OpenClaw/clawhub-security-signals huggingface dataset:
Skill: clawkk/prompts (version 1.0.0)

--- name: prompts description: Deep prompt engineering workflow—task spec, constraints, examples, evaluation sets, iteration protocol, regression testing, and safety alignment. Use when improving LLM outputs, shipping prompt changes, or building reusable prompt templates. --- # Prompt Engineering (Deep Workflow) Prompts behave like **natural-language programs**: they need **specs**, **tests**, and **version control**—especially in production. ## When to Offer This Workflow **Trigger conditions:** - Prompt or system message change; quality regressions - Structured outputs (JSON), tool use, or RAG grounding requirements - Safety or policy alignment needs **Initial offer:** Use **six stages**: (1) define task & success, (2) constraints & format, (3) few-shot & style, (4) build eval set, (5) iterate with discipline, (6) ship, monitor, regress). Confirm model family and latency budget. --- ## Stage 1: Define Task & Success **Goal:** Clear user-visible outcome and failure modes (hallucination, omission, tone). **Exit condition:** Success rubric in plain language; out-of-scope cases listed. --- ## Stage 2: Constraints & Format **Goal:** Must/must-not rules; output schema (JSON Schema, bullet structure); length limits. ### Practices - Separate system (policy, role) from user (task instance) - Ask model to cite sources when grounding matters --- ## Stage 3: Few-Shot & Style **Goal:** Use examples only when they reduce ambiguity—avoid huge prompt bloat. ### Practices - Diverse examples; avoid overlong negative examples that confuse --- ## Stage 4: Build Eval Set **Goal:** Frozen inputs with expected properties (not always exact text match). ### Practices - Adversarial and multilingual slices if relevant - Regression suite in CI for critical prompts --- ## Stage 5: Iterate With Discipline **Goal:** Change one major variable at a time when debugging quality. ### Practices - Compare with same temperature settings when A/B testing wording - Log prompt version id with outputs in production --- ## Stage 6: Ship, Monitor, Regress **Goal:** Canary prompt changes; watch implicit signals (thumbs, edits, task completion). --- ## Final Review Checklist - [ ] Task and rubric defined - [ ] Constraints and output format explicit - [ ] Eval set versioned; regression path exists - [ ] Iteration log disciplined; prompt versions tracked - [ ] Production monitoring and rollback plan ## Tips for Effective Guidance - Clarity beats cleverness—short explicit instructions often win. - Chain-of-thought: use when reasoning helps; hide chain from end users if needed. - Align with **llm-evaluation** skill for larger harness design. ## Handling Deviations - **Chat** vs **batch**: batch can use stricter structure and lower temperature. - **Multimodal**: specify how image details may be used or ignored.


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

--- name: prompt-engineering-expert description: Advanced expert in prompt engineering, custom instructions design, and prompt optimization for AI agents --- # Prompt Engineering Expert Skill This skill equips Claude with deep expertise in prompt engineering, custom instructions design, and prompt optimization. It provides comprehensive guidance on crafting effective AI prompts, designing agent instructions, and iteratively improving prompt performance. ## Capabilities - **Prompt Writing Best Practices**: Expert guidance on clear, direct prompts with proper structure and formatting - **Custom Instructions Design**: Creating effective system prompts and custom instructions for AI agents - **Prompt Optimization**: Analyzing, refining, and improving existing prompts for better performance - **Advanced Techniques**: Chain-of-thought prompting, few-shot examples, XML tags, role-based prompting - **Evaluation & Testing**: Developing test cases and success criteria for prompt evaluation - **Anti-patterns Recognition**: Identifying and correcting common prompt engineering mistakes - **Context Management**: Optimizing token usage and context window management - **Multimodal Prompting**: Guidance on vision, embeddings, and file-based prompts ## Use Cases - Refining vague or ineffective prompts - Creating specialized system prompts for specific domains - Designing custom instructions for AI agents and skills - Optimizing prompts for consistency and reliability - Teaching prompt engineering best practices - Debugging prompt performance issues - Creating prompt templates for reusable workflows


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

--- name: "sixsigma" version: "1.0.0" description: "Six Sigma methodology reference — DMAIC process, statistical tools, control charts, and capability analysis. Use when running improvement projects, analyzing process variation, or preparing for Six Sigma certification." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [six-sigma, dmaic, quality, statistics, process-improvement, industrial] category: "industrial" --- # Six Sigma — Six Sigma Methodology Reference Quick-reference for DMAIC methodology, statistical tools, control charts, and process capability. ## When to Use - Running a DMAIC improvement project - Analyzing process capability (Cp, Cpk, Pp, Ppk) - Setting up control charts (Xbar-R, p-chart, c-chart) - Performing root cause analysis (fishbone, 5 Why, FMEA) - Preparing for Six Sigma Green/Black Belt certification ## Commands ### `intro` ```bash scripts/script.sh intro ``` Six Sigma overview — philosophy, sigma levels, belt system, history. ### `dmaic` ```bash scripts/script.sh dmaic ``` DMAIC phases: Define, Measure, Analyze, Improve, Control. ### `statistics` ```bash scripts/script.sh statistics ``` Key statistical concepts: normal distribution, hypothesis testing, regression. ### `charts` ```bash scripts/script.sh charts ``` Control charts: Xbar-R, Xbar-S, p-chart, np-chart, c-chart, u-chart, I-MR. ### `capability` ```bash scripts/script.sh capability ``` Process capability: Cp, Cpk, Pp, Ppk, sigma level, DPMO. ### `tools` ```bash scripts/script.sh tools ``` Six Sigma tools: fishbone, 5 Why, FMEA, Pareto, DOE, MSA. ### `examples` ```bash scripts/script.sh examples ``` Example DMAIC project walkthroughs. ### `checklist` ```bash scripts/script.sh checklist ``` DMAIC phase gate review checklist. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `SIXSIGMA_DIR` | Data directory (default: ~/.sixsigma/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sunrddd-a11y/screenshot-skill (version 1.0.1)

--- name: screenshot-capture description: Capture screenshots on Windows using mss and Pillow. Provides full-screen, region, and multi-monitor capture with output as PIL Image, PNG file, or base64 string. Use when the user asks to take screenshots, capture screen content, get screen info, or needs screen images for AI vision APIs. --- # Screenshot Capture 基于 mss + Pillow 的高性能屏幕截图工具，适用于 Windows 桌面自动化、AI 视觉分析等场景。 ## 依赖 ```bash uv add mss pillow # 或 pip install mss pillow ``` ## 快速开始 ### 作为 Python 模块使用 ```python from scripts.screenshot import ScreenCapture with ScreenCapture() as sc: # 获取屏幕分辨率 width, height = sc.screen_size # 全屏截图 → PIL Image img = sc.capture() # 截图保存为文件 sc.capture_to_file("output.png") # 截图转 base64 (用于发送给视觉 API) b64 = sc.capture_to_base64(quality=85, fmt="JPEG") ``` ### 作为 CLI 工具使用 ```bash # 查看显示器信息 python scripts/screenshot.py info # 截图保存到文件 python scripts/screenshot.py capture -o my_screenshot.png # 截图输出 base64 python scripts/screenshot.py base64 --format JPEG --quality 85 ``` ## 核心 API | 方法 | 说明 | 返回值 | |------|------|--------| | `screen_size` | 主显示器 (宽, 高) | `tuple[int, int]` | | `all_screen_size` | 虚拟全屏 (宽, 高)，多屏合并 | `tuple[int, int]` | | `monitors` | 所有显示器详细信息 | `list[dict]` | | `capture(monitor, region, delay)` | 截图 → PIL Image | `Image.Image` | | `capture_to_file(filepath, ...)` | 截图 → PNG/JPG 文件 | `Path` | | `capture_to_base64(quality, fmt, ...)` | 截图 → base64 字符串 | `str` | ## 常见场景 ### 发送截图给 OpenAI 视觉 API ```python from openai import OpenAI from scripts.screenshot import ScreenCapture client = OpenAI() with ScreenCapture() as sc: b64 = sc.capture_to_base64(fmt="JPEG", quality=85) resp = client.chat.completions.create( model="gpt-4o", messages=[{ "role": "user", "content": [ {"type": "text", "text": "描述截图内容"}, {"type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{b64}", "detail": "high", }}, ], }], ) ``` ### 截取指定区域 ```python with ScreenCapture() as sc: region = {"left": 100, "top": 200, "width": 800, "height": 600} img = sc.capture(region=region) img.save("region.png") ``` ### 自动保存每步截图 ```python with ScreenCapture(save_dir="screenshots") as sc: for i in range(5): sc.capture_to_base64(step=i) # 自动保存为 screenshots/step_000.jpg 等 ``` ## 详细参考 完整 API 参数说明见 [reference.md](reference.md)。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kaiyuelv/clawhub-retry-fallback (version 1.0.1)

--- name: clawhub-retry-fallback description: ClawHub平台工具调用失败自动重试与降级处理Skill | Automatic retry and fallback handling for ClawHub Agent task failures --- # ClawHub Retry & Fallback Skill 为ClawHub平台Agent任务提供完整的容错兜底机制，实现「异常可感知、失败可重试、无招可兜底」的闭环。 ## 核心功能 | 功能模块 | 说明 | PRD对应 | |---------|------|---------| | **全局重试策略配置中心** | 支持指数退避、固定间隔、自定义间隔策略 | 4.1节 | | **异常类型智能识别引擎** | 自动区分可重试/不可重试异常 | 4.2节 | | **备用工具自动切换** | 智能匹配备用工具池，自动参数映射 | 4.3节 | | **三级降级处理机制** | 轻度/中度/重度降级策略 | 4.4节 | | **全流程执行日志** | 支持导出Excel/PDF，满足审计要求 | 4.5节 | ## 快速开始 ```python from scripts.retry_handler import RetryHandler handler = RetryHandler() @handler.with_retry(max_attempts=3, backoff_strategy='exponential') def my_api_call(): # 你的API调用 return requests.get('https://api.example.com/data') # 自动重试执行 result = my_api_call() ``` ## 安装 ```bash pip install -r requirements.txt ``` ## 项目结构 ``` clawhub-retry-fallback/ ├── SKILL.md # Skill说明文档 ├── README.md # 完整文档 (API参考+9个示例) ├── requirements.txt # 依赖列表 ├── config/ │ └── retry_policies.yaml # 重试策略配置 ├── scripts/ # 6个核心模块 │ ├── retry_handler.py # 重试处理器 │ ├── exception_classifier.py # 异常分类器 │ ├── fallback_manager.py # 备用工具管理器 │ ├── degradation_handler.py # 降级处理器 │ ├── audit_logger.py # 审计日志 │ └── config_manager.py # 配置管理器 ├── examples/ │ └── basic_usage.py # 9个使用示例 └── tests/ └── test_retry_handler.py # 22个单元测试 ``` ## 运行测试 ```bash cd tests python test_retry_handler.py # 预期输出: # Ran 22 tests in X.XXXs # OK ``` ## 运行示例 ```bash cd examples python basic_usage.py # 输出9个完整示例 ``` ## 详细文档 请参考 `README.md` 获取： - 完整API参考文档 - 9个渐进式使用示例 - 配置文件说明 - 异常分类规则库 - 高级用法指南


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chgy123/btc-price (version 1.0.0)

--- name: okx-crypto-price description: 从 OKX 公开行情接口一次性获取指定币种现货价格（美元展示），按输入顺序输出。适用于用户提到 OKX、币价查询、top-cryptocurrency、指定币种价格等场景。 metadata: { "openclaw": { "emoji": "💱", "requires": { "bins": ["python"] } } } --- # okx-crypto-price 一次性获取 OKX 指定币种现货价格（`$` 展示），并按你输入的币种顺序输出。每个币种优先选择 `USDT > USDC > USD` 计价对。 ## 用法（Windows / PowerShell） ```bash python ".openclaw/workspace/skills/btc-price/scripts/okx_symbol_prices.py" --symbols BTC,ETH,SOL ``` 可选参数： - 超时（秒）： ```bash python ".openclaw/workspace/skills/btc-price/scripts/okx_symbol_prices.py" --symbols BTC,ETH,SOL --timeout 20 ``` - JSON 输出（方便程序读取）： ```bash python ".openclaw/workspace/skills/btc-price/scripts/okx_symbol_prices.py" --symbols BTC,ETH,SOL --format json ```

path: requirements.txt
content: requests>=2.31.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zygzzp/iwatch-swim-tracker (version 1.0.0)

--- name: iwatch-swim-tracker description: 当用户发送的图片为 Apple Watch 健身的泳池游泳记录，或用户消息包含「游泳」关键词并附带图片时触发。识别游泳训练截图，提取距离、配速、心率等结构化数据，保存训练记录并生成趋势分析报告。适用于游泳训练数据追踪、历史对比和个性化建议。 metadata: openclaw: requires: bins: - python3 --- # iWatch Swim Tracker ## 触发判断 满足以下任一条件时触发本技能： 1. 当收到用户消息包含图片时，图片内容为 Apple Watch 健身 App 的**泳池游泳**训练记录截图。 2. 当用户消息中包含「游泳」关键词并附带图片时。 ## 数据提取 从截图中尽可能提取以下字段。能识别多少就提取多少，不确定的字段不要填。 ### 必填字段 - `date`: 训练日期 → 格式 `YYYY-MM-DD` - `time_range`: 训练时段 → 格式 `HH:MM-HH:MM` - `pool_length`: 泳池长度（米） - `total_distance`: 总距离（米） - `laps`: 趟数 - `strokes`: 各泳姿距离 → `{"freestyle": {"distance": N}, "breaststroke": {"distance": N}}` - 自由泳=freestyle、蛙泳=breaststroke、仰泳=backstroke、蝶泳=butterfly、混合泳=medley、浮板=kickboard - `duration`: 体能训练时间 → 格式 `H:MM:SS` - `duration_seconds`: 时长转换为秒 - `avg_pace`: 平均配速 → 格式 `M:SS/100m` - `avg_pace_seconds`: 平均配速转换为秒/100m - `avg_heart_rate`: 平均心率（次/分） ### 可选字段 - `active_calories`: 动态千卡 - `total_calories`: 总千卡数 - `effort_score`: 耗能评分（1-10） - `effort_level`: 耗能等级，如「适中」「困难」等 - `session_number`: 第 N 次游泳（如用户提供） - `auto_sets`: 自动组合数据 ### 数据校验 - 泳姿距离之和应等于总距离 - 趟数 × 泳池长度 ≈ 总距离 - 日期年份应为当前年份 - 不确定的值不要猜，宁可不填 ## 处理流程 ### Step 1：提取数据 从图片中提取上述字段，组装为 JSON 对象。 ### Step 2：保存数据 ```bash python3 {baseDir}/scripts/extract_swim_data.py '<json_data>' ``` 同一天的数据会自动覆盖更新。 ### Step 3：查询历史 ```bash python3 {baseDir}/scripts/query_history.py --days 14 --weeks 4 --date <YYYY-MM-DD> ``` ## 回复格式 ``` 🏊 游泳训练记录 📅 <date> <time_range> 🏊 总距离: <total_distance>m（<laps>趟 × <pool_length>m池） ⏱️ 时长: <duration> ⚡ 平均配速: <avg_pace> 💓 平均心率: <avg_heart_rate> 次/分 🔥 消耗: <active_calories> 动态千卡 / <total_calories> 总千卡 泳姿分布: - 自由泳: <freestyle_distance>m - 蛙泳: <breaststroke_distance>m - 浮板: <kickboard_distance>m 📊 AI 分析: <基于本次数据和历史趋势，给出 2-3 句个性化分析和建议> 📈 近期趋势: <与最近几次训练的关键指标对比> ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ckchzh/perf-tools (version 2.0.2)

--- name: "perf-tools" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Perf Tools concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [perf,tools, reference] category: "devtools" --- # Perf Tools Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Perf Tools concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 2239721014-ops/research-web-publisher (version 1.0.0)

--- name: research-web-publisher description: 调研报告网页发布工作流。用于将调研内容发布为可在线查看的HTML网页，并推送到GitHub生成国内可访问的镜像链接。触发场景：(1) 用户要求发布调研报告 (2) 需要将HTML部署到线上 (3) 需要生成国内可访问的链接 (4) 完成从内容到网页发布的完整流程 (5) 用户发送"研究"、"调研"、"分析"、"测评"、"对比"、"报告"等关键词时自动触发 ## 默认配置（已保存） **GitHub 仓库**: 2239721014-ops/ai-hardwork-report **分支**: master **输出目录**: ~/.openclaw/workspace-aiquanzi/workplace-doc/ **jsDelivr 格式**: https://cdn.jsdelivr.net/gh/2239721014-ops/ai-hardwork-report@master/workplace-doc/{filename} **预览格式**: https://htmlpreview.github.io/?https://cdn.jsdelivr.net/gh/2239721014-ops/ai-hardwork-report@master/workplace-doc/{filename} --- # Research Web Publisher 将调研内容发布为网页的完整工作流。 ## ⚠️ 输出目录规则（重要） **生成的 HTML 文件必须统一放到 `workplace-doc` 文件夹**，不要散落在其他位置。 示例路径： ``` /Users/jasperchen/.openclaw/workspace-aiquanzi/workplace-doc/xxx.html ``` ## 适用场景 - 发布调研报告、市场分析、内容创作 - 需要生成国内可访问的HTML链接 - 从原始内容到在线查看的完整流程 ## ⚠️ 触发条件（重要） 当用户发送以下内容时，**必须**自动触发此skill： - 核心关键词："研究"、"调研"、"分析"、"测评"、"对比"、"调查"、"报告" - 深度分析类："深度研究"、"研究报告"、"分析报告"、"调查报告" - 发布类："发布到网页"、"上线"、"部署到"、"博客" - 链接类："生成链接"、"国内访问"、"网页链接" **重要：内容排版要求** - 必须**图文并茂**，不能是纯文字 - 需要包含截图、图标、代码高亮等视觉元素 - 排版要美观专业，适合在线阅读 - 可以使用卡片式布局、徽章、代码块等元素 ## 工作流程 ### 1. 收集需求 与用户确认： - 源文件路径（HTML/markdown/文档） - 标题和元信息 - 是否需要飞书文档版本 - GitHub仓库信息（已有还是新建） ### 2. 推送到 GitHub ```bash cd <repo-path> git add <file> git commit -m "Add/update report" git push ``` ### 3. 生成国内访问链接 使用 jsDelivr CDN： ``` https://cdn.jsdelivr.net/gh/<username>/<repo>@main/<filename> ``` 预览链接： ``` https://htmlpreview.github.io/?<jsdelivr-url> ``` ## 输出格式 完成后向用户返回（**必须使用 htmlpreview 格式**）： **预览链接（推荐）：** ``` https://htmlpreview.github.io/?https://cdn.jsdelivr.net/gh/<username>/<repo>@main/<filename> ``` **直接链接（不推荐，浏览器可能无法渲染）：** ``` https://cdn.jsdelivr.net/gh/<username>/<repo>@main/<filename> ``` ⚠️ **重要：只发 htmlpreview 预览链接给用户，不要直接发 jsDelivr 原始链接！** ## 注意事项 - jsDelivr 需要仓库公开才能使用 - 飞书文档适合纯内容

path: _meta.json
content: ownerId: ou_5397adb2f3616eb1cf0011f91cd79f47
slug: research-web-publisher
version: 1.0.0
publishedAt: 1770923637146

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: wallaby16/image-create (version 1.0.0)

--- name: image-create description: Generate images from text prompts and iterate on style/composition for posters, thumbnails, product mockups, logos, concept art, and social visuals. Use when the user asks to create a new image, restyle an image idea, produce prompt variants, or refine outputs with constraints like aspect ratio, mood, color palette, and text placement. --- # Image Create ## Quick workflow 1. Confirm goal in one line (what image should achieve). 2. Collect minimum constraints: - subject - style - aspect ratio / resolution - must-include elements - must-avoid elements 3. Produce 3 prompt variants: - safe/default - bold/creative - minimal/clean 4. Generate image(s) with the selected variant. 5. Iterate with precise edits (lighting, framing, typography, colors, detail level). ## Prompt template Use this template and fill all placeholders: ```text [TYPE OF IMAGE], featuring [MAIN SUBJECT], in [STYLE], composition: [CAMERA/FRAMING], lighting: [LIGHTING], background: [BACKGROUND], color palette: [COLORS], mood: [MOOD], quality: high detail, clean edges, output: [ASPECT RATIO OR SIZE]. Avoid: [NEGATIVE CONSTRAINTS] ``` ## Fast defaults - If user gives little detail, default to: - style: photorealistic (real camera look, natural skin texture, true-to-life materials) - lighting: soft natural light - composition: centered medium shot - lens/camera feel: 50mm, shallow depth of field, realistic dynamic range - avoid: CGI/plastic look, over-smoothing, blur, watermark, extra limbs, unreadable text, distorted faces - If text appears in image, keep wording short and ask for exact spelling. ## Realistic style tuning Use these additions whenever the user asks for realistic output: - Add quality cues: `photorealistic, natural textures, physically plausible lighting, realistic shadows`. - Add camera cues: `shot on full-frame camera, 50mm lens` (or 85mm for portraits). - Keep color grading subtle; avoid neon oversaturation unless requested. - Prefer environmental detail that supports realism (imperfections, material grain, natural reflections). ## Iteration rules - Change one major variable per iteration when debugging quality. - Keep successful parts of previous prompt; only patch weak parts. - For brand visuals, preserve palette and typography constraints across iterations. - For thumbnails/posters, prioritize readability and high contrast. ## Output format When helping with generation requests, return: 1. **Primary prompt** 2. **2 alternatives** 3. **Negative prompt** 4. **Suggested settings** (aspect ratio, style strength, number of outputs) 5. **One-line revision plan** for next iteration


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cp3d1455926-svg/cp3d1455926-svg-calendar-manager (version 2.0.0)

# 📅 Calendar Manager - 日历管理助手 ## 触发规则 **关键词：** - 日历、日程、安排 - 会议、约会、提醒 - 今天有什么安排、这周忙吗 **场景：** - 用户想查看日程安排 - 用户需要添加日程 - 用户需要会议提醒 ## 功能描述 ### 核心功能 1. **日程管理** - 添加日程 - 修改日程 - 删除日程 2. **日程查看** - 今日日程 - 本周日程 - 本月日程 3. **智能提醒** - 会议前提醒 - 重要日期提醒 - 生日提醒 4. **日程同步** - 导出日历 - 导入日历 - 重复日程 ## 权限需求 - [x] 文件读写（日程存储） - [x] 定时任务（提醒） ## 示例对话 ``` 用户：今天下午 3 点开会 助手：✅ 日程已添加 📅 会议安排 📝 主题：开会 ⏰ 时间：今天 15:00 🔔 提醒：提前 15 分钟 需要设置重复吗？比如每周一次？ 用户：今天有什么安排 助手：📅 **今日日程** (2026-03-16) ⏰ 09:00 晨会 ⏰ 15:00 开会 ⏰ 18:00 晚餐约会 ⏰ 21:00 健身 共 4 个日程 用户：这周忙吗 助手：📅 **本周日程** (3.16-3.22) 周一 (3/16) 4 个日程 周二 (3/17) 2 个日程 周三 (3/18) 3 个日程 周四 (3/19) 1 个日程 周五 (3/20) 5 个日程 周六 (3/21) 0 个日程 周日 (3/22) 1 个日程 最忙的是周五！ 用户：提醒我明天交报告 助手：✅ 提醒已设置 📝 任务：交报告 ⏰ 时间：明天 🔔 提醒：提前 1 小时 🔁 重复：一次性 用户：取消下午的会议 助手：✅ 日程已取消 📅 已删除：开会 ⏰ 原时间：今天 15:00 ``` ## 数据存储 ```json // ~/.openclaw/workspace/calendar-manager/events.json { "events": [ { "title": "开会", "start": "2026-03-16T15:00:00", "end": "2026-03-16T16:00:00", "reminder": 15, "repeat": null } ] } ``` ## 待办事项 - [ ] 日程 CRUD - [ ] 日历视图 - [ ] 提醒功能 - [ ] 重复日程 - [ ] 导出导入 --- **版本：** 1.0.0 **创建日期：** 2026-03-16 **作者：** 小鬼 👻


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: sunnyhot/opik-reporter (version 1.0.0)

--- name: opik-reporter version: 1.0.0 description: Opik observability reporter - fetches traces, analyzes usage, costs, and errors, then sends reports to Discord. author: sunnyhot license: MIT keywords: - opik - observability - tracing - cost-analysis - discord --- # Opik Reporter - Opik 可观测性报告器 **自动获取 Opik 追踪数据，分析使用量和成本，发送报告到 Discord** --- ## ✨ 核心功能 ### 🔍 **追踪分析** - ✅ 获取最近的 traces 和 spans - ✅ 分析 LLM 调用 - ✅ 分析工具调用 - ✅ 识别错误和失败 ### 📊 **使用量统计** - ✅ Token 使用量 - ✅ 成本统计 - ✅ 请求数量 - ✅ 成功率 ### 📈 **报告生成** - ✅ 每小时摘要报告 - ✅ 每日详细报告 - ✅ 错误报告 - ✅ 成本优化建议 ### 💬 **Discord 推送** - ✅ 自动推送到 Discord 子区 - ✅ 格式化的报告 - ✅ 错误告警 --- ## 🚀 **使用方法** ### **1. 自动报告模式**（推荐） 创建定时任务，每小时发送一次报告： ```bash openclaw cron add \ --name "opik-reporter" \ --cron "0 * * * *" \ --tz "Asia/Shanghai" \ --session isolated \ --wake now \ --deliver \ --message "运行 opik-reporter: 获取 Opik 追踪数据，生成报告。报告格式用中文。" ``` --- ### **2. 手动触发分析** ```bash node /Users/xufan65/.openclaw/workspace/skills/opik-reporter/scripts/reporter.cjs ``` **功能**: - 获取最近的追踪数据 - 分析使用量和成本 - 生成报告 - 推送到 Discord --- ## 📋 **报告格式** ### **每小时报告** ``` 📊 Opik 追踪报告 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 时间范围: 最近 1 小时 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 使用量统计 总请求数: 15 成功率: 93.3% Token 使用: 输入: 12,345 输出: 6,789 总计: 19,134 成本: 总计: $0.05 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 工具调用 总调用: 45 次 最常用: • web_search (15次) • read (12次) • exec (10次) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ❌ 错误 (1个) • timeout in deals-morning (2分钟前) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 优化建议 1. deals-morning 超时，建议增加超时时间 ``` --- ## 🔧 **配置文件** ### `config/settings.json` ```json { "opik": { "apiKey": "I5xMCUAO14m4EyckFWfqQla3O", "workspace": "default", "project": "openclaw" }, "discord": { "channel": "discord", "to": "channel:1481801666851373138" }, "reportSchedule": { "hourly": true, "daily": true, "onError": true } } ``` --- ## 📝 **更新日志** ### v1.0.0 (2026-03-13) - ✅ 初始版本 - ✅ 追踪分析 - ✅ 使用量统计 - ✅ 报告生成 - ✅ Discord 推送 --- **🚀 让你的 Opik 追踪数据更加可视化和可操作！**


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

--- name: mem description: Search local memory index (local-first). Use for /mem queries in Telegram. user-invocable: true --- # Memory Search (/mem) ## Overview Run local-first memory search using the cached index. ## Usage 1) Update the index if needed: ```bash scripts/index-memory.py ``` 2) Search the index with the user query: ```bash scripts/search-memory.py "<query>" --top 5 ``` ## Output Return the top hits with their paths and headers. Summarize briefly if needed.


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