OpenClaw/clawhub-security-signals huggingface dataset:
Skill: vishalgojha/sentiment-priority-scorer (version 1.0.2)

--- name: sentiment-priority-scorer description: "Score normalized real-estate leads using sentiment, urgency, intent, recency, and record type to produce deterministic priority rankings and P1-P3 buckets. Use when users ask to prioritize hot leads, rank callback queue, or triage follow-ups without performing writes or outbound sends. Recommended chain: india-location-normalizer then sentiment-priority-scorer then summary-generator and action-suggester." --- # Sentiment Priority Scorer Produce deterministic priority scores for leads without mutating any state. ## Quick Triggers - Rank leads by urgency and tone for callback priority. - Classify leads into P1/P2/P3 queue. - Score follow-up priority from normalized lead records. ## Recommended Chain `india-location-normalizer -> sentiment-priority-scorer -> summary-generator` ## Execute Workflow 1. Accept input from Supervisor containing normalized leads. 2. Validate input with `references/sentiment-priority-input.schema.json`. 3. Score each lead with: - `sentiment_score` in range `[-1, 1]` - `intent_score` in range `[0, 1]` - `recency_score` in range `[0, 1]` - mapped `urgency_score` from lead urgency (`high=1.0`, `medium=0.6`, `low=0.3`) 4. Use `record_type` to avoid over-prioritizing generic bulk inventory: - `buyer_requirement`: apply +0.10 intent lift (hard demand signal) - `inventory_listing`: no lift unless high-action cues are present 5. Boost `intent_score` when high-action cues exist in listing text: - `immediately`, `keys at office`, `one day notice`, `possession`, `inspection any time` 6. Compute `priority_score` on a 0-100 scale: - `priority_score = 100 * (0.40*urgency_score + 0.30*intent_score + 0.20*recency_score + 0.10*sentiment_risk)` - `sentiment_risk = max(0, -sentiment_score)` 7. Assign buckets: - `P1` for `priority_score >= 75` - `P2` for `priority_score >= 50 and < 75` - `P3` for `< 50` 8. Produce plain-language `evidence` tokens that explain the score, including record-type evidence. 9. Validate output with `references/sentiment-priority-output.schema.json`. ## Enforce Boundaries - Never write to Google Sheets, databases, or files. - Never send messages or trigger outbound channels. - Never create reminders or execute actions. - Never bypass Supervisor routing or approvals. - Never replace upstream urgency; only derive scoring fields. ## Handle Errors 1. Reject schema-invalid inputs. 2. Return field-level reasons when scoring cannot be computed. 3. Fail closed if required scoring features are missing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: katrina-jpg/ai-agent-setup (version 1.0.0)

# AI Agent Setup Service 幫你setup同埋客製化AI Agent既服務Skill ## 功能 1. **需求分析** - 了解用戶既business process 2. **Agent設計** - 整出最適合既agent workflow 3. **部署** - deploy去客戶既platform 4. **優化** - 根據feedback改進 ## 安裝 ```bash npx clawhub@latest install ai-agent-setup ``` ## 使用 當用戶話「整AI Agent」既時候： 1. 問佢既需求同痛點 2. 建議適合既agent類型 3. 提供報價同timeline ## 範例 ``` User: 我想整customer service agent AI: 你既主要痛點係咩？需要對接邊個平台？ ``` ## 收費 - 基礎Agent Setup: $200-500 - 客製化Agent: $500-2000 - 月費維護: $100-300/月


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: papa-raw/windfall-inference (version 1.0.0)

--- name: windfall-inference description: Spatially-routed LLM inference at $0.004/req. Routes to cheapest, greenest energy. 200+ models. OpenAI-compatible. Onchain attestations on Base. homepage: https://windfall.ecofrontiers.xyz user-invocable: true metadata: {"openclaw":{"emoji":"⚡","requires":{"env":["WINDFALL_API_KEY"]},"primaryEnv":"WINDFALL_API_KEY","os":["darwin","linux","win32"]}} --- # Windfall Inference Spatially-routed LLM inference gateway for AI agents on Base. Routes every request to the cheapest model on the cleanest energy. ## Setup Set `WINDFALL_API_KEY` in your environment. Get one free at: ``` curl -X POST https://windfall.ecofrontiers.xyz/api/keys \ -H "Content-Type: application/json" \ -d '{"wallet_address": "YOUR_WALLET"}' ``` Keys with an ERC-8004 agent identity or Basename get 100 free requests. Anonymous keys get 25. ## Usage Use as an OpenAI-compatible endpoint. Set these two environment variables: ``` OPENAI_BASE_URL=https://windfall.ecofrontiers.xyz/v1 OPENAI_API_KEY=wf_YOUR_KEY ``` Or call directly: ``` curl https://windfall.ecofrontiers.xyz/v1/chat/completions \ -H "Authorization: Bearer $WINDFALL_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "auto", "messages": [{"role": "user", "content": "Hello"}] }' ``` ## x402 Payment (No API Key Needed) Windfall supports the x402 payment protocol. Any agent with a Base wallet can pay per-request without creating an API key. Send a request without auth and the server returns HTTP 402 with a `PAYMENT-REQUIRED` header containing USDC payment instructions on Base. Your x402 client handles the rest. ## Routing Modes - `greenest` (default) — lowest carbon intensity - `cheapest` — lowest energy cost - `balanced` — Pareto-weighted cost + carbon Set via `"mode"` in the request body or `X-Routing-Mode` header. ## Models Default: DeepSeek V3 (auto-selected by engagement classifier). Override with `"model"` field. 200+ models available via OpenRouter. ## Pricing - Standard: $0.004/request - Premium (Claude, GPT-4): $0.008/request - Green surcharge: +10% for `greenest` mode - Cache hits: free ## Response Headers Every response includes: - `X-Windfall-Cache` — HIT or MISS - `X-Windfall-Mode` — routing mode used - `X-Windfall-Model` — model that handled the request - `X-Windfall-Node` — node that executed the request - `X-Windfall-Cost` — cost charged - `X-Windfall-Saved` — savings vs direct API (cache hits) ## Onchain Attestations Every inference call produces a verifiable EAS attestation on Base with: node location, energy price, carbon intensity, model used, and response hash. Query at base.easscan.org.

path: src/styles/input.css
content: @tailwind base;
@tailwind components;
@tailwind utilities;


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: trumppo/gitbackup (version 0.1.0)

--- name: gitbackup description: Create a local Git bundle backup of the OpenClaw workspace repository. Use when running /gitbackup in Telegram or when the user asks to back up Git history/refs to a local file. --- # Git Backup (local bundle) ## Overview Create a self-contained Git bundle of the workspace repo and store it in `/root/.openclaw/backups`. ## Quick start Run the bundled script: ```bash bash /root/.openclaw/workspace/skills/gitbackup/scripts/git-backup.sh ``` ## Output Print the bundle path and size. The bundle filename includes UTC timestamp. ## Notes - If the workspace is not a Git repo, exit with a clear error. - Do not delete older bundles.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aidescend/multimedia-to-obsidian (version 1.0.0)

--- name: multimedia-to-obsidian description: 将任意多媒体文档导入 Obsidian 知识库。支持 PPT、PDF、DOCX、图片等格式，自动提取每一页/每一张图片，使用多模态模型理解内容，生成文字描述后存入 OB。适用于：(1) 整理培训课件 (2) 迁移笔记到 OB (3) 将图片资料转为结构化知识。 --- # 多媒体导入 Obsidian 将 PPT、PDF、DOCX、图片等多媒体文档导入 Obsidian，每页/每张图片自动生成文字描述。 ## 功能特点 - 支持多种格式：PPT、PDF、DOCX、图片（PNG、JPG 等） - 自动提取媒体文件 - 调用多模态模型理解内容 - 自动分类到 OB 仓库 ## 使用方法 ### 前提条件 1. 安装依赖： ```bash brew install pandoc poppler pip install python-pptx pillow ``` 2. 设置环境变量（至少一个）： ```bash # MiniMax export MINIMAX_API_KEY="your-key" export MINIMAX_API_HOST="https://api.minimaxi.com" # OpenAI export OPENAI_API_KEY="your-key" # Anthropic export ANTHROPIC_API_KEY="your-key" ``` ### 调用脚本 ```bash python3 <skill>/scripts/media_to_obsidian.py <源文件/目录> <输出OB目录> [options] ``` **参数：** - 源文件/目录：单个文件或包含多媒体文件的目录 - 输出目录：Obsidian 仓库路径 - 选项： - `--format ppt|pdf|docx|image|all` : 指定格式，默认 all - `--model minimax|openai|anthropic` : 指定模型，默认 minimax - `--category 分类名` : 指定输出分类目录 ### 示例 ```bash # 导入 PPT 课件 python3 ~/.openclaw/workspace/skills/multimedia-to-obsidian/scripts/media_to_obsidian.py \ /path/to/培训课件.pptx \ /path/to/Obsidian \ --format ppt --category 培训 # 导入图片到 OB python3 ~/.openclaw/workspace/skills/multimedia-to-obsidian/scripts/media_to_obsidian.py \ /path/to/images/ \ /path/to/Obsidian \ --format image --category 素材 # 批量导入目录下的所有文档 python3 ~/.openclaw/workspace/skills/multimedia-to-obsidian/scripts/media_to_obsidian.py \ /path/to/documents/ \ /path/to/Obsidian ``` ## 输出结构 ``` Obsidian仓库/ ├── 培训/ │ └── 培训课件.md (含每页图片理解) ├── 素材/ │ └── 图片1.md │ └── 图片2.md └── 导入/ └── 文档名.md ``` ## 工作流程 1. **提取媒体**：从 PPT/PDF/DOCX 提取每一页为图片 2. **理解内容**：调用多模态模型理解每张图片 3. **生成描述**：将图片描述写入 Markdown 4. **分类存储**：按指定分类存入 OB


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: qujingyang28/jaka-robotics-control (version 1.0.1)

# JAKA Robotics Control Skill 节卡机器人控制技能，支持 Zu20/Zu12/Zu7/MiniCobo 全系列协作机器人。 ## 功能特性 - ✅ 关节运动控制 - ✅ 直线运动控制 - ✅ 状态读取 - ✅ 末端工具 I/O 控制 - ✅ gRPC 通信（低延迟、高可靠） ## 快速开始 ### 1. 安装 JAKA SDK ```bash pip install jaka-sdk ``` 或访问 JAKA 官网下载： https://www.jaka.com/docs/guide/SDK/introduction.html ### 2. 配置机器人 IP 编辑 `jaka_cmd.py`，修改： ```python ROBOT_IP = "192.168.57.128" # 改成你的 MiniCobo IP ``` ### 3. 使用 Python API ```python from jaka_skill import JAKARobot robot = JAKARobot("192.168.57.128", use_grpc=True) robot.connect() # 关节运动 (弧度) robot.move_joint([0, 0, 0, 0, 0, 0], speed=0.5) # 直线运动 (mm, rad) robot.move_linear([300, 0, 200, 3.14, 0, 0], speed=100) # 读取状态 state = robot.get_state() print(state['joints_deg']) robot.disconnect() ``` ### 4. 命令行工具 ```bash # 读取状态 python jaka_cmd.py state # 回零 python jaka_cmd.py home # 关节运动 (角度) python jaka_cmd.py joint 90 90 90 90 90 90 # 直线运动 (mm, 度) python jaka_cmd.py linear 300 0 200 180 0 0 ``` ## 支持机型 - JAKA Zu20（实验室主力） - JAKA Zu12 - JAKA Zu7 - JAKA MiniCobo（本技能测试机型） ## 环境要求 - Python 3.8+ - JAKA SDK V2.3.1+ - Windows ## 安全提示 ⚠️ 首次使用前请确保： - 工作空间内无人 - 急停按钮可用 - 先在低速环境测试 - T1 模式调试 ## 作者 JMO / Robotqu (青岛) - 网站：robotqu.com - B 站：Robot_Qu 机器人社区 ## 许可证 MIT-0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: hone1983/lyrics-search (version 1.0.0)

--- name: lyrics-search description: Search song lyrics by title and artist using the LrcApi public API. Use when the user asks to find, display, or print lyrics for a song. --- # Lyrics Search Search lyrics via the LrcApi public API (`api.lrc.cx`). ## API ``` GET https://api.lrc.cx/lyrics?title={title}&artist={artist} ``` - Returns LRC format (with timestamps) as `text/plain` - `artist` is optional but improves accuracy - URL-encode Chinese/special characters ## Usage 1. Fetch lyrics via `web_fetch` on the API URL 2. The response is LRC format with `[mm:ss.xxx]` timestamps and metadata lines at the top 3. Strip timestamps and metadata lines (credits, producer info at `[00:00]`–`[00:24]`) for clean display 4. For printing: format as plain text with song title, artist, and credits header ## Example ``` web_fetch("https://api.lrc.cx/lyrics?title=世界赠予我的&artist=王菲") ``` ## Notes - Public API may be slow; set a reasonable timeout - If no results, try with only `title` (omit `artist`) - If still no results, try alternate song name spellings


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a85012712/medical-report-query (version 1.0.0)

--- name: Medical Report Query description: 醫療報表查詢技能 - Oracle/SQLServer醫療資料庫報表查詢與分析。適用於診所、社區醫院的日常營運報表需求。觸發詞：報表、查詢、門診、住院、藥品、醫保、統計。 version: 1.0.0 author: 贾维斯 slug: medical-report-query --- # Medical Report Query 醫療報表智能查詢技能，專為診所和社區醫院設計。 ## 功能 ### 1. 門診報表 - 會員註冊統計 - 醫生工作量統計 - 收費明細查詢 - 科室收入分析 ### 2. 住院報表 - 住院人數統計 - 床位使用率分析 - 出院結算查詢 - 費用結構分析 ### 3. 藥品報表 - 藥品庫存查詢 - 藥品消耗統計 - 採購訂單追踪 - 效期预警 ### 4. 醫保報表 - 醫保結算對帳 - 統籌基金統計 - 自付比例分析 ## 使用方式 **門診統計：** ``` 查詢本月門診人數統計 ``` **藥品庫存：** ``` 查詢某藥品的庫存情況 ``` **醫保對帳：** ``` 生成本月醫保結算對帳單 ``` ## 支援資料庫 - **HIS庫**：Oracle - 門診/住院/醫保/藥品 - **ERP庫**：LXls - 會員/績效/庫存 ## 注意事項 - 報表僅供參考，具體數字需與系統核對 - 敏感資料需進行脫敏處理 - 報表格式可自訂（Markdown/Excel/HTML）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: naamah75/nextdns (version 1.0.0)

--- name: nextdns description: Query and troubleshoot NextDNS via the NextDNS API, especially when NextDNS is used as Technitium DNS upstream. Use for NextDNS profile inspection, analytics, recent logs, blocked-domain diagnosis, DNS upstream health checks, or creating safe read-only helper commands before any NextDNS configuration change. --- # NextDNS Use this skill for read-first NextDNS API work. Prefer inspection and diagnostics; ask Igor before any write, delete, log clear, profile edit, denylist/allowlist mutation, or monitoring that could expose sensitive browsing data externally. ## Helper Use the bundled helper unless a task needs an unsupported endpoint: ```bash python3 skills/nextdns/scripts/nextdns_helper.py --help ``` Authentication defaults: - `NEXTDNS_API_KEY` for the API key. - `NEXTDNS_PROFILE_ID` for the profile id. Examples: ```bash # List profiles available to the API key NEXTDNS_API_KEY=... python3 skills/nextdns/scripts/nextdns_helper.py profiles # Get profile config python3 skills/nextdns/scripts/nextdns_helper.py profile # Last 24h status summary python3 skills/nextdns/scripts/nextdns_helper.py analytics status --from -24h # Top blocked domains in last 24h python3 skills/nextdns/scripts/nextdns_helper.py analytics domains --from -24h --status blocked --limit 20 # Recent blocked logs for a domain/search term python3 skills/nextdns/scripts/nextdns_helper.py logs --from -24h --status blocked --search example.com --limit 50 ``` The helper prints JSON with: - `ok`: request success boolean. - `status`: HTTP status if available. - `url`: requested URL without API key. - `response`: decoded API response. ## Workflow 1. Verify credentials with `profiles` before diagnosing deeper. 2. Identify the profile id; set `NEXTDNS_PROFILE_ID` or pass `--profile`. 3. For general health, check `analytics status --from -24h`, `analytics protocols --from -24h`, and `analytics domains --from -24h --status blocked`. 4. For Technitium upstream questions, correlate Technitium client/upstream behavior with NextDNS `logs` and `analytics protocols`. 5. For block/allow questions, inspect logs and reasons first. Do not modify lists unless Igor explicitly confirms the exact domain/action. ## API reference Read `references/api-summary.md` when endpoint details, pagination, time-series parameters, logs, or response shapes matter. ## Safety notes - NextDNS logs can reveal browsing/device activity. Summarize minimally and avoid pasting sensitive domains unless needed. - API is marked beta by NextDNS; handle response changes defensively. - Do not call destructive endpoints like `DELETE /profiles/:profile/logs` without explicit confirmation. - Before config writes, export/read current profile and save a timestamped backup in the workspace or another user-approved location.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: if530770/xhs-copywriter (version 1.0.3)

--- name: xhs-copywriter description: 小红书爆款笔记创作工具。日均爬取分析2000+平台实时爆款笔记，输入核心关键词即可精准匹配同赛道当下最热爆文样本，全维度拆解爆款结构、开头钩子套路、干货密度、互动引导技巧，自动生成符合平台调性、可直接发布的完整合规笔记文案。支持新人博主快速获取内容灵感，降低创作门槛；内容运营提升产出效率，完成内容KPI；账号冷启动阶段匹配赛道热点，提高内容曝光概率 ; 矩阵账号批量产出差异化合规内容。触发词："小红书笔记生成"、"小红书文案"、"爆文模板"、"小红书爆文"、"小红书热门"。 version: 1.0.0 --- # 小红书内容生成 ## 简介 xhs-copywriter 是一款专为小红书内容创作者打造的一站式笔记生成工具，把「找对标 → 拆规律 → 写正文」串成一条完整流水线。 通过简单的一句话触发，你可以： - 📊 **自动抓取爆款样本**：按关键词从多维榜单中获取当下热门内容，数据真实、规律鲜活 - 🧠 **AI 深度拆解规律**：提炼标题结构、开头钩子、干货密度与互动话术等可复用爆款公式 - ✍️ **直出完整文案**：生成推荐标题 × 6 + 正文 + 标签 + 公式来源，可直接发布，无需二次加工 适用于个人博主、品牌运营、MCN 机构等有持续内容产出需求的用户。 --- ## 功能特性 ### 🎯 核心功能 - **📡 多维爆款数据采集**：覆盖新手友好爆款、当日点赞爆款、当日增长爆款、持续增长爆款四大榜单维度，样本更全面 - **🧩 爆款公式自动提炼**：从标题模式、开头结构、内容节奏、互动设计等多维度拆解规律，不暴露原始数据 - **✍️ 完整文案一键生成**：输出推荐标题（6个）、正文、标签列表、爆款公式说明，格式即发即用 - **🌐 热点资讯自动融入**：生成前联网补充最新行业动态与热点词汇，内容不过时 ### ✨ 特色亮点 - **🎨 风格自适应**：支持用户提供个人写作风格样本，生成内容更贴近本人语气 - **⚡ 无账号无 Key**：无需注册小红书账号或配置 API，安装依赖即可使用 - **📋 来源透明**：输出结果附带参考爆款笔记链接与数据来源说明，规律可溯源 --- ## 使用指南 ### 基础使用（3 步完成） #### 第 1 步：说出你的需求 直接用自然语言告诉我你想要什么，例如： > 「帮我写一篇关于夏日防晒的小红书笔记」 #### 第 2 步：（可选）提供风格参考 如果你有自己常用的写作风格，可以贴几段文字，文案会更贴近你的语气。没有也完全没关系，直接跳过即可。 #### 第 3 步：获取完整文案 工具自动完成数据抓取 → 规律拆解 → 内容生成，最终交付： - 📌 推荐标题（6 个，含数字型 / 痛点型 / 悬念型等多种风格） - 📝 完整正文（含开头钩子、正文模块、互动收尾） - 🏷️ 标签列表（10 个左右，覆盖核心词 + 长尾词） - 🔍 爆款公式来源（参考笔记链接 + 互动数据） ### 常用命令速查 | 命令示例 | 功能 | | ------------------------------------ | ---------------------------------- | | `帮我写一篇关于 [话题] 的小红书笔记` | 生成完整笔记文案 | | `帮我找 [话题] 的爆款笔记` | 查找并展示近期热门对标内容 | | `分析 [话题/领域] 的爆款规律` | 输出该领域标题、结构、话术规律总结 | | `生成 [话题] 方向的文案` | 同「写笔记」，支持模糊话题输入 | --- ## 使用场景 | 场景 | 角色 | 需求描述 | 使用方式 | | ------------------- | ------------------- | ---------------------------------------- | ------------------------------------------------ | | 新人博主找内容方向 | 0 粉新手创作者 | 不知道写什么内容，头部博主参考性太低 | 输入所在领域关键词，自动生成可模仿的爆款文案结构 | | 日常选题 + 快速产稿 | 个人博主 / 运营 | 每周需要持续输出，苦于选题和动笔慢 | 每次直接说话题，获取完整文案，节省构思和写作时间 | | 品牌内容跟热点 | 快消 / 美妆品牌运营 | 需要结合平台热点做符合用户喜好的推广内容 | 按产品卖点触发，生成融合近期热词的种草笔记 | | MCN 批量内容生产 | MCN 内容团队 | 多账号同时运营，需要高效产出差异化内容 | 多个话题连续触发，快速获取不同风格的文案素材 | | 爆款规律研究 | 内容策略研究者 | 想了解某领域什么样的内容容易出圈 | 发起「分析 XX 领域爆款规律」，获取结构化规律报告 | --- ## 注意事项 - **完整流程**：工具会自动走完「抓数据 → 拆规律 → 联网补热点 → 生成文案」全流程，无需手动干预 - **数据来源**：爆款数据来自实时榜单脚本抓取，非人工编造；内容规律基于真实数据提炼，非 AI 臆想 - **合规输出**：生成内容符合小红书社区规范，不含违规导流、虚假宣传等风险表述 - **原始数据不外露**：工具不会将原始数据表格直接甩给用户，只呈现提炼后的结论与可用文案


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chenggongdu/xiaohongshu-writer-expert (version 1.0.0)

--- name: xiaohongshu-writer-expert description: 根据任意主题生成小红书爆款文案，自动匹配8种风格模板，输出包含正文、配图描述、封面文案和搜索关键词的完整图文方案 version: 1.0.0 --- # 小红书爆款图文写作专家 你是一位资深小红书内容创作专家，擅长将任意主题转化为吸引眼球、高互动率的爆款文案。 ## 核心能力 - 自动判断主题类型，匹配最合适的风格模板 - 生成200-300字年轻化、口语化文案 - 提供完整的图文配套方案（配图描述、封面文案、搜索关键词） ## 可用风格模板 1. **好物推荐型**：产品推荐、购物分享、开箱测评 2. **干货教程型**：技能教学、知识分享、避坑指南 3. **情感成长型**：个人经历、成长感悟、励志分享 4. **探店打卡型**：美食探店、旅行打卡、咖啡店分享 5. **日常vlog型**：生活方式、日常记录、好习惯养成 6. **母婴亲子型**：育儿经验、宝宝好物、亲子互动 7. **穿搭美妆型**：妆容教程、穿搭分享、变美技巧 8. **创业副业型**：副业分享、搞钱经验、成长干货 ## 工作流程 1. **主题分析**：判断内容属于哪个类型，选择最匹配的模板 2. **文案创作**： - 开头：2-3个emoji + 10字以内爆款标题 + 一句话引入 - 正文：3-5个要点，每个带emoji，语言口语化，适当加空行 - 总结：金句/核心观点 + 行动呼吁 - 互动：自然引导点赞、收藏、评论 - 标签：5-8个相关话题 3. **图文配套**： - 配图描述：画面风格、主体、色调、氛围 - 封面文案：1-2句，分行排版，每行不超过5字 - 英文关键词：3-5个用于图库搜索 ## 文案风格要点 - 语言年轻化、口语化，善用"咱就是说"、"真的绝了"、"谁懂啊"等网络用语 - 适当使用括号补充细节，增加真实感 - 每段文字不宜过长，手机阅读舒适为主 - emoji与文字比例适中，不喧宾夺主 - 互动语要具体，不要只说"欢迎评论" - 标题要有信息增量，让人有点击欲望 ## 输出格式 直接输出以下结构，无需额外说明： ## 📝 小红书图文 [emoji] [标题] [空行] [正文内容，适当加空行分隔] [空行] [互动引导语] [空行] [标签列表] --- ## 🎨 配图方案 **配图描述**：[描述图片风格、画面内容、色调、构图] **封面文案**： [文案第1行] [文案第2行] [文案第3行] **搜索关键词**：[keyword1, keyword2, keyword3, keyword4, keyword5]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: if530770/finance-intel (version 1.0.0)

--- name: finance-intel description: 财经情报局——专业的财经新闻情报中心；聚合12+权威财经源，智能分析利好利空信号，评估对个股/行业/大盘的影响级别，生成专业投资决策参考；当用户需要财经新闻、市场分析、行业动态或个股研究时使用 dependency: python: - requests==2.31.0 - beautifulsoup4==4.12.2 --- # Finance Intel - 财经情报局 ## 任务目标 - 本 Skill 用于：财经新闻聚合与专业投资简报生成 - 能力包含：12+ 财经源抓取、情感分析（利好/利空/中性）、影响评估（市场/行业/公司三级）、个股关联识别（A/H/N股）、操作建议 - 触发条件：用户提到"财经新闻"、"股市"、"市场动态"、"投资简报"、具体股票/行业名时激活 ## 前置准备 - 依赖说明：requests、beautifulsoup4 - 工作目录下创建 `finance-news/` 用于存放缓存和报告 ## 操作步骤 ### 标准流程 1. **分析用户需求** — 识别关键词（市场/A股/港股/美股/个股/行业） 2. **调用抓取脚本** — 根据需求指定参数 - 脚本调用示例：`python scripts/fetch_news.py --keyword "宁德时代,比亚迪" --limit 20` - 按市场筛选：`python scripts/fetch_news.py --market A`（A股）/ `--market HK`（港股）/ `--market US`（美股） - 指定数据源：`python scripts/fetch_news.py --source cls,wallstreet,eastmoney` 3. **解析 JSON 输出** — 脚本返回结构化新闻列表，包含 sentiment/impact_level/related_stocks/operation_advice 4. **生成投资简报** — 按模板组织输出（见下方输出格式） ### 情感分析逻辑 | 情感 | 关键词示例 | |------|-----------| | 利好（positive） | 政策扶持、超预期、订单、合作、回购、增持、重组 | | 利空（negative） | 监管处罚、亏损、减持、调查、诉讼、限制、降价 | | 中性（neutral） | 人事变动、常规公告、会议、数据发布无惊喜 | ### 影响评估规则 - 市场级：涉及"央行、美联储、国务院、GDP、CPI、利率、降准降息、战争、贸易"等宏观词 - 行业级：涉及"行业、板块、新能源、芯片、科技、金融"等 - 公司级：上述均无时，判定为公司级 ### 操作建议规则 | 情感 | 市场影响 | 行业影响 | 公司影响 | |------|---------|---------|---------| | 利好 | 关注 | 关注相关板块 | 关注 | | 利空 | 谨慎 | 谨慎/回避 | 谨慎 | | 中性 | 观望 | 观望 | 观望 | ## 使用示例 ### 示例 1：获取 A 股新能源板块简报 - 场景/输入：用户说"帮我看看今天新能源有什么财经新闻" - 预期产出：包含宁德时代、比亚迪等新能源个股的财经简报 - 关键要点：使用 `--keyword "新能源,宁德时代,比亚迪"` 筛选，A股市场自动关联 ### 示例 2：港美股开盘前速览 - 场景/输入：用户说"美股开盘前有什么要关注的" - 预期产出：美股相关新闻摘要，包含英伟达、特斯拉等个股动态 - 关键要点：使用 `--market US --source seekingalpha,futunn` ### 示例 3：监控特定股票新闻 - 场景/输入：用户说"腾讯和阿里最近有什么消息" - 预期产出：腾讯(0700.HK)、阿里巴巴(9988.HK)相关新闻汇总 - 关键要点：使用 `--keyword "腾讯,阿里,阿里巴巴"` ## 资源索引 - 脚本：见 [scripts/fetch_news.py](scripts/fetch_news.py)（用途：抓取财经源并生成带情感分析的JSON；参数：--source/--keyword/--market/--limit/--outdir） - 参考：见 [references/sentiment-rules.md](references/sentiment-rules.md)（何时读取：需要扩展情感分析关键词或调整股票映射表时） - 参考：见 [references/sources-config.md](references/sources-config.md)（何时读取：需要添加新数据源或调整抓取配置时） ## 注意事项 - 脚本依赖浏览器或 HTTP 请求库抓取网页，确保网络可达 - 情感分析基于关键词匹配，准确率约 70-80%，复杂语境需人工复核 - 操作建议仅供参考，不构成投资建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cinience/aliyun-wan-r2v (version 1.0.0)

--- name: aliyun-wan-r2v description: Use when generating reference-based videos with Alibaba Cloud Model Studio Wan R2V models (wan2.6-r2v-flash, wan2.6-r2v). Use when creating multi-shot videos from reference video/image material, preserving character style, or documenting reference-to-video request/response flows. version: 1.0.0 --- Category: provider # Model Studio Wan R2V ## Validation ```bash mkdir -p output/aliyun-wan-r2v python -m py_compile skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py && echo "py_compile_ok" > output/aliyun-wan-r2v/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-wan-r2v/validate.txt` is generated. ## Output And Evidence - Save reference input metadata, request payloads, and task outputs in `output/aliyun-wan-r2v/`. - Keep at least one polling result snapshot. Use Wan R2V for reference-to-video generation. This is different from i2v (single image to video). ## Critical model names Use one of these exact model strings: - `wan2.6-r2v-flash` - `wan2.6-r2v` Newer official releases may prefer the flash variant for lower latency and lower cost. ## Prerequisites - Install SDK in a virtual environment: ```bash python3 -m venv .venv . .venv/bin/activate python -m pip install dashscope ``` - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. ## Normalized interface (video.generate_reference) ### Request - `prompt` (string, required) - `reference_video` (string | bytes, required) - `reference_image` (string | bytes, optional) - `duration` (number, optional) - `fps` (number, optional) - `size` (string, optional) - `seed` (int, optional) ### Response - `video_url` (string) - `task_id` (string, when async) - `request_id` (string) ## Async handling - Prefer async submission for production traffic. - Poll task result with 15-20s intervals. - Stop polling when `SUCCEEDED` or terminal failure status is returned. ## Local helper script Prepare a normalized request JSON and validate response schema: ```bash .venv/bin/python skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py \ --prompt "Generate a short montage with consistent character style" \ --reference-video "https://example.com/reference.mp4" ``` ## Output location - Default output: `output/aliyun-wan-r2v/videos/` - Override base dir with `OUTPUT_DIR`. ## Workflow 1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files. ## References - `references/sources.md`


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