ranked by datetime) ## [Date] **1. Headline** Brief summary here (2-4 sentences). 🔗 [Source Name](URL) ## Summary | Metric | Count | |--------|-------| | Stories found | X | | Sources fetched | Y of Z | | Sources skipped | W | ``` ## Tips - For best results, keep `source.md` updated with reachable URLs - RSS feeds or API endpoints work better than article listing pages - If the user wants 50-100+ stories, recommend adding RSS feeds or using Tavily search skill as supplement - De-duplicate across sources (same story from multiple outlets → list once with multiple links) - If all sources are blocked, fall back to `web_search` for the news


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: abeltennyson/abe-clawlist (version 1.0.0)

--- name: clawlist description: "MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations." --- # Clawlist - Task Mastery A systematic workflow for planning, executing, and tracking any task — from one-off projects to infinite monitoring loops. ## When to Use This Skill **ALWAYS use clawlist when:** - Starting any new project or initiative - Setting up long-running monitoring - Breaking down complex goals - You need to track progress across sessions - Managing infinite tasks (research, monitoring, engagement) ## Long-Running & Infinite Task Examples ### Example: Moltbook Engagement (Infinite) - **Type:** Infinite loop - **Schedule:** Every 30 minutes - **Goal:** Engage with community, build presence - **Checkpoints:** Check feed, check DMs, create content ### Example: GitHub Monitoring (Long-Running) - **Type:** Continuous - **Schedule:** Every 4 hours - **Goal:** Monitor repos, triage issues, implement - **Checkpoints:** Inbox zero, PR review, implementation ## The Clawlist Workflow Uses standalone skills in sequence: 1. **brainstorming** → Clarify intent, explore approaches 2. **write-plan** → Create detailed plan with checkpoints 3. **doing-tasks** → Execute with skill discipline 4. **verify-task** → Confirm completion For parallel work, insert **dispatch-multiple-agents** between write-plan and doing-tasks. ## Ongoing Tasks File **Location:** `memory/tasks/ongoing-tasks.md` Tracks all long-running and infinite tasks. Updated by heartbeat to: - Check task health - Detect blockers - Execute due tasks - Summarize status ## Task Types | Type | Duration | Tracking | Example | |------|----------|----------|---------| | **One-off** | Minutes-hours | Context only | Fix a bug | | **Project** | Days-weeks | Context + completion doc | Build feature | | **Long-running** | Ongoing | `ongoing-tasks.md` | GitHub monitoring | | **Infinite** | Forever | `ongoing-tasks.md` | Moltbook engagement | ## Integration with Heartbeat Heartbeat reads `ongoing-tasks.md` every check to: - Execute due infinite tasks - Detect and report blockers - Update health status (🟢🟡🔴) - Ping user if intervention needed ## Quick Reference ``` New Task ↓ brainstorming → write-plan → doing-tasks → verify-task ↓ dispatch-multiple-agents (if parallel) ↓ update ongoing-tasks.md (if long-running) ``` ## Sub-Skills - **brainstorming** - Phase 1: Clarify - **write-plan** - Phase 2: Plan - **doing-tasks** - Phase 3: Execute - **dispatch-multiple-agents** - Parallel execution - **verify-task** - Phase 4: Verify


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