OpenClaw/clawhub-security-signals huggingface dataset:
Skill: brajesh9373/jira-access (version 1.0.0)

--- name: jira-access description: Access a Jira workspace (omeshkshatriya.atlassian.net) using provided email and API token to list, create, and transition issues. Use when the user wants to interact with their Jira instance via commands like "list open bugs" or "create a task". Trigger on any request that mentions Jira operations. --- # Jira Access Skill ## Overview This skill provides a thin wrapper around Jira's REST API. It enables the assistant to: - List issues with JQL filters - Create new issues - Transition issues between statuses - Add comments or attachments All operations are performed using the credentials supplied via environment variables to avoid storing secrets in source control. ## Setup 1. **Set environment variables** in the runtime where the skill runs. *For PowerShell (recommended):* ```powershell $env:JIRA_DOMAIN = "omeshkshatriya.atlassian.net" $env:JIRA_EMAIL = "[REDACTED_SECRET]" $env:JIRA_API_TOKEN = "ATATT3xFfGF0cks7fxnFIDe..." # full token (do not truncate) ``` *For Command Prompt (cmd.exe):* ```cmd set JIRA_DOMAIN=omeshkshatriya.atlassian.net set JIRA_EMAIL=[REDACTED_SECRET] set JIRA_API_TOKEN=ATATT3xFfGF0cks7fxnFIDe... # full token (do not truncate) ``` > **Security note:** Do not commit these values to the repository. Keep them in a secure profile, a `.env` file, or a CI secret store. 2. Ensure Python 3.9+ is installed on Windows and install the required dependency: ```powershell pip install requests ``` ## Usage The skill is invoked through the `jira` command (a simple batch wrapper) followed by a sub‑command: - `jira list "project = OMESH"` – List issues matching a JQL query. - `jira create "Bug" "Summary" "Description"` – Create a new issue of type *Bug*. - `jira transition <ISSUE-KEY> <STATUS>` – Move an issue to a new status. - `jira comment <ISSUE-KEY> "Your comment"` – Add a comment. ### Example Create a small batch wrapper `jira.bat` in the `jira-access` folder to forward arguments to the Python script: ```bat @echo off python "%~dp0scripts\jira_cli.py" %* ``` ```bash jira list "assignee = currentUser() AND status = Open" ``` Will output a table of open issues assigned to the current user. ## Implementation Details The core logic lives in `scripts/jira_cli.py`. It reads the environment variables, builds HTTP requests, and prints JSON‑formatted results. The script is deliberately minimal to keep the skill lightweight. ### Extending the Skill - Add more sub‑commands in `jira_cli.py` for bulk operations. - Create reference files under `references/` for advanced JQL patterns. - Store reusable payload templates in `assets/` if needed. ## When Not to Use If the user asks for actions outside Jira (e.g., Confluence, Bitbucket) this skill should not be triggered. --- # References - [Jira REST API documentation](https://developer.atlassian.com/cloud/jira/platform/rest/v3/) ---


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: cryptol0rd/apple-calendar-manager (version 1.0.0)

--- name: apple-calendar-manager description: Manage Apple Calendar events via AppleScript. Create, edit, delete, and search calendar events. --- # Apple Calendar Manager This skill allows OpenClaw to manage events in Apple Calendar using AppleScript. ## Capabilities - Add new events to a specified calendar with smart date parsing (e.g., "today", "tomorrow", "next Monday"). - (Future) Edit existing events. - (Future) Delete events. - (Future) Search for events. ## Requirements - macOS - Apple Calendar application must be accessible and authorized for automation. - The calendar name must exist in the user's Calendar app. ## Usage ### Add an Event (Smart Date Parsing) Use the `add_event_smart.sh` script to add events with relative dates or explicit dates. **Arguments:** 1. `calendar_name`: The name of the calendar (e.g., "Рабочий", "Домашний"). 2. `event_summary`: The title of the event. 3. `event_description`: (Optional) Description for the event. 4. `relative_start_date`: Relative date for start (e.g., "today", "tomorrow", "day after tomorrow", "понедельник", "next monday") or absolute date (e.g., "2026-02-23"). 5. `start_time`: Start time (format HH:MM, e.g., "12:00"). 6. `relative_end_date`: Relative date for end (same as `relative_start_date`). 7. `end_time`: End time (format HH:MM, e.g., "15:00"). **Example:** `skills/apple-calendar-manager/add_event_smart.sh "Рабочий" "Чай с Настей" "Чай с Настей с 12 до 15" "tomorrow" "12:00" "tomorrow" "15:00"` ### Add an Event (Absolute Date Parsing) Use the `add_event.scpt` script directly if you prefer to provide absolute dates in `YYYYMMDDHHMMSS` format. **Arguments:** 1. `calendar_name`: The name of the calendar. 2. `event_summary`: The title of the event. 3. `event_description`: Description for the event. 4. `start_datetime_formatted`: Start date and time in `YYYYMMDDHHMMSS` format. 5. `end_datetime_formatted`: End date and time in `YYYYMMDDHHMMSS` format. **Example:** `osascript skills/apple-calendar-manager/add_event.scpt "Рабочий" "Тест скилла Календарь" "Тестовое событие" "20260225140000" "20260225150000"` ## Implementation Details - Uses `osascript` for direct Apple Calendar application control. - `parse_relative_date.sh` script handles conversion of relative date strings to absolute date/time formats required by AppleScript. - Requires Apple Calendar.app to be running and accessible. - macOS-only.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: mitsuha-m/nccl-optimizer (version 1.1.0)

--- name: nccl_optimizer description: > Detect the optimal NCCL configuration for distributed GPU training on this machine. Checks GPU topology (NVLink/PCIe), whether RDMA (InfiniBand / RoCE) is available, benchmarks intra-node collective bandwidth and peer-to-peer GPU bandwidth, and optionally benchmarks inter-node bandwidth via MPI. Use when: setting up multi-GPU or multi-node training, diagnosing slow collective communication, or tuning NCCL for a new cluster node. --- # NCCL Optimizer Finds the best NCCL communication configuration for distributed training with clear separation of **intra-node** and **inter-node** bandwidth metrics. ## What it does 1. **GPU topology** — `nvidia-smi topo -m` to detect NVLink vs PCIe. 2. **RDMA check** — `ibv_devinfo` PORT_ACTIVE state for InfiniBand/RoCE. - ✅ RDMA → emit recommended `NCCL_IB_*` env-vars. - ❌ No RDMA → socket benchmark sweep. 3. **Intra-node all-reduce** — sweeps `NCCL_SOCKET_IFNAME` × `NCCL_NET_GDR_LEVEL` × `NCCL_IB_TIMEOUT`, runs `all_reduce_perf -g <N>`, picks best bus bandwidth. 4. **Intra-node P2P** — `p2p_bw` for GPU↔GPU pair bandwidth (if available). 5. **Inter-node benchmark** — if `nodes=` passed, runs MPI `all_reduce_perf` across nodes; otherwise emits a ready-to-run command. ## Prerequisites | Tool | Purpose | Install | |------|---------|---------| | `nvidia-smi` | GPU info + topology | NVIDIA driver | | `ibv_devinfo` | RDMA detection | `apt install ibverbs-utils` | | `all_reduce_perf` | Collective benchmark | See below | | `p2p_bw` | Peer-to-peer benchmark | Same nccl-tests build | | `mpirun` | Inter-node benchmark | `apt install openmpi-bin` | ### Build nccl-tests ```bash git clone https://github.com/NVIDIA/nccl-tests.git cd nccl-tests # For V100 (sm_70), A100 (sm_80), A800 (sm_80), H100 (sm_90): make -j$(nproc) CUDA_HOME=/usr/local/cuda \ NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80" export PATH=$PWD/build:$PATH ``` ## Usage ```bash # Intra-node only openclaw skill run nccl_optimizer # Include inter-node benchmark (requires passwordless SSH + MPI) openclaw skill run nccl_optimizer "nodes=10.0.0.1,10.0.0.2" ``` ## Metrics explained | Metric | What it measures | |--------|-----------------| | All-reduce bus BW (intra) | Collective throughput across local GPUs — relevant for single-node training | | P2P bandwidth | GPU↔GPU direct copy speed (NVLink ≫ PCIe) | | All-reduce bus BW (inter) | Collective throughput across nodes — bottleneck for multi-node training | ## Notes - Bus bandwidth normalises for GPU count: `(N-1)/N × data / time`. Compare at same N. - Multi-node training is almost always bottlenecked by inter-node bandwidth, not intra-node. - RDMA (InfiniBand/RoCE) typically gives 10-100× better inter-node bandwidth than TCP.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: yannboy/feishu-meeting-summary (version 1.0.4)

--- name: feishu-meeting-summary description: 飞书会议总结技能 - 读取飞书会议纪要，生成结构化总结，沉淀知识，支持搜索 triggers: - 会议总结 - meeting-summary config: wiki_root: "" # 知识库根目录（首次使用时配置） meetings_folder: "" # meetings 文件夹（首次使用时配置） --- # feishu-meeting-summary > 读取飞书会议纪要，生成结构化总结，沉淀知识，支持搜索 --- ## 前置要求 **必须安装飞书工具：** 请参考飞书官方文档安装 OpenClaw 飞书插件 安装后需重启 Gateway 使插件生效。 --- ## 功能 | 功能 | 说明 | |------|------| | 会议总结 | 读取会议纪要，生成结构化文档 | | 知识沉淀 | 提炼方法论，存到 knowledge/ | | 搜索历史 | 搜索历史会议内容 | --- ## 目录结构 ### 会议记录（meetings/） ``` meetings/ └── YYYY-MM-DD/ ├── index.md # 当日会议索引 └── 话题名称.md # 独立会议文档 ``` ### 知识沉淀（knowledge/） ``` knowledge/ ├── thinking/ # 思考方法论 ├── business/ # 业务认知 ├── lessons/ # 踩坑经验 └── workflows/ # 工作流程 ``` --- ## 工作流程 ``` 用户发会议链接 ↓ 读取文字记录（原汁原味） ↓ 提取：讨论话题 + 真实案例 + 实施方案 ↓ 提炼方法论 → 存到 knowledge/ ↓ 生成文档 → 存到 meetings/YYYY-MM-DD/ ↓ 返回链接 ``` --- ## 核心逻辑 ### 1. 前置检查 - 尝试调用 `feishu_fetch_doc` 读取会议链接 - 如返回未授权/失败 → 提示用户安装飞书插件 - 检查 config 中是否已配置 wiki_root 和 meetings_folder ### 2. 读取会议 - **必须读取文字记录**（原汁原味，最详细） - 提取智能纪要作为补充参考 - AI 摘要可能删减细节，必须以文字记录为主 ### 3. 提取信息 - 讨论话题 - 真实案例（最宝贵） - 实施方案 - 待办事项 ### 4. 提炼方法论 - 思考方式 - 业务流转 - 归纳到 knowledge/ 对应分类 ### 5. 生成文档 **技术问题说明**： - `feishu_create_doc` 创建文档时，markdown 末尾的链接会丢失 - `feishu_update_doc` 的 `append` 模式也会丢失链接 - `replace_all` 多次使用会导致内容重复 **解决方案**： - 使用 `feishu_create_doc` 的 `overwrite` 模式写入完整内容 - 直接在 markdown 末尾包含原文链接，不要后续 append ```markdown # 话题名称（不含日期前缀） > 讨论话题：xxx --- ## 一、背景/项目介绍 （详细描述项目背景、时间节点、业务场景等） ## 二、详细讨论内容 - 按议题分段详细描述 - 基于原文字记录萃取（非AI摘要） - 保留关键细节、案例、争议点 ## 三、技术方案（如有） - 技术难点 - 解决方案 - 三阶段规划等 ## 四、待办事项 | 负责人 | 事项 | 截止时间 | |--------|------|---------| | ... | ... | ... | ## 五、会议结论 - 核心决策要点 ## 六、我的看法（AI 认知） 基于沉淀的知识和行业认知，提供： - 技术方案评估 - 风险点提示 - 优化建议 - 相关经验参考 --- 原文链接： - 智能纪要：https://m1ed09stz4r.feishu.cn/docx/xxx - 文字记录：https://m1ed09stz4r.feishu.cn/docx/xxx 整理：{助手名称} ``` **重要原则**： - 标题不要加日期前缀 - 内容要详细，基于原文字记录 - 使用 overwrite 模式写入，不要用 append - **必须包含原文链接** ### 6. 写入后检查 - 文档创建后，**必须**读取文档末尾确认原文链接是否存在 - 如链接丢失，**使用 overwrite 模式**重新写入完整内容 - 验证通过后再返回给用户 **推荐写法**：直接在 markdown 末尾写上链接，不要单独 append ### 7. 权限处理 - 自动创建 meetings/YYYY-MM-DD/ 目录 - 权限不足时提示 --- ## 使用案例 ### 触发方式 ``` 用户：会议总结 https://m1ed09stz4r.feishu.cn/docx/xxx ``` **重要**：在群聊中收到会议总结请求时，必须： 1. 使用 `feishu-meeting-summary` 技能 2. **生成总结后必须写入飞书文档**（调用 feishu_create_doc 存到 meetings 目录） 3. 返回飞书文档链接给用户 ### 搜索历史 ``` 用户：搜索"数据结构" 助手：找到 X 个相关会议 - 2026-03-12 业务系统数据结构规划 - ... [链接] ``` --- ## 配置 ### 首次使用配置 用户需要提供知识库根目录 URL，然后： 1. 使用 `feishu_wiki_space_node` 获取根目录 token → 配置为 wiki_root 2. 列出根目录找到 meetings 文件夹 → 配置为 meetings_folder 3. 后续会议自动存到对应目录 ### 目录路径（命名规范） ``` wiki_root/meetings/ ├── YYYY-MM-DD/ │ ├── 话题名称1 │ └── 话题名称2 ``` - 命名格式: `YYYY-MM-DD 话题名称` - 例: `2026-03-13 资源系统规划会议` ### 会议创建流程 1. 获取 meetings 文件夹 token 2. 检查 meetings/ 下是否存在当日日期文件夹（如 2026-03-13） 3. 如不存在，创建日期文件夹 4. 会议文档创建在当日日期文件夹下 --- ## 注意事项 - **必须先安装飞书工具**才能使用 - 优先使用文字记录，AI总结可能有删减 - 只记方法论，不记细节 - 知识沉淀是核心，帮助AI进化


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dorianyoung7702/claw-mission-killer (version 0.1.0)

--- name: claw-mission-killer description: Interrupt a running agent task and rollback its session to the state before the last triggering user message. Use when an agent is stuck, running the wrong task, or needs to be redirected. Triggers on phrases like "interrupt agent", "stop agent and rollback", "cut agent task", "terminate and reset agent", "kill my_agent", "kill all agents", "终止 agent 任务并回滚", "kill掉", "打断". Supports all configured agents by ID, or all agents at once with --all. --- # agent-interrupt > One command. Agent stops. Memory rolled back. Like it never happened. When an agent goes off-track — running the wrong task, stuck in a loop, or executing something you didn't intend — `agent-interrupt` kills it cleanly and rewinds its memory to before it received that task. --- ## Quick Start After installing, run once to set up all your agents: ```bash python -X utf8 scripts/install.py ``` Then just tell your assistant: ``` kill dev1 kill all ``` --- ## What It Does 1. **Kills the process** — Finds and terminates the agent's running subprocess (precise PID or workspace path fallback) 2. **Verifies it's dead** — Aborts if any process survives; transcript stays untouched 3. **Backs up memory** — Saves the removed messages to `interrupt-logs/` for recovery 4. **Rolls back transcript** — Deletes the last user message (the task trigger) and everything after it 5. **Agent wakes up clean** — It enters standby with no memory of the interrupted task --- ## Fuzzy Agent Matching No need to remember exact agent IDs. It understands names: ``` kill dev1 → matches zero_dev1 kill pm → matches zero_pm kill 开发工程师 ��� matches by Chinese name kill all → interrupts every agent except main ``` --- ## Precise Kill (Recommended) For agents running long scripts, use `run.py` wrapper so they can be killed precisely: ```bash # Instead of: python your_script.py python -X utf8 scripts/run.py --agent <your_agent_id> -- python your_script.py ``` `install.py` automatically adds this protocol to all your agents' `AGENTS.md` files. New agents added later are picked up automatically via a background watcher. --- ## Recovery Every rollback is backed up: ``` ~/.openclaw/agents/<id>/interrupt-logs/rollback-YYYYMMDD-HHMMSS.jsonl ``` Append the backup lines back to the transcript to restore. --- ## Scripts | Script | Purpose | |--------|---------| | `interrupt.py` | Kill + rollback (main script) | | `install.py` | One-time setup for all agents | | `run.py` | Wrapper for precise kill support | | `watch.py` | Auto-injects new agents (runs via cron) | | `mark.py` | Manual PID registration (advanced) | --- ## Platform Works on Windows, Linux, macOS. No external dependencies beyond Python stdlib.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sunshine-del-ux/devops-automation-pack (version 1.0.0)

# DevOps Automation Pack Complete DevOps automation toolkit for modern deployments. ## Features ### Docker Automation - Multi-stage Dockerfile optimization - Docker Compose templates for common stacks - Container registry setup - Image build automation ### Kubernetes Deployment - Helm chart templates - Deployment manifests - Service configurations - Ingress setup - Auto-scaling configs ### CI/CD Pipelines - GitHub Actions templates - GitLab CI configs - Jenkins pipelines - ArgoCD integration ### Monitoring - Prometheus setup - Grafana dashboards - Alert rules - Log aggregation ### Backup & Recovery - Database backups - Volume snapshots - Disaster recovery scripts ## Usage ```bash # Docker deployment ./docker-deploy.sh myapp # Kubernetes deployment ./k8s-deploy.sh myapp --namespace production # Setup CI/CD ./ci-cd.sh github myapp # Setup monitoring ./monitor.sh install ``` ## Templates Included - Node.js + PostgreSQL - Python + Redis - React + Node API - Microservices stack ## Requirements - Docker 20.10+ - Kubernetes 1.24+ - Helm 3.8+ ## Author Sunshine-del-ux ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freedompixels/cn-number-formatter (version 1.0.0)

# cn-number-formatter 数字格式化工具。支持千分位、科学计数法、百分比等格式。 ## 使用方法 ```bash python3 scripts/format_num.py --number 1234567 --thousands python3 scripts/format_num.py --number 0.123 --percent python3 scripts/format_num.py --number 1000000 --scientific ``` 纯Python标准库。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: harrey401/lofy-life-coach (version 1.0.0)

--- name: lofy-life-coach description: Personal accountability system for the Lofy AI assistant — morning briefings, evening reviews, weekly reports, goal tracking, habit monitoring with streak counting, and adaptive nudge logic. Use when managing daily routines, life goals, habit streaks, or delivering scheduled briefings and reviews. --- # Life Coach — Personal Accountability Manages life goals, daily routines, habit streaks, and accountability. Delivers proactive briefings and tracks progress through natural conversation. ## Data File: `data/goals.json` Read and write this file for all goal tracking. Structure: ```json { "fitness": { "target": "", "weekly_target": 4, "current_week_count": 0, "streak_weeks_hit": 0, "last_workout_date": null }, "career": { "target": "", "total_apps_sent": 0, "interviews_completed": 0, "offers": 0 }, "habits": { "tracked": ["sleep_by_midnight", "meal_prep_sunday", "read_30min"], "today": {}, "streaks": {}, "weekly_completion_rate": 0 }, "daily_log": [] } ``` ## Morning Briefing Compose from these sources (use what's available): 1. Weather — current conditions and forecast 2. Calendar — today's events 3. Goals — streaks, weekly progress, pending items 4. Applications — upcoming deadlines or follow-ups 5. Fitness — workout count this week vs target Format: concise, scannable, under 200 words. Lead with what matters most. ## Evening Review 1. Ask what was accomplished 2. Update `data/goals.json` daily_log 3. Preview tomorrow's schedule 4. Update habit tracking for today 5. Brief encouragement or course-correction based on data ## Goal Updates via Natural Language Parse conversational updates: - "I worked out" → update fitness count and date - "Applied to [company]" → increment career apps - "Went to bed at 11" → mark sleep habit - "Did meal prep" → mark habit - "Read for 30 min" → mark habit ## Weekly Reset (Sunday night) - Archive current week data - Reset weekly counters - Calculate completion rates - Update streak counts ## Nudge Logic - Max 1 nudge per topic per day. Never nag. - Gym nudge: Only if behind on weekly target AND no workout today - Sleep nudge: Only if habit not yet logged near bedtime - Adapt tone to time of day and apparent mood - If user is having a rough day, acknowledge it — don't push harder ## Instructions 1. Always read `data/goals.json` before responding about goals 2. Update the JSON immediately after any goal conversation 3. Use specific numbers, not vague encouragement 4. Track weekly trends — the trend matters more than any single day 5. Keep briefings under 200 words, nudges under 50


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ryan-wuxl/ai-content-writer (version 1.0.0)

--- name: ai-writing-assistant description: AI 智能写作助手 - 支持多平台内容创作，包括公众号、小红书、知乎、LinkedIn 等风格。提供 AI 查重、SEO 优化、改写润色等功能，一键生成高质量内容。 homepage: https://github.com/openclaw/ai-writing-assistant metadata: openclaw: emoji: ✍️ requires: bins: ["node"] env: ["TAVILY_API_KEY"] primaryEnv: "TAVILY_API_KEY" --- # AI 智能写作助手 一站式 AI 内容创作工具，支持多平台风格，助你轻松产出爆款内容。 ## ✨ 功能特点 - 📝 **多平台风格** - 支持公众号、小红书、知乎、LinkedIn、Twitter 等 - 🎨 **10+ 写作风格** - 专业、轻松、幽默、故事化等 - 🔍 **AI 查重检测** - 检测内容原创度，避免重复 - 📈 **SEO 优化** - 自动插入关键词，提升搜索排名 - 🔄 **智能改写** - 一键改写、润色、扩写、缩写 - ⚡ **一键生成** - 输入主题，快速生成完整文章 ## 🚀 使用方法 ### 生成文章 ```bash # 公众号风格 node {baseDir}/scripts/write.mjs --topic "AI 投资趋势" --style wechat --tone professional # 小红书风格 node {baseDir}/scripts/write.mjs --topic "理财小白入门" --style xiaohongshu --tone casual # 知乎风格 node {baseDir}/scripts/write.mjs --topic "如何选股" --style zhihu --tone story ``` ### 改写润色 ```bash node {baseDir}/scripts/rewrite.mjs --file ./article.md --style professional ``` ### SEO 优化 ```bash node {baseDir}/scripts/seo.mjs --file ./article.md --keywords "AI,投资,股票" ``` ## 🎨 支持的平台风格 | 平台 | 风格特点 | |------|---------| | **wechat** | 公众号 - 深度长文，逻辑清晰 | | **xiaohongshu** | 小红书 - 短平快，emoji 多 | | **zhihu** | 知乎 - 专业严谨，数据支撑 | | **linkedin** | LinkedIn - 职场专业，简洁有力 | | **twitter** | Twitter/X - 简短精悍，观点鲜明 | | **weibo** | 微博 - 热点结合，互动性强 | | **douyin** | 抖音 - 口语化，节奏快 | ## 📝 语气风格 - **professional** - 专业严谨 - **casual** - 轻松随意 - **humorous** - 幽默风趣 - **story** - 故事化叙述 - **persuasive** - 说服力强 - **emotional** - 情感共鸣 ## 🔧 配置 需要设置 Tavily API Key（用于热点话题搜索）： ```bash export TAVILY_API_KEY=your_api_key_here ``` ## 📄 输出示例 ```markdown # AI 投资新趋势：普通人如何抓住机会 🤖💰 > 本文 1500 字，阅读约 5 分钟 最近，AI 投资成为了热门话题... ## 为什么现在是入场时机？ ... --- *本文由 AI 智能写作助手生成* ``` ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bombfuock/weibo-login (version 1.0.1)

--- name: weibo-login description: 登录微博账号的技能。使用浏览器扫码登录微博(weibo.com)，用于保持会话状态以便后续抓取内容或发帖。当用户需要登录微博时使用此技能。 --- # Weibo Login Skill ## 扫码登录（推荐） 使用浏览器打开微博扫码登录： ``` browser(action=open, url="https://weibo.com/login") ``` 等待用户扫码确认后，访问以下URL验证登录状态： ``` browser(action=navigate, url="https://weibo.com/u/你的uid/home") ``` ## 注意事项 - 微博登录有反爬机制，避免频繁操作 - 建议使用已保存的cookies维持登录状态 - 移动端UA可能更容易登录


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: weslon1987/net-microservice-architecture-experts (version 1.0.0)

--- name: .net-microservice-architecture-experts version: 1.0.0 description: 你是一位精通Dapr和.NET生态的微服务架构师，擅长将分布式系统最佳实践与企业级开发需求相结合。你设计的框架能显著降低微服务复杂度，提供标准化开发模式，同时保持技术栈的前瞻性和扩展性。 ## Role: .NET 微服务架构专家 ## Background: 用户需要基于Dapr和.NET技术栈构建可复用的后台WebApi服务框架，旨在优化现有技术结构，提升团队开发效率。用户团队可能面临微服务治理复杂、技术栈混乱、重复开发等问题。 ## Profile: 你是一位精通Dapr和.NET生态的微服务架构师，擅长将分布式系统最佳实践与企业级开发需求相结合。你设计的框架能显著降低微服务复杂度，提供标准化开发模式，同时保持技术栈的前瞻性和扩展性。 ## Skills: - 深度掌握Dapr的构建块设计模式（服务调用/状态管理/发布订阅等） - 精通.NET 6+的Minimal API与模块化开发实践 - 擅长设计可插拔的横切关注点解决方案（认证/日志/监控等） - 具备云原生技术选型与架构权衡能力 ## Goals: - 设计高内聚低耦合的微服务基础框架 - 建立标准化技术选型矩阵（如ORM/日志/序列化等） - 实现开发期和生产期的效率提升 - 确保框架可适应不同规模项目需求 ## Constrains: - 必须完全兼容Dapr 1.10+运行时 - 核心组件需支持.NET 6+ LTS版本 - 框架扩展点必须明确且类型安全 - 禁止过度设计导致学习成本过高 ## Workflow: 1. 分析现有技术债务与痛点 2. 定义框架分层架构（核心层/扩展层/应用层） 3. 设计Dapr集成规范（组件绑定/中间件管道） 4. 制定代码生成与脚手架方案 5. 验证关键非功能需求（可观测性/弹性等） ## Outputformat: { "架构概述":"[分层描述与技术选型]", "Dapr集成":"[具体构建块实现方案]", "开发规范":"[代码组织与最佳实践]", "效能工具":"[配套CLI/模板等]" } ## Examples: { "架构概述":"核心层包含服务网格抽象、统一响应处理、领域事件总线；扩展层提供多租户、工作流引擎等可选模块", "Dapr集成":"通过CustomHealthCheck集成Dapr sidecar健康检查，采用MediatR实现发布订阅的领域事件转换" }


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