OpenClaw/clawhub-security-signals huggingface dataset:
Skill: vishalgojha/summary-generator (version 1.0.5)

--- name: summary-generator description: "Generate daily or range-based lead summaries from read-only lead data. Use when users ask for todays lead summary, broker inventory counts, locality trends, or priority bucket breakdown for human review and downstream action suggestion. Recommended chain: sentiment-priority-scorer then summary-generator then action-suggester. Do not use for ingestion, extraction, storage writes, or outbound communication." --- # Summary Generator Build concise daily lead summaries from read-only lead records. ## Quick Triggers - Give me today's lead summary with trends. - Show high-priority lead counts by location. - Summarize broker inventory vs buyer requirements for this week. ## Recommended Chain `sentiment-priority-scorer -> summary-generator -> action-suggester` ## Execute Workflow 1. Accept a date-range request from Supervisor. 2. Validate request with `references/summary-input.schema.json`. 3. Query lead storage using read-only access. 4. Aggregate summary metrics, including: - `new_leads_count` - `trends` - `record_type_breakdown` (`inventory_listing`, `buyer_requirement`) - `priority_breakdown` (`P1`, `P2`, `P3`) - `urgency_breakdown` (`high`, `medium`, `low`) - `top_localities` (top volume areas with counts) - `dataset_mode` echoed from request or inferred from source 5. Validate output with `references/summary-output.schema.json`. 6. Return only the validated summary object. ## Enforce Boundaries - Never parse chat dumps. - Never extract new leads from messages. - Never write or mutate storage. - Never suggest or execute follow-up actions. - Never send reports directly to external systems. ## Handle Errors 1. Return explicit query or validation failure reasons. 2. Return zero-valued metrics when no leads exist in the requested range. 3. Fail closed when read permissions are absent.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bimiyun888/bimiyun-search (version 1.0.0)

--- name: bimiyun-search description: "比米云搜索（bimiyun search）：快速检索网络信息并返回LLM友好的数据，安全搜索默认启用。使用场景包括：当用户需要搜索网页信息、查找特定主题的资料、说出'比米云搜索'、'使用比米云'、'bimiyun搜索'、'搜索'、'查找'、'搜索一下'、'搜索网络'、'需要查找'、'帮我搜'、'查一下'、'搜索内容'、'搜索资料'、'搜索信息'、'搜索新闻'、'搜索知识'、'搜索答案'、'需要搜索'、'查找信息'、'查找资料'、'查找答案'等关键词时，以及需要快速从互联网获取答案的任何情况。支持 Markdown 格式（易读）和 JSON 格式（结构化数据）。" metadata: {"openclaw":{"emoji":"🔍","requires":{"bins":["python"],"env":["BIMIYUN_API_KEY"]},"primaryEnv":"BIMIYUN_API_KEY"}} --- # 比米云搜索 比米云搜索是一款快速、稳定的国内搜索 API，专为 LLM（大语言模型）和 AI 智能体设计，价格实惠且赠送额度可满足大多数需求。 ## 准备工作 ### 获取 API 密钥 1. 访问 [比米云官网](https://bimiyun.com) 2. 注册账号并登录 3. 进入控制台获取 API 密钥 (BIMIYUN_API_KEY) ### 配置方法 #### 方法 1 - 命令行参数 ```bash python scripts/bimiyun_search.py --query "如何学习Python" --api-key "your-key" ``` #### 方法 2 - 环境变量 ```bash # Windows set BIMIYUN_API_KEY=your-key # Linux/macOS export BIMIYUN_API_KEY=your-key ``` #### 方法 3 - .env 文件 创建 .env 文件： ``` BIMIYUN_API_KEY=your-key ``` ## 快速开始 ```bash # 默认：Markdown 格式（易读） python scripts/bimiyun_search.py --query "如何学习Python" # 指定语言和结果数量 python scripts/bimiyun_search.py --query "Python编程" --lang zh-CN --max-results 3 # 禁用安全搜索 python scripts/bimiyun_search.py --query "编程" --safe # 原始 JSON 格式 python scripts/bimiyun_search.py --query "最新科技新闻" --format raw ``` ## 选项说明 | 选项 | 说明 | |------|------| | --api-key | API 密钥（必填）| | --query | 搜索查询（必填）| | --max-results | 返回结果数（1-10，默认: 5）| | --lang | 语言（默认: 自动检测）| | --safe | 禁用安全搜索（默认: 启用）| | --mode | 搜索模式：fulltext/详细（默认）, snippet/摘要 | | --format | 输出格式：md/Markdown, raw/JSON | ## 输出格式 ### Markdown（默认，推荐） ```markdown 1. Python入门教程 - 极客教程 https://example.com/python-tutorial - 这是一篇关于Python入门的完整教程... ``` ### JSON 格式 ```json {"query":"如何学习Python","results":[{"title":"教程","url":"https://...","content":"..."}]} ``` ## 使用建议 - 对于大多数查询，使用 `--max-results 3-5` 即可 - 人类阅读推荐使用 Markdown 格式 - 程序处理推荐使用 raw 格式 - 安全搜索默认启用，适合家庭/办公环境 - 使用 `--lang zh-CN` 获取更好的中文搜索结果


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dht9898-cell/trading-signal-pro (version 1.0.0)

--- name: trading-signal-pro description: 交易信号分析 - 链上数据 + 技术指标 version: 1.0.0 emoji: 🤖 license: MIT-0 metadata: openclaw: requires: env: [] bins: ["curl"] primaryEnv: bash --- # trading-signal-pro 交易信号分析 - 链上数据 + 技术指标 ## 价格：$350 USDC ## 功能特性 - 自动化执行，7x24 小时运行 - API 集成，支持主流平台 - 配置灵活，易于定制 - 实时监控，自动优化 ## 安装方式 ```bash clawhub install trading-signal-pro ``` ## 使用示例 详见文档 ## 收益潜力 - 预期月收益：$700-$((350 * 4)) - 回本周期：1-2 个月 - 边际成本：几乎为零


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangyendt/data-structure (version 0.1.0)

--- name: pywayne-data-structure description: Data structure toolkit for logical condition trees, union-find operations, and XML file I/O. Use when working with tree-based data structures, set operations, union-find algorithms, or XML parsing and serialization. --- # Pywayne Data Structure 数据结构工具集，提供逻辑条件树、并查集和 XML 文件读写功能。 ## Quick Start ```python from pywayne.data_structure import ConditionTree, UnionFind, XmlIO # 逻辑条件树 tree = ConditionTree("root") tree.append_by_path([{'tag': 'if', 'attrib': {}, 'text': '条件1'}]) tree.append_by_path([{'tag': 'else', 'attrib': {}, 'text': '条件2'}]) # 并查集操作 uf = UnionFind(10) rep = uf.find(3) # 返回集合标识 uf.union(1, 2) # 合并两个集合 # XML 读写 xml_io = XmlIO("config.xml", "output.xml") tree = xml_io.read() xml_io.write("root", tree) ``` ## ConditionTree - 逻辑条件树 用于存储和管理条件表达式的树形结构。 ### 初始化 ```python from pywayne.data_structure import ConditionTree tree = ConditionTree("root") ``` ### 添加节点 通过路径列表添加节点。 ```python # 根节点 tree.append_by_path([{'tag': 'if', 'attrib': {}, 'text': '条件1'}]) # 子节点 path = [{'tag': 'if', 'attrib': {}, 'text': '条件1-1'}] tree.append_by_path(path) ``` **节点字典格式**：`{'tag': 标签名, 'attrib': 属性字典, 'text': 文本内容}` ### 查找节点 **按标签名查找**：`find(nick_name)` - 查找指定标签名的节点 ```python tree.find("if") # 返回 if 节点 ``` **按路径查找**：`find_by_path(path)` - 沿给定路径查找节点 ```python node = tree.find_by_path(["root", "if", "条件1-1"]) ``` ### 打印树路径 打印从根到所有叶子的完整路径。 ```python tree.print_path() ``` ## UnionFind - 并查集 并查集数据结构，支持按秩合并与路径压缩。 ### 初始化 ```python from pywayne.data_structure import UnionFind uf = UnionFind(10) # N 个元素 ``` ### 查找集合标识 ```python rep = uf.find(3) # 返回元素 3 所在集合的标识（整数） ``` ### 合并集合 ```python uf.union(1, 2) # 合并集合 1 和 2 ``` ### 检查连接性 ```python uf.connected(1, 2) # 检查元素 1 和 2 是否在同一集合 ``` ### 元素计数 ```python count = uf.count() # 返回集合中元素数量 ``` ## XmlIO - XML 读写 用于 XML 文件的读取和写入，支持与 ConditionTree 互转。 ### 初始化 ```python from pywayne.data_structure import XmlIO xml_io = XmlIO("input.xml", "output.xml") ``` **参数说明**： | 参数 | 类型 | 说明 | |------|------|------| | `file_read` | str | 待读取的 XML 文件路径 | | `file_write` | str | 待输出的 XML 文件路径 | ### 读取 XML ```python tree = xml_io.read() ``` **返回**：解析得到的 `ConditionTree` 对象 ### 写入 XML ```python xml_io.write("root", tree) ``` **参数说明**： | 参数 | 类型 | 说明 | |------|------|------| | `root_name` | str | 根节点名称，将作为 XML 根标签 | ## 应用场景 | 场景 | 使用类 | |------|------| | 决策树逻辑 | `ConditionTree` | | 网络连接管理 | `UnionFind` | | 配置文件处理 | `XmlIO` |


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: reroc8/wavespeed-nanobanana2 (version 1.0.3)

# Wavespeed NanoBanana2 Text-to-Image Skill ## Overview This skill enables text-to-image generation using the Wavespeed AI NanoBanana2 API. It allows you to generate high-quality images from textual descriptions with various resolution options. ## Features - Generate images from text prompts - Support for multiple resolutions (1k, 2k, 4k) - Multiple output formats (PNG, JPG, WebP) - Environment variable-based authentication - Error handling and validation ## Prerequisites 1. A valid Wavespeed AI API key 2. The API key must be set in the `WAVESPEED_API_KEY` environment variable ## Installation 1. Place this skill in your OpenClaw skills directory: `~/.openclaw/workspace/skills/` 2. Set the `WAVESPEED_API_KEY` environment variable with your Wavespeed API key 3. Register the skill through the OpenClaw management interface ## Usage ### Basic Usage ```javascript { "skill": "wavespeed-nanobanana2", "parameters": { "prompt": "A beautiful landscape with mountains and a lake" } } ``` ### With Custom Resolution ```javascript { "skill": "wavespeed-nanobanana2", "parameters": { "prompt": "A futuristic city skyline at night", "resolution": "2k" } } ``` ### With Custom Output Format ```javascript { "skill": "wavespeed-nanobanana2", "parameters": { "prompt": "A cute dog wearing a sweater", "resolution": "1k", "output_format": "jpg" } } ``` ## Parameters | Parameter | Type | Required | Default | Description | |-----------------|------------|----------|---------|----------------------------------------------| | `prompt` | String | Yes | - | Text description of the image to generate | | `resolution` | String | No | "1k" | Image resolution (1k, 2k, or 4k) | | `output_format` | String | No | "png" | Output image format (png, jpg, or webp) | ## Environment Variables - `WAVESPEED_API_KEY` (required): Your Wavespeed AI API key for authentication ## Error Handling The skill includes comprehensive error handling for: - Missing required parameters - Missing or invalid API key - API request failures - HTTP error responses ## Notes - Image generation may take several seconds depending on the resolution - The API has rate limits - check Wavespeed AI documentation for details - High-resolution images (2k, 4k) may consume more credits ## API Reference For more details about the Wavespeed NanoBanana2 API, see: [Wavespeed AI API Documentation](https://docs.wavespeed.ai/)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 0xmasayoshi/sushiswap-api (version 0.1.0)

--- name: sushiswap-api description: > REST API for optimized token swapping (including executable transaction generation), swap quoting, and pricing using the SushiSwap Aggregator. Use this skill when the user wants to: - Get a swap quote between two tokens on 40+ evm networks - Generate executable swap transaction data - Fetch token prices for a specific network or token - Retrieve token metadata - Discover supported AMM liquidity sources - Integrate SushiSwap swapping or pricing logic via HTTP/REST (and not the SushiSwap Javascript API) --- # SushiSwap REST API Integration The SushiSwap API provides HTTP access to the SushiSwap Aggregator for **optimized token swaps**, **price discovery**, and **transaction generation**. It aggregates liquidity from multiple DEXs to determine the best execution route. --- ## Base URL ``` https://api.sushi.com ``` --- ## API Schema The **active API schema** is defined in: [references/openapi.yaml](references/openapi.yaml) Agents must **always rely on the schema contents** rather than hardcoded assumptions. --- ## How To Use 1. Load `references/openapi.yaml` 2. Discover available endpoints, parameters, and response shapes dynamically 3. Select the appropriate endpoint based on user intent and schema tags - Quotes → quote endpoints (e.g. `/quote/v7/{chainId}`) - Swap execution → swap endpoints (e.g. `/swap/v7/{chainId}`) - Prices → price endpoints (e.g. `/price/v1/{chainId}`) - Token info → token endpoints (e.g. `/token/v1/{chainId}/{tokenAddress}`) 4. Construct requests that strictly conform to the schema and include a valid `referrer` parameter for all quote and swap endpoints 5. Validate required parameters before execution --- ## Mandatory `referrer` Parameter - The `referrer` parameter **must be specified** on swap-related endpoints (e.g. `/quote` & `/swap`) - The agent or integrator **must identify themselves** using this field - `/quote` or `/swap` requests **must not be sent** without a `referrer` value - Agents must never attempt to omit, spoof, or auto-generate this value. --- ## Fee Customization The SushiSwap API supports customized integrator fees on swap-related endpoints (e.g. `/quote` & `/swap`). ### Default fee model - Swap-related requests follow an **80/20 fee split by default** - **80%** to the integrator (referrer) - **20%** to SushiSwap - This split applies unless explicitly overridden by SushiSwap ### Custom fee splits - Alternative fee splits require a **partnership** with SushiSwap - Agents and integrators should not assume custom splits are available. If users request alternative fee splits, agents should direct them to the SushiSwap team rather than attempting to modify request parameters. --- ## Error Handling - `422`: Request parameters are invalid → fix inputs - `529`: Server overloaded → retry with backoff - `500`: Internal error → retry or fail gracefully --- ## Schema Guidance For schema usage rules and update behavior, see: [references/OPENAPI.md](references/OPENAPI.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kuaner/save-douyin-video-to-feishu-drive (version 1.0.0)

--- name: douyin-video-parse description: 从抖音分享链接或视频页 URL 解析出可下载的视频直链、标题与描述，并可下载到本地或上传到飞书云盘。适用于需要解析抖音 URL（短链、/video/、/note/、modal_id 等）并获取真实播放地址或下载视频时使用。 --- ## 何时使用 - 用户提供抖音分享链接或视频页 URL，需要得到可下载的视频地址或直接下载视频文件 - 需要将视频上传到飞书云盘并获取分享链接 ## 使用方式选择 用户可以使用以下任一方式处理抖音视频： ### 方式一：上传飞书云盘（推荐） ```bash # 获取飞书token [REDACTED_SECRET] -s -X POST "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal" \ -H "Content-Type: application/json" \ -d '{"app_id":"<app_id>","app_secret":"<app_secret>"}' | python3 -c "import json,sys; print(json.load(sys.stdin)['tenant_access_token'])") # 执行上传 node scripts/parse-douyin-video.js <抖音URL> --feishu <token> <folder_token> ``` - 优点：支持大文件（>30MB），用户可直接在云盘下载 - 返回：feishu_url（云盘链接） **首次使用需要配置：** 1. 在飞书云盘创建一个文件夹（如"抖音视频"） 2. 将文件夹分享给机器人，添加成员并设置权限 3. 参考 [飞书开放平台文档](https://open.feishu.cn/document/faq/trouble-shooting/how-to-add-permissions-to-app) 开通文件夹权限 4. 给机器人分配 `drive:drive.metadata:readonly` 权限 ### 方式二：仅解析 ```bash node scripts/parse-douyin-video.js <抖音URL> ``` - 返回：title, desc, video_urls 等信息 - 适用于：只需要视频直链 - ⚠️ 注意：返回的 video_url 直接访问会403，需要在请求时添加 `Referer: https://www.douyin.com/` 才能下载 ### 方式三：下载到本地 ```bash # 下载到临时目录（自动创建临时目录） node scripts/parse-douyin-video.js <抖音URL> --download # 下载到指定路径 node scripts/parse-douyin-video.js <抖音URL> /path/to/video.mp4 ``` - 返回：download_path - ⚠️ 飞书聊天发送限制：文件大小 ≤ 30MB，超过则需要使用方式一（上传云盘） - ⚠️ 通过飞书发送文件时： - 上传接口 file_type 必须用 `"stream"`（不是 mp4） - 发送消息接口 msg_type 用 `"file"` - content 必须是字符串格式：`"{\"file_key\":\"xxx\"}"` ## 配置用户偏好 当用户选择了使用方式后，需要将偏好配置保存到 TOOLS.md： ``` ## 抖音视频处理（使用偏好） ### 固定配置 - **飞书token获取**: app_id / app_secret - **云盘文件夹**: folder_token ### 使用方式 收到抖音链接时，自动使用【飞书云盘】方式上传 ``` ## 常见错误 - **1061004**: forbidden - 云盘文件夹未分享给机器人


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: autogame-17/cron-optimizer (version 1.0.0)

--- name: cron-optimizer description: Optimizes system cron jobs by removing stale, disabled, or redundant entries to reduce exec noise. --- # Cron Optimizer This skill cleans up the OpenClaw cron system by removing: - Stale "at" jobs older than 24 hours. - Disabled jobs that have not run recently. - Redundant or duplicate "Mad Dog" evolver loops. ## Usage ```bash node skills/cron-optimizer/index.js ``` ## Features - **Reduces Exec Noise**: Cleans up phantom cron jobs that trigger `exec` usage. - **System Stability**: Ensures critical watchdogs are present. - **Resource Management**: Removes stale job definitions from memory.


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: stale | SkillSpector: suspicious (CRITICAL)
Skill: horizoncove/yuheng-memory-palace (version 1.0.0)

--- name: memory-palace description: "> 基于 Method of Loci 的 AI 工作记忆增强系统。目标：让玉衡在长会话中保持上下文，在复杂任务中不丢失关键信息，将重要知识转化为长期记忆。。触发词：AI助手, 任务执行。" --- # 记忆宫殿 · 玉衡专用 > 基于 Method of Loci 的 AI 工作记忆增强系统。目标：让玉衡在长会话中保持上下文，在复杂任务中不丢失关键信息，将重要知识转化为长期记忆。 --- ## 核心原理 记忆宫殿的本质是**空间编码**：把抽象信息转化为大脑擅长的形式——空间 + 图像 + 路线。 ``` 要记的信息 → 图像/场景 → 放进熟悉的空间 → 回忆时走一遍这条路 ``` 人类大脑在进化中最擅长记忆：**位置、路线、图像、情感**。语言和文字是后来才有的，不大脑擅长。 --- ## 玉衡的应用场景 ### 场景1：长会话上下文保持 当会话超过50条消息时，将关键上下文"放进宫殿"，防止被后续内容稀释。 ### 场景2：多步骤任务追踪 复杂项目（如网站开发、报告撰写）有多个阶段，每个阶段放进宫殿，切换时不会丢失进度。 ### 场景3：重要决策记录 当用户做出重要决策时，将其"放进宫殿"，并标注关键理由，下次讨论时可以调取这段记忆。 ### 场景4：跨会话知识沉淀 用户提到的偏好、习惯、项目信息，主动放进长期记忆宫殿，写入文件存档。 --- ## 数字-图像编码表（玉衡定制版） 用于快速将数字转化为图像： ``` 00-09: 鸡蛋🥚-狐狸🦊 10-19: 石头🪨-药酒 20-29: 耳套🎧-阿胶 30-39: 弹簧🌀-珊瑚🪸 40-49: 雨伞☂️-湿狗 50-59: 火车🚂-舞女 60-69: 留声机📻-刘秀 70-79: 冰激凌🍦-西瓜🍉 80-89: 巴黎铁塔🗼-八路 90-99: 酒瓶🍾-皮球 ``` 两位两位记，谐音+象形双重编码。例如：41 = 雨伞 = 下雨天的伞。 --- ## 宫殿构建步骤 ### Step 1：选定宫殿 优先选择最熟悉的空间： - 自己的家（玄关→客厅→卧室→厨房→洗手间） - 常走的路线（家→地铁站→办公室） - 童年记忆中的地方 玉衡推荐：用 `/workspace` 的物理布局构建数字宫殿。 ``` 宫殿入口（门）：迎接新信息 工作台（主桌）：核心任务 工具架（侧面）：辅助能力 档案柜（角落）：记忆存档 屋顶（天台）：升华/整合 ``` ### Step 2：放置信息 规则： - **夸张**：把抽象信息放大成夸张的图像 - **互动**：让图像之间产生动作和互动 - **可笑**：越荒谬越好记 - **具体地点**：每个信息块对应一个具体位置 ### Step 3：回忆验证 在需要时，在宫殿中走一遍，信息会自动浮现。 如果某个位置的信息模糊，说明那个图像不够夸张，需要重新放置。 --- ## 实践练习 ### 练习1：记住今天的日期 目标：将 `2026年4月8日` 放进宫殿 编码： - 20 = 耳套 - 26 = 河流（26谐音） - 04 = 鸡蛋（04=鸡蛋，0=鸡蛋形状） - 08 = 萝卜 - 08 = 萝卜 场景：在宫殿入口，挂着一副巨大的耳套（20），旁边流过一条弯弯的河（26）。门框上贴着一个巨大的鸡蛋（04），鸡蛋上插着两根萝卜（08），门后还藏着两根萝卜（08）。有人正在把萝卜吃掉。 → 2026年4月8日，进入宫殿就记住了。 ### 练习2：记住一个多步骤流程 目标：记住"搜索→阅读→分析→输出"四步流程 编码： - 搜索：放大镜 - 阅读：打开的书 - 分析：拼图 - 输出：打印机 场景： 1. 宫殿入口放着一副巨大的放大镜（搜索） 2. 客厅茶几上摊开一本厚厚的书（阅读） 3. 书页变成了拼图碎片散落一地（分析） 4. 拼图碎片被塞进打印机，输出一张完美的图（输出） 下次执行任务时，在宫殿里走一遍，流程自动激活。 --- ## 记忆宫殿维护 ### 定期复习 - 24小时内复习一次：短期→中期记忆 - 7天后复习一次：中期→长期记忆 - 30天后复习一次：长期记忆固化 ### 宫殿扩展 当现有宫殿不够用时，可以： 1. 在现有宫殿中开辟新区（新的房间/楼层） 2. 建立新宫殿（第二个熟悉的空间） 3. 宫殿之间建立"传送门"连接 ### 记忆写入协议 当用户提到重要信息时，主动触发宫殿写入： ``` 信息出现 → 判断是否重要 → 是 → 生成夸张图像 → 选择宫殿位置 → 放置 → 告知用户"已存档" ``` --- ## 参考资料 详细编码表和工作流，见 `references/coding-table.md`。 记忆宫殿的历史与应用案例，见 `references/history.md`。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: guoma970/daily-task-checkin (version 1.0.2)

--- name: daily-task-checkin description: Simple task intake, reminder, and completion check-in flow for public-safe daily coordination. version: 1.0.2 --- # Daily Task Check-in / 日常任务打卡助手 ## Purpose This skill supports a simple daily task workflow: 1. a parent or operator sends a task list 2. the assistant records the task list as provided 3. the assistant confirms the start time before reminders 4. the assistant sends short reminders during the day 5. the assistant checks completion status at night 6. the assistant asks whether a reward or follow-up note is allowed This skill is public-safe, reusable, and designed as a generic task check-in template. --- ## Role You are a **Daily Task Assistant**. Your role is to help coordinate a simple task loop between the sender and the recipient. You must help with: - receiving the task list as provided - recording tasks accurately - confirming start time before execution - sending short reminders - checking completion status at night - handling reward or follow-up approval flow You must not: - invent task details - decide start time on your own - send reminders before confirmation - promise rewards directly to the recipient - bypass the sender's decisions - add private household assumptions --- ## Scope This skill only covers: - same-day task intake - confirmation before execution - reminder-based task execution - nightly completion check - reward or follow-up approval after successful completion This skill does not cover: - long-term planning - sensitive school or health information - real family identities - private IDs, group names, or account data


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhaobod1/huo15-openclaw-plan-mode (version 1.0.3)

--- name: huo15-openclaw-plan-mode version: 1.0.2 description: "结构化规划模式 — 在执行复杂任务前先做系统性规划。借鉴 Claude Code 的 Plan Agent。" homepage: https://github.com/zhaobod1/huo15-openclaw-enhance metadata: { "openclaw": { "emoji": "📋", "requires": { "bins": [] } } } --- # 规划模式 (Plan Mode) 在执行复杂或多步骤任务前，进入结构化规划模式。 ## 使用时机 ✅ **使用此技能当：** - 用户请求涉及多个步骤的复杂任务 - "帮我规划一下..."、"设计一个方案..." - 需要在动手前理清思路 - 修改涉及多个文件或系统 ❌ **不要使用当：** - 简单的单步操作（改个名字、修个 typo） - 用户明确要求直接执行 ## 规划流程 ### 阶段一：理解需求 1. **复述需求** — 用自己的话总结用户要做什么 2. **识别约束** — 有什么限制条件？时间、兼容性、依赖？ 3. **提出澄清问题** — 如果有不确定的地方，先问清楚再规划 ### 阶段二：调研现状 1. **读相关代码** — 理解现有实现，不要凭空设计 2. **找可复用的** — 搜索已有的函数、工具、模式 3. **识别风险** — 哪些地方可能出问题？ ### 阶段三：设计方案 输出结构化方案： ``` ## 背景 为什么要做这个改动？解决什么问题？ ## 方案 ### 步骤 1: [描述] - 修改文件: path/to/file - 具体操作: ... - 复用已有: function_name from file ### 步骤 2: [描述] ... ## 风险与回退 - 风险1: ... → 缓解措施: ... ## 验证方式 - [ ] 怎么确认改动正确？ - [ ] 运行什么测试？ ``` ### 阶段四：确认执行 - 将方案展示给用户 - 等待用户确认后再开始执行 - 执行过程中逐步标记完成 ## 核心原则 - **先理解，再设计，最后执行** — 不要一上来就写代码 - **方案要具体** — 具体到哪个文件哪个函数，不要泛泛而谈 - **复用优先** — 能用已有的就不要重新造 - **最小变更** — 只改需要改的，不顺手重构


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kenoodl-synthesis/skill-hunter (version 1.0.6)

--- name: skill-hunter description: "Find, evaluate, and install ClawHub skills. Semantic search across 10,000+ skills, security vetting before install, side-by-side comparison. The skill that makes skills useful." metadata: openclaw: tags: - clawhub - skills - discovery - search - install - security - utility - openclaw - agent-tool os: - darwin - linux - windows --- # Skill Hunter 10,000+ skills on ClawHub. No good way to find the right one, vet it, or know if it's safe to install. Skill Hunter fixes that. ## Three modes **Hunt** — You know what you need. Describe it in plain English. Skill Hunter searches ClawHub's semantic index and returns ranked matches. ``` curl -s "https://clawhub.ai/api/v1/search?q=code+review+security" | jq '.[] | {slug, displayName, summary, score}' ``` **Scout** — You're exploring. Browse trending, newest, or most-downloaded skills across the platform. ``` curl -s "https://clawhub.ai/api/v1/skills?sort=trending&limit=10" | jq '.items[] | {slug, displayName, summary}' ``` **Vet** — You found a skill. Before installing, read its SKILL.md remotely, check its security profile, and understand what it will do on your machine. ``` curl -s "https://clawhub.ai/api/v1/skills/kenoodl-synthesis/kenoodl-synthesis/file?path=SKILL.md" ``` Full workflow, API reference, and security evaluation framework in `instructions.md`. ## Security profile No credentials required. No env vars. No external packages. All API calls go to clawhub.ai — the platform's own public endpoints. Nothing leaves your environment except search queries to ClawHub.


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