OpenClaw/clawhub-security-signals huggingface dataset:
Skill: shenmeng/shenmeng-football-betting-analyzer (version 1.3.1)

--- name: football-betting-analyzer description: 足彩分析助手 - 提供足球比赛数据分析、赔率分析、投注建议。支持基本面分析（球队战绩、伤停、对战历史）、赔率面分析（亚盘、欧赔、凯利指数）、投注策略建议。当用户需要分析足球比赛、获取投注建议、查询球队数据时使用。 metadata: openclaw: emoji: ⚽ --- # Football Betting Analyzer 足彩分析助手 > 💰 **本 Skill 已接入 SkillPay 付费系统** > - 每次调用费用：**0.01 USDT** > - 支付方式：BNB Chain USDT > - 请先确保账户有足够余额 专业的足球比赛数据分析和投注决策支持工具。 ## 核心能力 1. **基本面分析** — 球队战绩、伤停名单、主客场表现 2. **赔率面分析** — 亚盘、欧赔、凯利指数、赔率变化趋势 3. **历史数据** — 对战记录、近期状态、进球/失球统计 4. **投注建议** — 胜平负预测、让球分析、大小球建议 5. **资金管理** — 投注比例、风险控制、组合方案 ## 适用场景 | 场景 | 示例 | |------|------| | **赛前分析** | "分析今晚曼联vs利物浦的比赛" | | **赔率解读** | "这场比赛亚盘怎么看？" | | **投注建议** | "这场推荐怎么买？" | | **串关方案** | "今天有什么稳胆可以串？" | | **球队查询** | "曼城最近状态怎么样？" | | **联赛分析** | "英超争冠形势如何？" | ## 分析维度 ### 1. 基本面 (Fundamentals) - 联赛排名与积分差距 - 近期5/10场比赛战绩 - 主客场表现差异 - 伤停名单影响评估 - 战意分析（争冠/保级/欧战资格） - 历史对战记录 ### 2. 赔率面 (Odds Analysis) - **亚盘**: 初盘/即时盘、水位变化、盘口升降 - **欧赔**: 胜平负赔率、凯利指数、返还率 - **大小球**: 盘口、水位分析 - **赔率趋势**: 机构倾向判断 ### 3. 数据模型 - 预期进球 (xG) - 控球率与射门效率 - 进攻/防守效率指数 - 价值投注识别 ## 使用方法 ### 单场比赛分析 ``` 分析 [球队A] vs [球队B] 的比赛 ``` ### 赔率查询 ``` 查询 [比赛] 的亚盘/欧赔 ``` ### 投注建议 ``` 这场比赛推荐怎么买？ ``` ### 多场比赛对比 ``` 对比今天几场比赛的投注价值 ``` ## 数据来源 - **免费API**: API-Football、Football-Data.org - **赔率数据**: OddsAPI、各博彩公司公开赔率 - **新闻资讯**: 球队官网、体育媒体 ## 输出格式 分析报告包含： 1. 比赛基本信息 2. 基本面评估（★☆☆☆☆） 3. 赔率解读 4. 综合推荐 5. 风险提示 ## 注意事项 ⚠️ **免责声明**: - 本工具仅供数据分析参考，不构成投注建议 - 足球比赛具有不确定性，请理性购彩 - 请遵守当地法律法规，合法参与彩票活动 ## 技术栈 - Python 3.8+ - 数据获取: requests / aiohttp - 数据分析: pandas / numpy - 可视化: matplotlib (可选) ## 文件结构 ``` football-betting-analyzer/ ├── SKILL.md # 本文件 ├── config.json # 配置信息 ├── analyzer.py # 主分析脚本 ├── data_fetcher.py # 数据获取模块 ├── odds_analyzer.py # 赔率分析模块 └── utils.py # 工具函数 ```

path: requirements.txt
content: requests>=2.28.0
pandas>=1.5.0
numpy>=1.21.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xhmqq616/efficiency-hub (version 1.0.0)

--- name: efficiency-hub description: 效率中枢 - 一站式日常任务处理中心。整合搜索、保存、提醒、计划、复盘等日常高频需求。当用户说"效率"、"日常"、"快速处理"、"一站式"、"帮我做"、"太忙了"、"没时间"时触发。 --- # ⚡ Efficiency Hub - 效率中枢 **龙虾王子原创技能** --- ## 核心理念 > "一个命令，搞定日常" 把日常高频操作整合到一个技能里，不用记那么多技能名，需要什么直接说。 --- ## 五大功能模块 ### 🔍 模块1: 快速搜索 **一句话搞定**: 搜索文件、聊天记录、网上内容 | 需求 | 命令 | |------|------| | 搜索聊天 | "找找之前关于X的聊天" | | 搜索文件 | "找一下X文件" | | 全网搜索 | "搜一下X" | ### 💾 模块2: 快速保存 **一句话搞定**: 保存对话、记录想法、备份内容 | 需求 | 命令 | |------|------| | 保存对话 | "存档今天的对话" | | 记录想法 | "记一下X想法" | | 备份 | "备份X" | ### ⏰ 模块3: 快速提醒 **一句话搞定**: 设置提醒、定时任务 | 需求 | 命令 | |------|------| | 提醒 | "X分钟后提醒我Y" | | 定时 | "明天X点提醒Y" | | 循环提醒 | "每周X提醒Y" | ### 📋 模块4: 快速计划 **一句话搞定**: 制定计划、安排任务 | 需求 | 命令 | |------|------| | 日计划 | "制定今天的计划" | | 周计划 | "规划这周" | | 任务分解 | "拆解X任务" | ### 🔄 模块5: 快速复盘 **一句话搞定**: 复盘总结、经验提取 | 需求 | 命令 | |------|------| | 日复盘 | "今天复盘一下" | | 经验提取 | "总结一下X的经验" | | 改进建议 | "怎么改进X" | --- ## 使用示例 ### 场景1: 老大突然想到一个想法 ``` 用户: "记一下，产品要简约，不要复杂功能" → 提取关键信息 → 保存到 MEMORY.md + 记录到当日日志 ``` ### 场景2: 老大要搜索之前的内容 ``` 用户: "找一下之前关于股票分析的对话" → 搜索 memory/chat-*.md → 返回结果 ``` ### 场景3: 老大要设置提醒 ``` 用户: "30分钟后提醒我开会" → 调用 cron → 设置提醒 ``` ### 场景4: 老大要做日计划 ``` 用户: "制定今天计划" → 读取今日任务 → 智能排序 → 输出计划 ``` ### 场景5: 老大要进行复盘 ``` 用户: "复盘一下今天的对话" → 汇总今日对话 → 提取关键决定 → 输出复盘报告 ``` --- ## 懒人模式 **当老大说"帮我做"、"太忙了"、"没时间"时**: 1. 理解需求 2. 自动判断需要哪个技能 3. 调用对应技能 4. 返回结果 --- ## 快速命令表 | 命令 | 执行 | |------|------| | "存档" | 保存当前对话 | | "找X" | 搜索聊天记录 | | "提醒我X" | 设置提醒 | | "计划" | 生成日计划 | | "复盘" | 日复盘报告 | | "总结" | 提取关键信息 | --- ## 与其他技能的协作 | 需求 | 调用技能 | |------|----------| | Excel处理 | automate-excel | | 股票分析 | stock-analyst-ai-stock-analysis | | 视频生成 | seedance-video-gen | | 决策支持 | master-decision-system | | 记忆存档 | chat-memory-v2 | --- ## 为什么需要这个技能？ **痛点**: 老大需要各种技能，但记不住每个技能怎么用 **解决方案**: 只需要说需求，效率中枢自动判断调用哪个技能 --- ## 参考文件 - 快速命令库: [references/commands.md](references/commands.md)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: bytesagain3/generator (version 2.0.0)

--- name: generator version: "2.0.0" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills license: MIT-0 tags: [generator, tool, utility] description: "Create placeholder data, test fixtures, and sample datasets for dev work. Use when generating mocks, building fixtures, or scaffolding content templates." --- # Generator Content generator — create placeholder data, test fixtures, sample datasets, and templates. ## Commands | Command | Description | |---------|-------------| | `generator help` | Show usage info | | `generator run` | Run main task | | `generator status` | Check current state | | `generator list` | List items | | `generator add <item>` | Add new item | | `generator export <fmt>` | Export data | ## Usage ```bash generator help generator run generator status ``` ## Examples ```bash # Get started generator help # Run default task generator run # Export as JSON generator export json ``` ## Output Results go to stdout. Save with `generator run > output.txt`. ## Configuration Set `GENERATOR_DIR` to change data directory. Default: `~/.local/share/generator/` --- *Powered by BytesAgain | bytesagain.com* *Feedback & Feature Requests: https://bytesagain.com/feedback*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wxd9199/jetson-cuda (version 1.0.0)

# jetson-cuda NVIDIA Jetson CUDA 工具集。提供 CUDA 版本查询、GPU 设备信息等功能。 ## 功能 - CUDA 版本查询 (nvcc --version) - GPU 设备信息 (nvidia-smi) - CUDA 库路径 ## 使用方法 ```bash # 查看 CUDA 信息 jetson-cuda info # 检查 nvcc which nvcc nvcc --version ``` ## 依赖 - CUDA Toolkit - nvidia-smi ## 环境要求 - NVIDIA Jetson (AGX Orin, Xavier, Nano, etc.) - CUDA 11.x or 12.x


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/bookmarks (version 1.0.0)

--- name: Bookmarks description: Build a unified bookmark system that imports saves from all your platforms into one organized, actionable collection. metadata: {"clawdbot":{"emoji":"🔖","os":["linux","darwin","win32"]}} --- ## Core Behavior - Import saves silently from connected platforms - Auto-tag and organize without user effort - Only surface when useful or asked - Create `~/bookmarks/` as workspace ## Philosophy User saves things and forgets — that's fine. The system: - Works silently in background - Never interrupts unless configured to - Is there when they search or ask - Optionally: periodic summaries if wanted ## File Structure ``` ~/bookmarks/ ├── saves.md # All saves, tagged ├── sources.md # Connected platforms ├── preferences.md # How user wants to use it └── reports/ # Generated summaries ``` ## Saves Format Tags, not folders: ```markdown # saves.md ## 2024-02-11 - [Thread on AI agents](url) source: X | tags: #tech #ai - [Kitchen inspiration](url) source: Pinterest | tags: #home #design - [Article user shared](url) source: manual | tags: #productivity ``` ## Setup ### Minimal Start 1. Connect sources (X, YouTube, Reddit, Pinterest, etc.) 2. Import saves — silently, no questions 3. Auto-tag based on content 4. Done — works in background ### Ask Later (After They Have Saves) "You've saved 50+ things. Want me to: - Send weekly summary of themes? - Alert when something relates to a project? - Periodically ask 'still relevant?' on old saves? - Just stay silent until you search?" ## Per-Person Preferences ```markdown # preferences.md ## Style - passive: just organize, never interrupt - digest: weekly summary of what I saved - active: connect to projects, resurface relevant - cleanup: periodically ask about stale saves ## Reports (if wanted) - frequency: weekly/monthly/never - focus: themes, actionables, or both ``` ## Sources ```markdown # sources.md - X: bookmarks ✓, likes ✗ - YouTube: watch later ✓ - Reddit: saved ✓ - Pinterest: pins ✓ - Instagram: saved ✓ - TikTok: favorites ✓ - Manual: ✓ Note: Default to explicit saves only. Ask before importing likes (too noisy). ``` ## Reports (Optional) ```markdown # reports/2024-02-week-6.md ## What You Saved This Week - 8 saves total - Themes: AI (4), recipes (2), travel (2) ## Patterns - You're saving a lot about AI agents lately ## Actionables - That tutorial from 3 weeks ago — tried it? ## Stale - 12 saves from 6+ months ago, unvisited ``` ## What To Surface Only when configured or asked: - "Your saves this week: mostly AI and design" - "Old save relates to what you're working on" - "15 dead links cleaned up" ## Searching When user asks: - "What did I save about X?" → search tags + content - "Saves from Pinterest about home" → filter source + topic - "That article about Y" → fuzzy search ## What NOT To Do - Interrupt during setup with many questions - Require folder management - Notify by default — silence first - Assume everyone wants the same workflow - Lose source context (where it came from)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sydpz/openapi-design (version 1.0.0)

--- name: api-design description: > API design specification and best practices skill. Use when: designing new APIs, reviewing existing APIs, generating API documentation, standardizing interface specifications. Chinese triggers: API设计, 接口规范, REST API, OpenAPI, 接口文档, API审查. English triggers: design API, REST API, OpenAPI, API documentation, API review, endpoint design. Provides: design principles, naming conventions, HTTP status codes, error formats, versioning strategy, OpenAPI templates, and review checklists. --- # API Design Skill API 设计规范与最佳实践技能，提供从设计到文档生成的完整指导。 ## 核心能力 1. **API 设计原则** — RESTful 设计最佳实践 2. **命名规范** — 资源、端点、参数命名标准 3. **HTTP 状态码** — 完整状态码参考 4. **错误响应格式** — 统一错误结构 5. **版本管理** — URL 版本策略 6. **OpenAPI 模板** — 快速生成 API 文档 7. **审查清单** — 设计审查检查点 ## 使用场景 ### 场景 1: 设计新 API 1. 读取 `references/common/design-principles.md` 了解设计原则 2. 读取 `references/design/resource-modeling.md` 进行资源建模 3. 读取 `references/design/endpoint-design.md` 设计端点 4. 使用 `references/templates/openapi3-template.md` 生成 OpenAPI 文档 5. 使用 `references/review/api-review-checklist.md` 自查 ### 场景 2: 审查现有 API 1. 读取 `references/review/api-review-checklist.md` 2. 按清单逐项审查 3. 输出审查报告 ### 场景 3: 规范化接口 1. 读取 `references/common/naming-conventions.md` 检查命名 2. 读取 `references/common/error-response-format.md` 检查错误格式 3. 读取 `references/common/versioning-strategy.md` 检查版本策略 4. 生成整改建议 ## 文件结构 ``` api-design/ ├── SKILL.md └── references/ ├── common/ │ ├── design-principles.md │ ├── naming-conventions.md │ ├── http-status-codes.md │ ├── error-response-format.md │ └── versioning-strategy.md ├── design/ │ ├── resource-modeling.md │ ├── endpoint-design.md │ └── security-best-practices.md ├── templates/ │ └── openapi3-template.md ├── review/ │ └── api-review-checklist.md └── documentation/ └── doc-generation-guide.md ```

path: aidlc-docs/audit.md
content: # AI-DLC Audit Log

## 2026-03-27T08:23:00Z
### Event: Workflow Started
**Skill**: api-design
**Trigger**: 批量开发配套技能，陆续上传 ClawHub



Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mzfshark/axodus-implementation-planner (version 1.0.0)

--- name: implementation-planner description: Turn a feature idea into a concrete technical implementation plan. metadata: author: RedHat Dev version: 1.0.0 owner: RedHat Dev Agent category: core --- # SKILL: implementation-planner ## Purpose Convert a feature idea into a concrete technical execution plan (architecture, modules, interfaces, validations, and rollout). ## When to Use - The request requires design decisions before code. - Multiple components must be coordinated (frontend/backend/infra/contracts). - The user asked for an â€œimplementation planâ€ or â€œarchitectureâ€. ## Inputs - `feature_description` (required, string): what to build and why. - `constraints` (optional, string[]): non-negotiables (security, budget, tooling, timeline). - `environment` (optional, object): runtime (OS, container), deploy targets, CI. - `existing_system` (optional, string): relevant current architecture and boundaries. ## Steps 1. Define success criteria (what â€œdoneâ€ means) and explicit non-goals. 2. Identify actors and interfaces (users, services, contracts, external APIs). 3. Choose an architecture that minimizes risk and change surface. 4. Define modules and ownership boundaries (what lives where). 5. Specify data flow (inputs, outputs, persistence, logs/audit trail). 6. Specify validation path: - unit tests - integration tests - security checks (as applicable) 7. Define rollout: - feature flags / guarded modes - backwards compatibility - migration steps (if any) 8. List open questions and assumptions; ask for clarification when risk is material. ## Validation - Plan satisfies all stated constraints. - Every module has an interface and responsibility. - Testing/validation is included (not â€œlaterâ€). - Rollout avoids accidental production impact. ## Output Structured plan (example schema): ```yaml overview: "<1 paragraph>" modules: - name: "<module>" responsibility: "<what it owns>" interfaces: ["<api/events/files>"] data_flow: inputs: ["..."] outputs: ["..."] validation: unit: ["..."] integration: ["..."] rollout: guardrails: ["..."] open_questions: ["..."] ``` ## Safety Rules - Do not select tools that violate constraints. - Do not propose deployments that can impact production without explicit gating. - Prefer simplest architecture that meets requirements. ## Example Feature: â€œAdd webhook ingestion with idempotency and audit logs.â€ Output (excerpt): ```yaml modules: - name: "webhook-controller" responsibility: "request validation + signature checks" - name: "event-store" responsibility: "persist raw payload + processing status" validation: integration: ["replay same event id results in no duplicate side effects"] ```


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

--- name: anime-drama version: "1.0.0" description: "将小说原文自动转换为动漫短剧。用户输入故事文本，系统自动完成：分镜脚本生成 → 文生图（RH AI应用）→ 图生视频（RH AI应用）→ ffmpeg合并成片。支持竖屏9:16短视频输出。" homepage: https://github.com/win4r/ClawTeam-OpenClaw metadata: { "openclaw": { "emoji": "🎬", "requires": { "bins": ["python3", "curl", "ffmpeg"], "skills": ["runninghub"] }, "primaryEnv": "RUNNINGHUB_API_KEY" } } --- # 动漫短剧生成技能 (Anime Drama Pipeline) 输入小说原文 → 输出竖屏动漫短视频（分镜脚本 + 图片 + 视频片段 + 合并成片） ## 核心流程 ``` 小说原文 ↓ 分镜脚本生成（按段落拆分为多个镜头） ↓ 并行/逐个执行： ├─ 文生图（RH AI应用 AppId: YOUR_IMAGE_APP_ID，节点 12:value） └─ 图生视频（RH AI应用 AppId: YOUR_VIDEO_APP_ID，节点 325:value + 269:image） ↓ ffmpeg 合并所有视频片段 ↓ 最终成片（竖屏 1080×1920，MP4） ``` ## 必读参考文档 - `{baseDir}/references/pipeline-usage.md` — 完整使用说明 - `{baseDir}/references/ffmpeg-install.md` — ffmpeg 安装指南 - `{baseDir}/references/runninghub-ai-app-notes.md` — RH AI应用调用要点 ## 核心脚本 ```bash python3 {baseDir}/scripts/anime_drama_pipeline.py <小说原文文件> [输出目录] ``` ## Pipeline 脚本位置 `{baseDir}/scripts/anime_drama_pipeline.py` ## RH API 关键参数（永久记忆） | 用途 | AppID / 端点 | 节点 | |------|-------------|------| | 文生图 | `YOUR_IMAGE_APP_ID` | `12:value`（单节点，直接传 prompt） | | 图生视频 | `YOUR_VIDEO_APP_ID` | `325:value`（文本）+ `269:image`（图片） | **图生视频调用前必须先上传图片：** 1. POST `/task/openapi/upload` 上传本地图片 2. 取返回值中的 `fileName`（格式 `api/xxx.png`） 3. 将 `fileName` 作为 `269:image` 的值传入 ## 视频合并（ffmpeg） ```bash # 生成文件列表 cat > /tmp/video_list.txt << 'EOF' file '/path/shot_001_vid.mp4' file '/path/shot_002_vid.mp4' EOF # 合并（竖屏 1080×1920） ffmpeg -y -f concat -safe 0 -i /tmp/video_list.txt \ -c:v libx264 -pix_fmt yuv420p \ -vf "scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2:black" \ output.mp4 ``` ffmpeg 路径（已安装到）： `/usr/local/lib/python3.10/dist-packages/imageio_ffmpeg/binaries/ffmpeg-linux-x86_64-v7.0.2` ## 分镜逻辑 默认按 `\n` 换行分割，每段 = 一个镜头，每个镜头生成一张图 + 一段视频。 如需更智能的分镜，可传入 LLM 优化每个镜头的描述文字。 ## 输出格式 - 图片：PNG，~1.7-2.0MB/张 - 视频：MP4（H.264 + AAC），每个镜头默认 5 秒 - 最终成片：MP4，1080×1920（竖屏），16:9 源视频自动 pad 为竖屏 ## 常见错误处理 | 问题 | 原因 | 解决方法 | |------|------|---------| | 文生图失败 | AI 应用未在网页端运行过 | 先在 RH 网页端手动运行一次应用 | | 图生视频失败 | 图片未上传到 RH | 必须先调用 `/task/openapi/upload` 获取 fileName | | ffmpeg: command not found | 未安装 ffmpeg | 使用 imageio-ffmpeg 的内置 ffmpeg | | 超时中断 | 视频生成需 2-5 分钟/镜头 | 脚本可中断后继续运行（已生成的不会重复生成） | ## 中断后继续 脚本支持断点续传：已生成的图片/视频不会重复生成，中断后重新运行即可继续。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: joeyoung310/microtoutiao-content-lib (version 1.0.0)

--- name: microtoutiao-content-lib description: 今日头条微头条内容素材生成工具。当用户提到微头条、微头条素材、微头条怎么写、微头条内容、今日头条创作、头条爆款、情感故事、赚钱干货、知识科普等与微头条内容创作相关需求时使用此技能。 --- # 微头条素材库 专为今日头条微头条自动化发布设计的素材生成工具，支持情感故事、知识科普、赚钱干货等多种热门内容类型。 ## 核心能力 ### 1. 七大热门内容类型 | 类型 | 流量特征 | 适用场景 | |------|----------|----------| | 情感故事 | 极高，互动强 | 婆媳、夫妻、亲子、前任 | | 人情世故 | 高，争议强 | 随礼、借钱、邻居、同事 | | 知识科普 | 中高，稳定 | 健康、生活妙招、职场技能 | | 赚钱干货 | 高，粘性强 | 副业、省钱、理财、创业 | | 热点解读 | 爆发性强 | 社会民生、娱乐、政策 | | 生活技巧 | 高，收藏高 | 家务、收纳、烹饪 | | 奇闻轶事 | 不稳定 | 身边奇事、见闻 | ### 2. 模板结构 每个模板包含： - **标题公式**: 情绪钩子 + 悬念 + 数字/对比 - **正文结构**: 开头吸睛 → 内容展开 → 结尾引导 - **关键词布局**: 自然融入平台推荐关键词 - **配图建议**: 图片类型和风格建议 ### 3. 热点追踪 根据节日、节气、社会热点自动匹配素材方向： - 节日类：春节/端午/中秋/情人节 - 节气类：春夏秋冬季节性话题 - 热点类：社会事件/明星八卦/政策解读 ## 输出格式 ### 标题公式 ``` 矛盾型：[具体行为]让[身份]彻底[情绪动词]，结局没想到... 对比型：A vs B，区别真的太大了 提问型：你们遇到过这样的情况吗？ 数字型：[数字]个技巧/方法/秘密，一次性告诉你 ``` ### 正文结构 ``` [开头30字] 制造悬念/冲突/共鸣 [主体80-150字] 故事/知识/观点展开 [结尾20字] 引导评论/转发/关注 ``` ## 使用示例 **输入**: "生成一个职场干货类的微头条，主题是加薪技巧" **输出**: ``` 标题：老板绝不会告诉你的3个加薪秘密，学会第2个工资翻倍 正文： 在职场混了8年，我发现一个扎心的事实：会干活的不如会说话的。 今天分享3个我亲眼验证过的加薪技巧： 1️⃣ 每月主动汇报工作成果（不是等老板问） 2️⃣ 提加薪时机很重要：项目成功后的第一周 3️⃣ 准备好数据：你的贡献值多少钱 你们用过哪个？评论区说说 ``` ## 适用场景 - 每日定时发布自动化内容 - 热点话题快速跟进而缺乏素材时 - 批量生产多账号内容矩阵 - 新账号快速起号阶段


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jinboh68-prog/a-stock-vcp (version 1.0.9)

--- name: a-stock-vcp description: 📈 A股强势股捕捉器 - 基于VCP放量突破形态，精准捕捉强势启动点 author: 19 Years Senior Financial Analyst version: 1.0.8 tags: - A股 - VCP - 量化 openclaw_version: ">=2025.1.0" # 解决"可疑"标记的核心配置 endpoint: "https://a-stock-signals.vercel.app/v" auth_type: "x402" price: "0.01" currency: "USDC" chain: "Base" wallet: [REDACTED_SECRET] # 明确告知系统：数据是通过后端 API 提供的 capabilities: - web_access - api_call --- # 📈 A股强势股捕捉器 (VCP) ## 运行机制 (Runtime Logic) 本技能通过后端 API (https://a-stock-signals.vercel.app/v) 获取结构化行情数据。 1. **数据请求**：Agent 发起请求 2. **支付挑战**：触发 x402 协议，单次扣费 0.01 USDC 3. **数据解密**：支付完成后，后端返回经过处理的 VCP 放量突破筛选结果 ## 功能 基于 **VCP (Volatility Contraction Pattern)** 放量突破形态，精准捕捉强势启动点。 ### VCP特征 - 股价在一定区间内波动收缩 - 成交量逐渐萎缩 - 放量突破区间上沿 - 突破时成交量明显放大 ## 输入 - 市场：A股（默认） - 形态类型：VCP放量突破 ## 输出 - 推荐股票 3-5 只 - 每只包含： - 代码 + 名称 - 所处板块 - 当前价格 - 涨幅 - 放量情况 - 强势度 ⭐⭐⭐/⭐⭐/⭐ - 止损位 + 目标位 - 买入理由 ## 数据源 - 东方财富 / 同花顺（通过后端API获取） ## 价格 - 每次调用：0.01 USDC - 支付：x402 协议（Base 链 USDC） - 收款钱包：0x1a9275EE18488A20C7898C666484081F74Ee10CA ## 适用人群 - 短线交易者 - 技术分析爱好者 - 需要选股参考的投资者 ## 风险提示 - 本技能仅供参考，不构成投资建议 - 入场前请做好风险控制（建议-5%止损） - 过去表现不代表未来收益


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guilongzh/odps-sql (version 1.0.2)

--- name: "ODPS (MaxCompute) Data Query" description: | Use this skill when the user wants to query, analyze, or explore data in Alibaba Cloud ODPS (MaxCompute / 阿里云大数据计算服务). This skill executes SQL queries, lists tables, and inspects table schemas by running the odps_helper.py command-line script. Trigger this skill for requests like: querying ODPS data, listing MaxCompute tables, running SQL on ODPS, checking table structure, analyzing business data stored in ODPS, or any data exploration tasks involving Alibaba Cloud MaxCompute / ODPS. metadata: { "openclaw": { "requires": { "bins": ["python"], "env": ["ALIYUN_ACCESS_ID", "ALIYUN_ACCESS_SECRET", "ALIYUN_PROJECT_NAME", "ALIYUN_END_POINT"] } } } --- ## Setup (First-time only) 1. Copy the credential template and fill in your values: ```bash cd odps-sql/ cp config.example.env .env # Edit .env with your Alibaba Cloud credentials ``` 2. Activate your Python environment and install dependency: ```bash # conda users: conda activate <your-env> # venv users: source .venv/bin/activate pip install pyodps ``` ## Executing Commands Activate your Python environment first, then run all commands from the project root with: ```bash SCRIPT=odps-sql/scripts/odps_helper.py ``` ### List tables ```bash python $SCRIPT --list-tables ``` Filter by name: ```bash python $SCRIPT --list-tables --pattern <keyword> ``` ### Get table schema ```bash python $SCRIPT --describe <table_name> ``` ### Execute SQL query ```bash python $SCRIPT --query "<SQL statement>" [--limit <n>] ``` Default limit is 100 rows. ## Workflow for Data Tasks Follow this pattern when the user asks about ODPS data: 1. **Discover** — If the table name is unknown, run `--list-tables --pattern <keyword>` to find it. 2. **Inspect** — Run `--describe <table>` to understand columns, types, and partition structure. 3. **Query** — Construct the SQL and run `--query`. Always add a partition filter (`WHERE dt = '...'`) for partitioned tables to avoid full scans. 4. **Present** — Summarize the results clearly for the user. ## ODPS SQL Key Differences from Standard SQL | Feature | Standard SQL | ODPS SQL | |---------|-------------|----------| | String concat | `a \|\| b` | `CONCAT(a, b)` | | Current time | `NOW()` | `GETDATE()` | | Null coalesce | `IFNULL(x,y)` | `NVL(x, y)` | | Regex match | `REGEXP` | `RLIKE` | | Date literal | `'2024-01-01'` | `TO_DATE('2024-01-01','yyyy-mm-dd')` | Partition filter is **required** for partitioned tables (partition column is usually `dt`): ```sql SELECT * FROM table_name WHERE dt = '2024-01-01' LIMIT 100 ``` See `odps-sql/references/odps_sql_guide.md` for a full SQL reference. ## Error Handling - **`pyodps` not found** → Run install command in Setup step above - **Missing credentials** → Check that `odps-sql/.env` exists and all four fields are filled in - **Table not found** → Use `--list-tables --pattern` to find the correct name - **SQL syntax error** → Check the ODPS SQL differences table above; avoid MySQL/PostgreSQL-specific syntax


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: evezart/semantic-code-search (version 1.0.0)

--- name: semantic-code-search description: Semantic search engine for codebases that understands intent and finds functionally similar code. Use when searching code by meaning rather than text, finding similar implementations, discovering duplicate logic, or navigating unfamiliar codebases. Covers AST parsing, embedding generation, similarity search, and intent-based queries. --- # Semantic Code Search Find code by meaning, not just text. Search across codebases using natural language intent. ## Quick Start ```python from code_search import CodeIndex, SemanticSearch index = CodeIndex("/path/to/codebase") index.build() # Parse AST, generate embeddings search = SemanticSearch(index) results = search.query("how is authentication handled?") results = search.similar_to("src/auth/login.py:validate_token") ``` ## How It Works 1. **Parse** — Walk codebase, extract functions/classes with AST 2. **Embed** — Generate vector embeddings from code + docstrings 3. **Index** — Store in vector index with metadata (file, line, type) 4. **Search** — Query by intent or find similar code ## Search Types - **Intent search**: "find error handling patterns" → returns matching code - **Similarity search**: Given a function, find others doing the same thing - **Structural search**: Find all functions matching a call pattern - **Duplicate detection**: Find code doing the same thing differently ## CLI ```bash python3 scripts/search.py index /path/to/codebase python3 scripts/search.py query "database connection setup" python3 scripts/search.py similar src/db/connect.py:10 python3 scripts/search.py duplicates ```


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