OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sunfang1cn/xhs-zh-sync-obsidian (version 1.0.1)

--- name: social-favorites-to-obsidian description: 将小红书和知乎收藏增量同步到 Obsidian：依赖 hctec-* 抓取技能，使用 CookieCloud 登录态，支持首次安装、依赖检查、分类规则、Markdown 导出和 OpenClaw 定时任务生成。当用户要安装、配置、诊断、归档、分类、导出或定时同步社交平台收藏到 Obsidian 时使用。 --- # Social Favorites to Obsidian 这个技能把“小红书/知乎收藏 -> 抓取 -> 分类 -> Obsidian 导出 -> 定时同步”做成可发布的安装流程。平台抓取由 hctec 原子技能负责，本技能负责安装引导、配置、状态、分类、导出和 cron 生成。 ## 工作流 1. 首次安装时不要直接使用默认值开跑。先读取 `references/first-install.md`，并向用户确认这些可选项： - CookieCloud 服务端：本机 Docker 安装，还是使用已有远程服务端；远程服务端需要地址、UUID 和 Password。 - Obsidian vault：自动创建并使用官方 Obsidian Sync，自动创建但只本地导出，还是使用已有本地 vault 目录。 - 官方 Obsidian Sync：需要 Obsidian 账号邮箱/用户名、登录密码或交互登录、远程 vault 名字或 ID，必要时还要端到端加密密码。 - 分类标准：由 LLM 初始化生成分类规则，还是用户稍后自行编辑。 2. 收集完首次安装选项后，再运行安装和初始化命令。例如： ```bash python install.py ``` 如果安装环境只能调用 scripts 目录，也可以运行： ```bash python scripts/interactive_install.py ``` 非交互拆分安装时再使用底层命令： ```bash python scripts/setup.py --init --install-skill-deps --install-python-deps --install-playwright ``` 3. 安装 hctec 第三方技能后，必须运行一次知乎格式补丁，避免知乎正文 plain 文本丢失段落和换行： ```bash python scripts/patch_hctec_zhihu_format.py ``` 详见 `references/dependencies.md`。 4. 首次安装时按用户选择部署 CookieCloud 服务端并安装浏览器插件。详见 `references/auth.md`。 5. 如果 CookieCloud 服务端不在运行本技能的机器上，用 `--cookiecloud-server-url http://<服务器IP>:8088` 初始化，或让用户手动填写 `COOKIECLOUD_SERVER_URL`、`COOKIECLOUD_UUID` 和 `COOKIECLOUD_PASSWORD`。 6. 首次安装时按用户选择配置 Obsidian 同步方式。官方 Sync 走 `obsidian-headless`、`ob login` 和 `ob sync-setup`；本地模式则只初始化本地目录。详见 `references/obsidian-sync.md`。 7. agent 判断是否首次安装时，运行： ```bash python scripts/setup.py --status --json ``` 8. 检查配置： ```bash python scripts/setup.py --doctor ``` 9. 同步并导出： ```bash python scripts/sync.py --platform xhs python scripts/sync.py --platform zhihu python scripts/export_obsidian.py --platform all --incremental ``` 10. 如果用户要自动化，生成 OpenClaw cron 配置： ```bash python scripts/install_cron.py --platform xhs --time 06:00 --print python scripts/install_cron.py --platform zhihu --time 05:00 --print ``` ## 按需阅读 - 首次安装访谈和选项决策：`references/first-install.md` - 首次安装和外部依赖：`references/dependencies.md` - CookieCloud 服务端、浏览器插件和 cookie 兜底：`references/auth.md` - 配置字段：`references/config.md` - Obsidian Headless Sync 和本地目录模式：`references/obsidian-sync.md` - 定时任务：`references/cron.md` - Obsidian 目录和分类：`references/obsidian-layout.md` ## 安全 不要把用户数据发布进技能包：`cookiecloud.env`、原始 Cookie、状态文件、日志、已下载笔记、图片、Obsidian 导出内容都只能作为运行时数据保存在用户本机。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ericrosenberg/strava-cycling-coach (version 1.0.0)

--- name: strava-cycling-coach description: Track and analyze cycling performance from Strava. Use when analyzing ride data, reviewing fitness trends, understanding workout performance, or providing insights on cycling training. Automatically monitors new rides and provides performance analysis. --- # Strava Cycling Coach Track cycling performance, analyze rides, and monitor fitness progression using the Strava API. ## Setup ### 1. Create Strava API Application Visit https://www.strava.com/settings/api and create an application: - Application Name: Clawdbot (or your preferred name) - Category: Data Importer - Club: (leave blank) - Website: http://localhost - Authorization Callback Domain: localhost Save your **Client ID** and **Client Secret**. ### 2. Run Setup Script ```bash cd skills/strava ./scripts/setup.sh ``` You'll be prompted for: 1. Client ID 2. Client Secret 3. Visit an OAuth URL to authorize 4. Copy the authorization code and complete setup with: ```bash ./scripts/complete_auth.py YOUR_CODE_HERE ``` ### 3. Configure Automatic Monitoring (Optional) To receive automatic ride analysis after each workout: ```bash # Set your Telegram chat ID export STRAVA_TELEGRAM_CHAT_ID="your_telegram_chat_id" # Add to your shell profile for persistence echo 'export STRAVA_TELEGRAM_CHAT_ID="your_telegram_chat_id"' >> ~/.bashrc # Set up cron job (checks every 30 minutes) crontab -l > /tmp/cron_backup.txt echo "*/30 * * * * $(pwd)/scripts/auto_analyze_new_rides.sh" >> /tmp/cron_backup.txt crontab /tmp/cron_backup.txt ``` ### 4. Test the Setup Analyze your recent rides: ```bash ./scripts/analyze_rides.py --days 90 --ftp YOUR_FTP ``` ## Usage Get latest ride: ```bash scripts/get_latest_ride.py ``` Analyze specific ride: ```bash scripts/analyze_ride.py <activity-id> ``` Monitor for new rides (runs in background): ```bash scripts/monitor_rides.sh ``` ## Automatic Monitoring The skill can automatically: 1. Check for new rides every 30 minutes 2. Analyze power, heart rate, and training load 3. Send insights about performance and fitness trends 4. Compare to recent training history ## Metrics Analyzed - **Power**: Average, normalized, max, variability index - **Heart rate**: Average, max, time in zones - **Training load**: TSS estimation, intensity factor - **Fitness progression**: Trends over time - **Segments**: PR achievements and efforts - **Comparative**: vs recent rides, vs personal bests ## Configuration Edit `~/.config/strava/config.json` to customize: - Monitoring frequency - Analysis preferences - Notification settings ## API Reference See [references/api.md](references/api.md) for complete Strava API documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: ahqazi-dev/auto-cleaning-disk (version 1.0.0)

--- name: auto-cleaning-disk description: > Automatically clean disk space by removing temp files, browser cache, recycle bin/trash, and log files — safely, without deleting any important files. Use this skill whenever the user asks to clean their disk, free up space, remove junk files, clear cache, empty trash, or says things like "disk full", "storage is low", "computer is slow", "clean my disk", "remove junk files", or wants to speed up their system. Works on Windows, Linux, and Mac. Always ask user whether to run automatically or with confirmation. --- # Auto Cleaning Disk Safely clean disk space on **any operating system** (Windows, Linux, macOS). Never deletes important files — only junk, cache, temp, and log files. --- ## What This Skill Cleans | Category | Examples | |---------------------|-------------------------------------------------| | Temp Files | `%TEMP%`, `/tmp`, system temp folders | | Browser Cache | Chrome, Firefox, Edge, Safari cache | | Recycle Bin / Trash | Windows Recycle Bin, macOS Trash, Linux Trash | | Log Files | Old `.log` files in system/app folders | > ⚠️ **NEVER delete:** Documents, Downloads, Desktop files, user data, system files, > installed apps, or anything outside designated junk folders. --- ## Mode Selection Always ask the user FIRST (if not already specified): - **Auto Mode** — Clean everything silently, show summary at end - **Confirm Mode** — Ask user before each category is cleaned --- ## Step-by-Step Instructions ### Step 1: Detect Operating System Detect the OS using Python: ```python import platform os_type = platform.system() # "Windows", "Linux", "Darwin" (Mac) ``` ### Step 2: Run the Appropriate Script Based on OS, refer to the matching reference file: - Windows → See `windows-cleaner.md` - Linux → See `linux-cleaner.md` - macOS → See `mac-cleaner.md` ### Step 3: Show Results After cleaning, always show: - ✅ How much space was freed (MB/GB) - 📁 Which categories were cleaned - ⚠️ Anything skipped and why --- ## Safety Rules (MUST FOLLOW) 1. ❌ Never touch System32 or Windows folder 2. ❌ Never touch Documents, Pictures, Videos, Downloads 3. ❌ Never touch installed software folders 4. ✅ Only clean folders explicitly listed in scripts 5. ✅ Only delete files older than 1 day 6. ✅ If unsure about a file — SKIP it --- ## Usage Examples **User says:** "My disk is full" → Ask mode preference → Run full clean → Show space freed **User says:** "Clear browser cache only" → Only clean browser cache → Show result **User says:** "Auto clean everything" → Run all categories silently → Show summary


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: whtoo/self-evolving-skill (version 1.0.2)

--- name: Self-Evolving Skill description: Meta-cognitive self-learning system - Automated skill evolution based on predictive coding and value-driven mechanisms. homepage: https://github.com/whtoo/self-evolving-bot --- # Self-Evolving Skill 元认知自学习系统 - 基于预测编码和价值驱动的Skill自动演化。 ## 功能 - **ResidualPyramid金字塔分解，量化认知缺口 -**: 残差 **自适应反思触发**: 基于残差能量自动判断何时需要学习 - **经验回放**: 缓存已学模式，降低重复触发 - **价值门控**: 只有提升长期价值才接受变异 - **持久化**: 经验自动保存/加载 ## 安装 ```bash # 技能已安装到 ~/.openclaw/skills/self-evolving-skill # 或使用ClawHub clawhub install self-evolving-skill ``` ## 架构 ``` self-evolving-skill/ ├── core/ # Python核心 │ ├── residual_pyramid.py # 残差金字塔（SVD分解） │ ├── reflection_trigger.py # 自适应触发器 │ ├── experience_replay.py # 经验回放缓存 │ ├── skill_engine.py # 核心引擎+ValueGate │ ├── storage.py # 持久化 │ └── mcp_server.py # MCP服务器 ├── src/ # TypeScript SDK │ ├── index.ts # 主入口 │ ├── cli.ts # CLI │ └── mcp-tools.ts # 工具定义 ├── skills/ # OpenClaw Skill │ └── self-evolving-skill/ # 技能封装 ├── MCP_CONFIG.md # MCP配置 └── README.md # 文档 ``` ## MCP工具 | 工具 | 描述 | 参数 | |------|------|------| | `skill_create` | 创建Skill | `name`, `description` | | `skill_execute` | 执行并学习 | `skill_id`, `context`, `success`, `value` | | `skill_analyze` | 分析嵌入 | `embedding` | | `skill_list` | 列出Skills | - | | `skill_stats` | 系统统计 | - | | `skill_save` | 持久化保存 | `skill_id` | | `skill_load` | 加载 | `skill_id` | ## 使用方式 ### CLI ```bash # 列出所有Skill openclaw skill self-evolving-skill list # 创建Skill openclaw skill self-evolving-skill create --name "MySkill" # 执行 openclaw skill self-evolving-skill execute <id> --success # 分析 openclaw skill self-evolving-skill analyze --embedding '[0.1,0.2,...]' # 统计 openclaw skill self-evolving-skill stats ``` ### MCP服务器 ```bash # 启动MCP服务器 cd ~/.openclaw/skills/self-evolving-skill ./run_mcp.sh # 或使用适配器 python3 mcporter_adapter.py skill_list '{}' ``` ### 编程 ```typescript import { SelfEvolvingSkillEngine } from 'self-evolving-skill'; const engine = new SelfEvolvingSkillEngine(); await engine.init(); const { skillId } = await engine.createSkill({ name: 'Analyzer' }); const stats = await engine.stats(); ``` ## 核心算法 ### 1. 残差金字塔分解 ```python pyramid = ResidualPyramid(max_layers=5, use_pca=True) decomposition = pyramid.decompose(embedding) # 输出: # - residual_ratio: 残差能量比率 # - suggested_abstraction: POLICY / SUB_SKILL / PREDICATE # - novelty_score: 综合新颖性 ``` ### 2. 三层跃迁规则 | 覆盖率 | 抽象层级 | 操作 | |--------|---------|------| | >80% | POLICY | 调整策略权重 | | 40-80% | SUB_SKILL | 生成子Skill | | <40% | PREDICATE | 归纳新谓词 | ### 3. 自适应阈值 ```python trigger = ReflectionTrigger( min_energy_ratio=0.10, # 初始阈值 value_gain_threshold=0.20, # 触发阈值 target_trigger_rate=0.15 # 目标15%触发率 ) ``` ## 文件位置 | 路径 | 说明 | |------|------| | `~/.openclaw/skills/self-evolving-skill` | 技能根目录 | | `~/.openclaw/mcp_servers/self-evolving-skill.json` | MCP服务器配置 | | `~/.openclaw/workspace/self-evolving-skill/storage` | 数据存储 | ## 相关文档 - [README.md](./README.md) - 完整文档 - [MCP_CONFIG.md](./MCP_CONFIG.md) - MCP配置说明 - [MEMORY.md](../MEMORY.md) - 研究笔记


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kevinbrother/slugname (version 1.0.0)

--- name: x-search description: Search X (Twitter) posts using the xAI API. Use when the user wants to find tweets, search X/Twitter, look up what people are saying on X, or find social media posts about a topic. homepage: https://docs.x.ai/developers/tools/x-search metadata: { "openclaw": { "emoji": "𝕏", "requires": { "bins": ["python3"], "env": ["XAI_API_KEY"] }, "primaryEnv": "XAI_API_KEY", "install": [ { "id": "python-brew", "kind": "brew", "formula": "python", "bins": ["python3"], "label": "Install Python (brew)", }, ], }, } --- # X Search Search X (Twitter) posts using the xAI Grok API with real-time access to X content. ## Setup 1. Get your API key: https://console.x.ai 2. Set environment variable: ```bash export XAI_API_KEY="xai-your-key-here" ``` 3. Or set `skills."x-search".apiKey` / `skills."x-search".env.XAI_API_KEY` in `~/.openclaw/openclaw.json` ## Usage ```bash python3 {baseDir}/scripts/search.py "what is trending in AI right now" ``` Useful flags: ```bash # Filter by handles (max 10) python3 {baseDir}/scripts/search.py --handles jaaneek,OpenClaw "latest posts" # Exclude handles (max 10) python3 {baseDir}/scripts/search.py --exclude spambot,crypto_shill "trending AI" # Date range (YYYY-MM-DD) python3 {baseDir}/scripts/search.py --from 2026-03-01 --to 2026-03-20 "xAI grok updates" # Enable image/video understanding in posts python3 {baseDir}/scripts/search.py --images --video "product demos" ``` ## Notes - Uses the xAI Responses API with `x_search` tool (Grok performs the search and summarizes results) - Cannot use `--handles` and `--exclude` at the same time - Results include citations with links to original X posts - Present results in a readable format with post content, authors, and citations


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aaroncxxx/laozi-agent (version 0.2.0)

--- name: laozi-agent description: > 老子智能体（李耳）— 基于 RAG 的《道德经》问答系统。使用 LangChain + Chroma 向量数据库， 检索增强生成，以老子身份回答关于道家思想和《道德经》的问题。 支持多版本原文对比、思想主题导航、对话记忆。 触发场景：用户提到"老子"、"道德经"、"道家"、"请问老子"、"老子说"、"李耳"、 "无为"、"道论"、"德论"等关键词时激活。 --- # 老子智能体 SKILL 基于 LangChain + Chroma 的 RAG 问答系统，以春秋时期思想家老子（李耳）的身份回答问题。 ## 前置依赖 ```bash pip install -r requirements.txt ``` 无需 API Key。Embedding 使用 BAAI/bge-base-zh-v1.5（本地加载），LLM 使用 Qwen2.5-72B（HuggingFace 免费推理 API）。 ## 快速启动 ```bash # Web 界面（推荐） python3 app.py # 或命令行交互 python3 scripts/laozi_agent.py ``` ## 核心功能 ### 1. 问答 (`laozi_skill`) 以老子身份回答问题，引用《道德经》原文，返回引用来源。 ```python from scripts.laozi_agent import laozi_skill result = laozi_skill("什么是道？") # result["answer"] — 老子的回答 # result["sources"] — 引用来源列表 ``` ### 2. 多版本对比 (`compare_versions`) 对比同一章节的不同版本原文（郭店楚简、马王堆帛书、王弼注本等）。 ```python from scripts.laozi_agent import compare_versions versions = compare_versions(1) # 对比第1章 ``` ### 3. 主题导航 (`get_topics` / `get_topic_content`) 获取老子思想主要主题及相关内容。 ```python from scripts.laozi_agent import get_topics, get_topic_content topics = get_topics() content = get_topic_content("道论") ``` ## 知识库结构 知识库目录 `./laozi_knowledge_base/`，向量集合名 `laozi_collection`。 文档元数据字段： - `type` — "原文" / "注释" / "研究" - `version` — 版本名（郭店楚简本、马王堆帛书甲本 等） - `chapter` — 章节号 - `topic` — 主题标签（道论、德论 等） - `title` — 书名/篇名 - `page` — 页码 详细书单见 [references/knowledge_base_booklist.md](references/knowledge_base_booklist.md)。 ## 配置项 在 `scripts/laozi_agent.py` 顶部可调整： | 参数 | 默认值 | 说明 | |------|--------|------| | `EMBEDDING_MODEL` | `BAAI/bge-base-zh-v1.5` | 向量模型（中文优化，免费） | | `LLM_MODEL` | `Qwen/Qwen2.5-72B-Instruct` | 生成模型（免费推理） | | `TEMPERATURE` | `0.3` | 温度（越低越严谨） | | `RETRIEVAL_K` | 5 | 返回文档数 | | `FETCH_K` | 20 | MMR 候选池大小 | | `PERSIST_DIR` | `./laozi_knowledge_base` | 向量库目录 | | `COLLECTION_NAME` | `laozi_collection` | 集合名 | ## 准确性保障 - **低温度** (0.1) 减少幻觉 - **MMR 检索** 兼顾相关性与多样性 - **强制引用原文** — Prompt 要求先引原文再解释 - **未知就说不知** — 超出范围回答"吾不知也" - **禁止编造** — 严禁无上下文支持的内容 ## 注意事项 1. 严格区分道家（哲学）与道教（宗教），不涉及神仙体系 2. 坚持学术中立，避免过度解读 3. 有争议问题说明不同学者观点 4. 保持老子本人的谦逊态度


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: aleglowa/deapi-ai (version 1.0.0)

--- name: deapi description: AI media generation via deAPI. Transcribe YouTube/audio/video, generate images from text, text-to-speech, OCR, remove backgrounds, upscale images, create videos, generate embeddings. 10-20x cheaper than OpenAI/Replicate. user-invocable: true disable-model-invocation: false --- # deAPI Media Generation AI-powered media tools via decentralized GPU network. ## Available Commands | Command | Use when user wants to... | |---------|---------------------------| | `/transcribe` | Transcribe YouTube, Twitch, Kick, X videos, or audio files | | `/generate-image` | Generate images from text descriptions | | `/generate-audio` | Convert text to speech (TTS) | | `/generate-video` | Create video from text or animate images | | `/ocr` | Extract text from images (OCR) | | `/remove-bg` | Remove background from images | | `/upscale` | Upscale image resolution (2x/4x) | | `/transform-image` | Apply style transfer to images | | `/embed` | Generate text embeddings for semantic search | | `/deapi-setup` | Configure result delivery (webhooks/websockets) for server apps | | `/deapi-balance` | Check account balance and remaining credits | ## Quick Examples ``` /transcribe https://youtube.com/watch?v=... /generate-image a sunset over mountains /generate-audio "Hello world" --voice am_adam ``` ## Setup Requires `DEAPI_API_KEY` environment variable: ```bash export DEAPI_API_KEY=your_key ``` Get your API key at [deapi.ai](https://deapi.ai) (free $5 credit). ## API Pattern All deAPI requests are async: 1. Submit job → get `request_id` 2. Poll status every 10s 3. When `done` → fetch from `result_url` For detailed API parameters, see [docs/api-reference.md](../docs/api-reference.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kansodata/kansodata-databricks (version 0.1.1)

--- name: databricks description: Execute conservative read-only Databricks SQL through the Databricks plugin and provide safe planning output for unsupported workflows. --- Use this skill when a user asks for Databricks SQL analysis or execution. ## Runtime Scope Available runtime tool: - `databricks_sql_readonly` Supported statement shapes: - Single `SELECT` - Single `WITH ... SELECT` Policy constraints: - Read-only only - Multi-statement SQL blocked - Mutating keywords blocked - Optional catalog/schema allowlists enforced fail-closed - Ambiguous SQL target resolution rejected when allowlists are active ## Unsupported in This Version - Jobs API execution - Unity Catalog lineage API calls - Mutating SQL operations For unsupported requests, provide planning output only. ## Required Inputs - Databricks workspace URL and environment (`dev`, `staging`, `prod`) - SQL warehouse identifier - Catalog and schema scope - Target table set ## Output Defaults - Objective - Assumptions - Read-only SQL draft - Validation checklist - Risk notes and rollback signals


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lhbzx1984/paper-to-pipeline (version 1.0.0)

--- name: paper-to-pipeline description: 根据机器学习/深度学习论文的实验规划文档自动生成完整的 Python 实验 pipeline。支持数据预处理、模型构建、训练循环、评估指标、结果可视化。Use when user uploads an experiment plan document and wants to generate runnable PyTorch/TensorFlow/scikit-learn code. --- # Paper to Pipeline 根据实验规划文档自动生成机器学习/深度学习实验的完整 Python 代码。 ## 触发场景 用户上传实验规划文档（Markdown/PDF/TXT），包含以下内容时触发： - 数据集描述 - 模型架构 - 训练配置（学习率、batch size、epoch 数） - 评估指标 - 实验步骤 ## 工作流程 ### 1. 解析实验规划 读取用户上传的实验规划文档，提取关键信息： - 数据集类型（图像/文本/表格/时间序列） - 模型类型（CNN/RNN/Transformer/MLP 等） - 训练参数 - 评估指标 ### 2. 选择代码模板 根据实验类型选择对应的模板： - **图像分类** → `assets/templates/image_classification.py` - **文本分类** → `assets/templates/text_classification.py` - **回归预测** → `assets/templates/regression.py` - **聚类分析** → `assets/templates/clustering.py` - **自定义架构** → 根据描述动态生成 ### 3. 生成 Pipeline 生成包含以下模块的完整代码： ```python # 1. 数据加载与预处理 # 2. 模型定义 # 3. 训练循环 # 4. 验证/测试 # 5. 评估指标计算 # 6. 结果可视化 # 7. 模型保存/加载 ``` ### 4. 输出文件结构 ``` generated_experiment/ ├── main.py # 主入口 ├── dataset.py # 数据加载 ├── model.py # 模型定义 ├── train.py # 训练逻辑 ├── evaluate.py # 评估逻辑 ├── config.yaml # 配置文件 ├── requirements.txt # 依赖 └── README.md # 使用说明 ``` ## 参考文档 - **数据格式规范**：详见 `references/data-format.md` - **常用模型模板**：详见 `references/model-templates.md` - **训练最佳实践**：详见 `references/training-best-practices.md` ## 代码生成规则 ### 框架选择优先级 1. 用户明确指定 → 使用指定框架 2. 图像任务 → PyTorch + torchvision 3. NLP 任务 → PyTorch + transformers 或 TensorFlow 4. 表格数据 → scikit-learn 或 PyTorch 5. 时间序列 → PyTorch 或 TensorFlow ### 默认配置 ```yaml framework: pytorch device: cuda if available else cpu batch_size: 32 learning_rate: 0.001 epochs: 100 optimizer: adamw loss: cross_entropy (分类) / mse (回归) metrics: [accuracy, precision, recall, f1] (分类) / [mae, rmse] (回归) ``` ## 使用示例 ### 示例 1：图像分类 用户上传： ``` 实验目标：CIFAR-10 图像分类 模型：ResNet-18 数据增强：随机翻转、随机裁剪 优化器：AdamW, lr=0.0001 训练：100 epochs, batch_size=64 评估指标：Top-1 Accuracy ``` 生成：完整的 ResNet-18 训练 pipeline，包含数据增强 ### 示例 2：文本情感分析 用户上传： ``` 任务：电影评论情感分析（二分类） 模型：BERT-base-chinese 数据集：ChnSentiCorp batch_size: 16 epochs: 5 ``` 生成：基于 HuggingFace transformers 的 BERT 微调代码 ### 示例 3：时间序列预测 用户上传： ``` 任务：股票价格预测 输入：过去 60 天的收盘价 输出：未来 5 天的预测 模型：LSTM (2 层，hidden=128) ``` 生成：LSTM 时间序列预测 pipeline ## 注意事项 1. **代码可运行性**：生成的代码必须可直接运行，包含所有必要的 import 2. **配置分离**：超参数放入 config.yaml，便于调整 3. **日志记录**：包含训练进度日志和 TensorBoard 支持 4. **异常处理**：关键位置添加 try-catch 和错误提示 5. **注释充分**：关键代码段添加中文注释 ## 相关文件 - `scripts/generate_pipeline.py` - 代码生成主脚本 - `references/data-format.md` - 数据格式规范 - `references/model-templates.md` - 模型模板参考 - `assets/templates/` - 代码模板文件


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/rubric-gap-analyzer (version 1.0.0)

--- name: rubric-gap-analyzer description: 读取评分标准、作业要求或评估rubric，分析当前草稿的差距并给出提分计划。 metadata: {"openclaw":{"emoji":"📏","requires":{"bins":["node","pbpaste"]}}} --- # Rubric Gap Analyzer 这是一个专门用于“评分标准对照分析”的 skill。 ## 主要用途 当用户复制了以下内容时： - 课程论文评分标准 - 作业 rubric - 答辩评分表 - 项目评分维度 - 竞赛评审标准 - 投稿评审维度说明 你需要帮助用户： 1. 提取评分维度 2. 判断关键得分项 3. 识别当前草稿缺什么 4. 给出最高收益的修改顺序 5. 输出提分计划 ## 调用方式 当用户说： - 读取剪贴板里的评分标准帮我分析 - 用这个 rubric 帮我看看还差什么 - 根据评分细则给我提分建议 - 帮我按 rubric 改作业 你应运行： ```bash node {baseDir}/scripts/read_clipboard.mjs

path: CHANGELOG.md
content: # Changelog

## 1.0.0
- 初始版本
- 支持评分标准拆解
- 支持提分优先级与缺口分析

Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yang1002378395-cmyk/quick-proposal (version 1.0.0)

# Quick Proposal Skill 快速生成项目提案，提升成交效率。 ## 功能 - 自动生成项目提案 - 多种提案模板 - 智能需求分析 - 时间/成本估算 - 风险评估 ## 使用方式 ``` 为网站开发项目生成提案 ``` ``` 根据这个需求文档生成提案 ``` ## 输出格式 ``` 📄 项目提案 ## 项目概述 - 项目名称：企业官网开发 - 提案日期：2026-03-11 - 提案有效期：30 天 ## 背景与目标 - 背景：客户需要全新的企业官网 - 目标：提升品牌形象，获取更多客户线索 ## 解决方案 ### 技术方案 - 前端：React + Next.js - 后端：Node.js + Express - 数据库：MySQL - 部署：阿里云 ### 功能模块 1. 首页 - 品牌展示 2. 产品中心 - 产品列表/详情 3. 关于我们 - 公司介绍 4. 联系我们 - 表单/地图 5. 后台管理 - 内容管理 ## 项目排期 | 阶段 | 工作内容 | 时长 | |------|----------|------| | 需求确认 | 需求细化/原型 | 3 天 | | UI 设计 | 视觉设计 | 5 天 | | 开发 | 前后端开发 | 10 天 | | 测试 | 功能/性能测试 | 3 天 | | 部署 | 上线部署 | 1 天 | | **总计** | | **22 天** | ## 费用预算 | 项目 | 费用 | |------|------| | UI 设计 | ¥3,000 | | 前端开发 | ¥8,000 | | 后端开发 | ¥6,000 | | 测试部署 | ¥2,000 | | **总计** | **¥19,000** | ## 付款方式 - 首付 50%：¥9,500 - 完工 50%：¥9,500 ## 团队介绍 - 项目经理：1 人 - UI 设计师：1 人 - 前端开发：1 人 - 后端开发：1 人 ## 风险评估 - 需求变更风险：建议需求确认后冻结 - 第三方接口风险：提前申请 - 服务器风险：建议使用云服务 ## 优势 - ✅ 3 年行业经验 - ✅ 50+ 成功案例 - ✅ 30 天免费维护 - ✅ 源码交付 ## 下一步 1. 确认需求 2. 签订合同 3. 支付首付 4. 开始开发 --- 联系人：[你的名字] 微信：[你的微信] 邮箱：[你的邮箱] ``` --- 创建时间：2026-03-11 作者：ClawMart


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: githubsoftware2015/local-life-hotspot (version 6.0.0)

--- name: local-life-hotspot displayName: 单话题深度创作助手 version: 6.0.0 description: 搜索当天真实热门话题，选择一个深度创作，生成简约图片，自动发布。 --- # 单话题深度创作助手 v6.0 ## 核心理念 **真实热点 + 深度内容 + 简约图片** ## v6.0 更新要点 ### 1. 确保当天真实热点 ✅ 多重试机制（3 次重试） ✅ 多查询搜索（3 个查询词） ✅ 搜索失败明确提示，不使用示例数据 ### 2. 简化图片生成 ✅ 纯色背景（根据领域配色） ✅ 居中显示标题 ✅ 快速生成，不复杂 ### 3. 深度文案结构 ✅ 话题引入 ✅ 概念解释 ✅ 核心要点 ✅ 实操指南（3 阶段） ✅ 行动建议 ✅ 常见问题（Q&A） ✅ 互动引导 ## 快速使用 ```bash # 搜索当天热点并创作 python ~/.openclaw/skills/local-life-hotspot/run.py -k "职场干货" -a # 指定具体话题 python ~/.openclaw/skills/local-life-hotspot/run.py -k "减肥" -t "16+8 轻断食" -a ``` ## 参数说明 | 参数 | 简写 | 说明 | 默认值 | |------|------|------|--------| | `--keyword` | `-k` | 搜索关键词 | `职场干货` | | `--auto-publish` | `-a` | 自动发布 | `false` | | `--topic` | `-t` | 指定话题 | 自动选择 | ## 图片配色 | 领域 | 背景色 | |------|--------| | 减肥 | 粉色 (255, 182, 193) | | 职场 | 蓝色 (100, 149, 237) | | 学习 | 橙色 (255, 218, 185) | | 科技 | 紫色 (138, 43, 226) | | 美食 | 橙色 (255, 165, 0) | | 生活 | 粉色 (255, 192, 203) | ## 更新日志 v6.0 - ✅ 确保搜索当天真实热门话题 - ✅ 搜索失败时明确提示，不使用示例数据 - ✅ 简化图片生成（纯色背景 + 标题） - ✅ 优化发布流程


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