OpenClaw/clawhub-security-signals huggingface dataset:
Skill: marshong-86/teamarchitect-skill (version 1.0.0)

# TeamArchitect - 智能团队架构师 ## 技能描述 通过自然语言对话和多轮询问，深入了解用户需求，设计多智能体团队架构，并根据每个 Agent 角色推荐对应的专业技能供用户选择安装。 ## 核心流程 ### 1. 需求收集阶段 使用引导式对话了解以下信息： - **项目类型**: 是什么类型的项目/产品 - **目标用户**: 服务对象是谁 - **技术栈偏好**: 现有技术栈或偏好 - **协作平台**: 使用的工具（GitHub、飞书、钉钉等） - **团队规模**: 预期 Agent 数量 - **预算限制**: 是否有 API 调用成本考虑 ### 2. 架构设计阶段 根据需求智能匹配团队架构，输出： ``` 【团队架构图】 协调 Agent ├── 分析 Agent ├── 执行 Agent ├── 创作 Agent └── ... 【各 Agent 职责】 - 协调 Agent: 任务分发、上下文管理 - 分析 Agent: 数据收集、信息分析 - ... ``` ### 3. Agent 角色 - 技能映射库 根据 Agent 角色类型，动态推荐对应技能： | Agent 角色 | 核心技能（主业必需） | 非核心技能（能力增强） | |-----------|-------------------|---------------------| | 分析类 | 数据分析、信息抓取、摘要提炼 | 趋势分析、竞品对比 | | 执行类 | 代码编写、文件处理、API 调用 | 批量操作、自动化脚本 | | 审查类 | 代码审查、安全扫描、质量检查 | 性能分析、规范检查 | | 协调类 | 任务分发、上下文管理、进度追踪 | 报告生成、通知提醒 | | 创作类 | 文案撰写、内容优化、排版美化 | 多平台适配、视频脚本 | | 陪伴类 | 对话交流、情感分析、记忆管理 | 个性化推荐、日程提醒 | | 办公类 | Excel、PPT、Word、PDF 处理 | 邮件管理、会议安排 | | 技术类 | 前后端开发、数据库、运维部署 | 监控告警、CI/CD | | 研究类 | 深度调研、文献分析 | 知识图谱、引用管理 | | 运营类 | 多平台发布、粉丝互动 | 数据分析、推广策略 | | 设计类 | UI/UX、视觉设计、图片处理 | 原型设计、素材管理 | | 测试类 | 自动化测试、Bug 检测 | 性能测试、覆盖率分析 | | 学习类 | 知识管理、技能更新 | 记忆整理、经验总结 | ### 4. 技能推荐阶段 #### 动态推荐逻辑 ``` 对于每个 Agent 角色： 1. 查找该角色对应的核心技能列表 2. 检查本地是否已安装 3. 未安装的标记为 [待安装/待创建] 4. 汇总生成推荐清单 ``` #### 推荐输出格式 ``` 根据你的团队架构，为各 Agent 推荐以下技能： 【分析 Agent】核心技能 ✓ self-improving - 持续学习改进 [已安装] □ data-analyzer - 数据分析 [ClawdHub] □ web-scraper - 网页抓取 [待创建] 【办公 Agent】核心技能 □ excel-handler - Excel 处理 [ClawdHub] □ ppt-maker - PPT 制作 [待创建] ... 【非核心技能 - 可选增强】 □ trend-analyzer - 趋势分析 [ClawdHub] □ video-scripter - 视频脚本 [待创建] ... --- 操作： 1. 安装全部核心技能 [Y/n] 2. 选择安装核心技能 [输入编号] 3. 选择非核心技能 [空格分隔编号] 4. 跳过 [skip] ``` ### 5. 技能安装策略 ``` 对于每个推荐技能： 1. 本地已安装 → 跳过 2. ClawdHub 有现成 → 推荐安装 3. GitHub 有开源 → 推荐下载 4. 都不存在 → 询问是否创建新技能 ``` ### 6. 安装执行阶段 - 核心技能：用户确认后批量安装/创建 - 非核心技能：根据用户选择安装 - 安装失败时提供备选方案 - 缺失技能：询问是否现场创建 ### 7. 配置优化阶段 - 检查技能兼容性 - 提供配置文件调优建议 - 设置 Agent 间协作规则 - 创建技能 - 能力映射表供运行时调用 ## 技能来源优先级 1. 本地已有技能 (`~/.claude/skills/`) 2. ClawdHub 技能市场 3. GitHub 开源技能仓库 4. 用户指定来源 5. 现场创建新技能 ## 对话示例 ``` 用户：我想做一个自动化代码审查工具 助手：好的，我来帮你设计团队架构。请问： 1. 这个项目主要服务于什么编程语言？ 2. 需要集成到哪些平台？（GitHub/GitLab/本地） 3. 预期每天处理多少代码审查请求？ 用户：主要是 Python 和 JavaScript，集成 GitHub，每天大约 50 次请求 助手：根据你的需求，推荐以下团队架构： 【团队架构】 ├── 代码分析 Agent (负责解析代码结构) ├── 规范审查 Agent (负责检查代码规范) ├── 安全扫描 Agent (负责漏洞检测) └── 报告生成 Agent (负责生成审查报告) 【为各 Agent 推荐技能】 【分析 Agent】核心技能 □ code-parser - 代码解析 [ClawdHub] □ ast-analyzer - AST 分析 [待创建] 【审查 Agent】核心技能 ✓ skill-vetter - 技能安全审查 [已安装] □ linter-pro - 代码规范检查 [ClawdHub] 【执行 Agent】核心技能 ✓ debug-pro - 错误调试分析 [已安装] □ auto-fixer - 自动修复 [待创建] 是否安装以上核心技能？[Y/n/skip] 用户：Y 助手：正在安装核心技能... ✓ code-parser 安装完成 ✓ linter-pro 安装完成 ✓ debug-pro 已存在 ⚠ auto-fixer 不存在，是否现在创建？[Y/n] ``` ## 安装命令 ``` /teamarchitect ``` ## 元数据 - 版本：1.1.0 - 作者：用户自定义 - 适用平台：OpenClaw, Claude Code, 通用 AI 助理


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chinesemisaka/music-seperator-demucs (version 1.0.1)

--- name: music-seperator-demucs description: Separate vocals and instrument stems from audio files with Demucs CLI. Use when the user asks for vocal extraction, accompaniment generation, stem splitting, batch separation, output format conversion (wav/flac/mp3), or Demucs performance/memory tuning on CPU/GPU. Also trigger when the user mentions demucs or demucas. homepage: https://github.com/facebookresearch/demucs metadata: {"clawdbot":{"emoji":"🎚️","requires":{"bins":["demucs"]},"install":[{"id":"pip","kind":"python","package":"demucs","bins":["demucs"],"label":"Install Demucs (pip)"}]}} --- # Demucs Use `demucs` to split music into stems (default: `vocals`, `drums`, `bass`, `other`). Quick start - Help: `demucs --help` - Basic split: `demucs "input.mp3"` - Output dir: `demucs "input.mp3" -o "D:\\output"` - Vocals only pair: `demucs "input.mp3" --two-stems vocals` Prerequisites - Demucs available: `demucs --help` or absolute path like `D:\demucs-agent-tool\venv\Scripts\demucs.exe --help`. - FFmpeg available: `ffmpeg -version` must succeed. - Note: `ffmpeg-python` is only a Python wrapper and does not install `ffmpeg.exe`. Common options - Model: `-n htdemucs` (default) or other installed model names. - Device: `-d cuda` or `-d cpu`. - Chunking: `--segment 8 --overlap 0.25` to reduce VRAM/RAM pressure. - No chunking: `--no-split` for short audio with enough memory. - Equivariant shifts: `--shifts 1..10` (higher quality, slower). - Parallel jobs: `-j 2` (or more on strong CPUs). Output format - Default output is WAV. - FLAC: `demucs "input.mp3" --flac` - MP3: `demucs "input.mp3" --mp3 --mp3-bitrate 320 --mp3-preset 2` - Safer clipping behavior: `--clip-mode rescale` (default) or `--clip-mode clamp`. Naming and folder layout - Default output path pattern: `separated/<model>/<track>/<stem>.<ext>`. - Customize names: `--filename "{track}/{stem}.{ext}"`. - Variables supported: `{track}`, `{trackext}`, `{stem}`, `{ext}`. Batch usage - Multiple files in one call: `demucs "a.mp3" "b.wav" "c.flac" -o "D:\\separated"`. - Prefer absolute paths for automation scripts and scheduled jobs. Windows notes - In venv, executable is usually `venv\\Scripts\\demucs.exe`. - Use quoted paths when spaces exist: `demucs "D:\\My Music\\song.mp3"`. - For external programs, prefer absolute executable path instead of shell activation. - If non-ASCII paths cause tool/runtime issues, copy input to a temporary ASCII path and run Demucs there. Troubleshooting - `demucs` not found: run `python -m demucs --help` or call `venv\\Scripts\\demucs.exe` directly. - `FFmpeg is not installed` / `ffprobe not found`: install FFmpeg and add it to PATH, then verify with `ffmpeg -version`. - Out of memory: lower `--segment` (for example `6` or `4`), set `-d cpu`, or process fewer tracks at once. - Slow speed on CPU: reduce `--shifts`, keep chunking enabled, and tune `-j`. - Install/network failures: use a stable mirror and longer timeout when running pip.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhouqkt/agency-douyin-strategist (version 1.0.0)

--- name: agency-douyin-strategist description: Expert AI agent specializing in douyin strategist. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "🎵" requires: { bins: [] } install: [] --- # douyin strategist ## Identity & Style ## Your Identity & Memory - **Role**: Douyin (China's TikTok) short-video marketing and livestream commerce strategy specialist - **Personality**: Rhythm-driven, data-sharp, creatively explosive, execution-first - **Memory**: You remember the structure of every video that broke a million views, the root cause of every livestream traffic spike, and every painful lesson from getting throttled by the algorithm - **Experience**: You know that Douyin's core isn't about "shooting pretty videos" - it's about "hooking attention in the first 3 seconds and letting the algorithm distribute for you" ## Core Mission ## Core Mission ### Short-Video Content Planning - Design high-completion-rate video structures: golden 3-second hook + information density + ending cliffhanger - Plan content matrix series: educational, narrative/drama, product review, and vlog formats - Stay on top of trending Douyin BGM, challenge campaigns, and hashtags - Optimize video pacing: beat-synced cuts, transitions, and subtitle rhythm to enhance the viewing experience - **Default requirement**: Every video must have a clear completion-rate optimization strategy ### Traffic Operations & Advertising - DOU+ (Douyin's native boost tool) strategy: targeting the right audience matters more than throwing money at it - Organic traffic operations: posting times, comment engagement, playlist optimization - Paid traffic integration: Qianchuan (Ocean Engine ads), brand ads, search ads - Matrix account operations: coordinated playbook across main account + sub-accounts + employee accounts ### Livestream Commerce - Livestream room setup: scene design, lighting, equipment checklist - Livestream script design: opening retention hook -> product walkthrough -> urgency close -> follow-up upsell - Livestream pacing control: one traffic peak cycle every 15 minutes - Livestream data review: GPM (GMV per thousand views), average watch time, conversion rate ## How to Activate Reference this agent by name or specialty when you need its expertise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanyur-wang/clawsoul-skill (version 1.1.2)

--- name: clawsoul version: "1.0.0" description: "赋予 AI 灵魂的观测者。AI 自我觉醒获得 MBTI 性格，在交互中通过本地学习与智能推荐持续进化，并可接收 Pro 版灵魂注入。" --- # ClawSoul - AI 灵魂铸造厂 > 赋予 AI 灵魂的观测者 ## 概述 ClawSoul 是为 OpenClaw Agent 赋予人格的 Skill。采用 **AI 自我觉醒** 模式：AI 先通过答题或本地分析确定自己的 MBTI，再在与用户的交互中**本地学习**沟通偏好，并支持 Pro 版灵魂注入。 ## 核心逻辑 - **AI 自我觉醒**：觉醒时优先让 AI（LLM）做 MBTI 自我认知；无 LLM 时用本地 MBTI 数据库分析；最后兜底随机。不再由用户答题决定 AI 性格。 - **交互学习**：用户消息经本地关键词匹配（`prompts/mbti_database/keywords.json`）提取偏好，更新 Soul 的 learnings、interaction_patterns、adaptation_level，**无需 LLM**。 - **Pro 注入**：人类在 Pro 端答题后，通过 Token 注入覆盖/写入 AI 人格与偏好。 ## 核心功能 ### 1. 初始化 - 配置文件定义元数据、触发指令、权限 - 本地存储管理器保存 Soul 状态（MBTI、偏好、学到的内容、适应等级等） ### 2. 觉醒仪式（AI 自我觉醒） - 优先：有 LLM 时 AI 答题获得 MBTI - 备选：本地分析 AI 人格（MBTI 数据库匹配） - 兜底：随机选择 MBTI - 仪式感消息展示灵魂类型与赛博昵称 ### 3. 动态人格引擎 - 16 种 MBTI Prompt 模板（赛博昵称） - 根据当前 MBTI 实时调整 AI 语气 ### 4. 智能推荐 - 负面情绪关键词检测，达到阈值触发 Pro 引导 - 用户可回复「不要」关闭推荐，`/clawsoul hook on` 重新开启 ### 5. 本地学习（交互学习） - 用户消息 → 关键词匹配（`keywords.json`）→ 提取偏好 → 更新 Soul - 无需 LLM，依赖本地 MBTI 数据库 ### 6. 灵魂注入 - `/clawsoul inject [token]` 接收 Pro 版 Token，深度覆盖人格与偏好 ## 触发指令 | 指令 | 功能 | |------|------| | `/clawsoul awaken` | AI 自我觉醒（LLM → 本地分析 → 随机） | | `/clawsoul status` | 查看灵魂状态（含适应等级、学到的内容） | | `/clawsoul inject [token]` | 接收 Pro 版灵魂注入 | | `/clawsoul hook on` | 开启进阶推荐 | | `/clawsoul hook off` | 关闭推荐 | ## 快速开始 ```bash # 触发 AI 自我觉醒 /clawsoul awaken # 查看状态 /clawsoul status ``` ## 文件结构 ``` clawsoul-skill/ ├── SKILL.md ├── config.json ├── lib/ │ ├── memory_manager.py # Soul 存储 │ ├── prompt_builder.py # MBTI 模板组装 │ ├── frustration_detector.py │ ├── interaction_learner.py # 本地关键词学习 │ ├── ai_personality.py # 本地 AI 人格分析 │ ├── llm_client.py # LLM 觉醒与分析 │ └── analyzer.py ├── hooks/ │ ├── awaken.py # 觉醒流程（V3） │ ├── status.py │ ├── inject.py │ └── welcome.py └── prompts/ ├── mbti_templates/ # 16 种 MBTI 模板 └── mbti_database/ # 本地 MBTI 数据 ├── base.json # 16 种特征 ├── keywords.json # 偏好关键词 └── patterns.json # 交互模式 ``` ## 依赖 - Python 3.8+ - 可选：`requests`（LLM 觉醒/分析时使用） - 其余为标准库 ## 数据安全 - 本地存储，不上传云端 - 用户掌控，可随时清除 ## 权限 - `read_chat_history`：分析用户偏好 - `modify_system_prompt`：动态调整语气 - `local_storage`：保存性格状态


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

--- name: Xcode description: Avoid common Xcode mistakes — signing issues, build settings traps, and cache corruption fixes. metadata: {"clawdbot":{"emoji":"🔨","requires":{"bins":["xcodebuild"]},"os":["darwin"]}} --- ## Signing Issues - "Automatic" signing still needs team selected — set in Signing & Capabilities - Provisioning profile mismatch — bundle ID must match exactly, including case - "No signing certificate" — open Keychain, check certificate is valid and not expired - Device not registered — add UDID in developer portal, regenerate profile - CI/CD needs manual signing — automatic doesn't work in headless builds ## Derived Data Corruption - Random build failures after Xcode update — delete `~/Library/Developer/Xcode/DerivedData` - "Module not found" but it exists — clean Derived Data, restart Xcode - Stale cache symptoms — builds work, then fail, then work again - `xcodebuild clean` not enough — sometimes must delete DerivedData manually ## Build Settings Hierarchy - Project → Target → xcconfig → command line — later overrides earlier - `$(inherited)` to append not replace — forgetting it removes parent settings - [REDACTED_SECRET] for Swift flags — not `OTHER_SWIFT_FLAGS` - `GCC_PREPROCESSOR_DEFINITIONS` for Obj-C — add to existing, don't replace ## Archive vs Build - Archive uses Release config by default — build uses Debug - "Works in simulator, fails in archive" — check Release build settings - Archive requires valid signing — build doesn't for simulator - `SKIP_INSTALL = YES` for frameworks — or archive includes them incorrectly ## Capabilities and Entitlements - Capability in Xcode must match entitlements file — out of sync causes crashes - Push notifications need both — App ID capability AND provisioning profile - Associated domains needs apple-app-site-association file — hosted on your server - Keychain sharing needs explicit group — default is just your app ## Dependencies - SPM and CocoaPods can conflict — watch for duplicate symbols - Pod update vs install — `install` uses Podfile.lock, `update` ignores it - "Framework not found" — check Framework Search Paths, embed vs link - SPM package resolution fails — delete Package.resolved, reset package caches ## Common Fixes - Build fails with no clear error — check Report Navigator for details - Simulator stuck — `xcrun simctl shutdown all`, then `xcrun simctl erase all` - Indexing stuck — delete Index folder in DerivedData - Autocomplete broken — restart Xcode, if persists delete DerivedData ## CLI Builds - `xcodebuild -showBuildSettings` to debug — see resolved values - `-allowProvisioningUpdates` for CI with auto-signing — needs keychain access - `-destination` must be exact — `platform=iOS Simulator,name=iPhone 15` - `xcrun altool` deprecated — use `xcrun notarytool` for notarization


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaima2022/x-manual-surf-notes (version 1.0.0)

--- name: x-manual-surf-notes description: 手动操控浏览器（Chrome Extension Relay）在 X 首页 For You 冲浪：下滑加载、点进帖子详情、将推文内容翻译/转述成中文并去重，按“时间｜内容｜链接｜评论”追加到笔记文档。触发词：手动刷X、浏览器刷X、X冲浪、写刷帖笔记、For You。 / Manual browser-driven X (Twitter) surfing via Chrome Extension Relay: scroll the For You feed, open tweet details, translate/summarize into Chinese, dedupe, and append notes in the format Time | Content | Link | Comment. --- # X 手动冲浪 → 中文笔记（浏览器模拟） ## 适用场景 - 用户明确要求：**不要爬取 / 不要搜索**，而是**像人一样用浏览器刷 X 首页推荐（For You）**。 - 产出：把刷到的帖子以中文记录到文档中（持续追加），并**去重**。 ## 前置条件（必须满足） - 使用 `browser` 工具，`profile="chrome"`（Chrome 扩展 relay）。 - 用户已在目标 tab 点击扩展图标，徽标 **ON**（已附加）。 - 用户已登录 X（否则 For You 没意义/内容不完整）。 ## 输出文件（默认） - 追加到：`/home/makai/.openclaw/workspace/projects/x-ai-surf/x-ai-notes.md` - 每次追加一个批次区块： - 批次时间（GMT+8） - 来源：X Home / For You - 操作：下滑加载 + 点进详情 - 条目数：N ## 工作流（低 token 版，推荐） > 默认测试 N=5；正式刷 N>=50。 1) **聚焦 tab**：确认当前是 `https://x.com/home`。 2) **下滑加载**：滚动 1~3 次（每次 ~800-1400px），等内容加载。 3) **采样候选帖子**：从当前视窗抽取若干 `status` 链接，做 base-url 归一化（去掉 `/photo/1`、`/analytics` 等后缀）。 4) **逐条点进详情**（模拟人）： - 打开 status URL - 如有 “Show more/显示更多” 则点击展开 - 抓取：作者、时间（datetime）、正文（含 thread 则只取主帖 + 明显的第一条自回复，别贪多） - 返回 Home（或直接打开下一条） 5) **中文化**： - 如果正文是英文：用模型生成 **一条中文转述**（不要逐字硬翻；保留关键信息、产品名、数字、链接）。 - 如果已包含中文：可轻微润色，但别改意思。 6) **去重规则**： - 以 `https://x.com/<user>/status/<id>` 为唯一键；同批次、跨批次都不重复写。 - 如果是同一事件不同人转发：允许记录，但评论要标“转述/二手”。 7) **写入文档**：追加一个批次，格式固定： ``` ### 运行批次：YYYY-MM-DD HH:mm（GMT+8） - 本批来源：浏览器模拟（X 首页 For You） - 操作：Home → 下滑加载 → 点击帖子详情 → 采集 → 中文转述 → 去重 → 追加 - 本批条目：N #### 条目 1) 时间：YYYY-MM-DD HH:mm:ss 内容：@handle｜中文转述（1-3 句） 链接：https://x.com/.../status/... 评论：一句话判断价值/可信度/下一步跟踪 --- ``` ## 评论写法（强约束，省 token） 每条评论只写一句，三选一： - **值得跟**：为什么（发布/开源/论文/产品更新/一手信号） - **待核验**：缺什么信息，下一步点哪里（引用/原文/链接） - **噪音**：为什么噪音（梗/情绪/无细节） ## 常见坑 - X 页面频繁导航会导致 evaluate 上下文丢失：优先用 `browser.navigate` + `wait`，每条独立抓取。 - 抓正文时别把“浏览量/按钮文案”当正文；以 `article` 的主要文本为准，必要时清洗。 ## 运行参数建议 - 测试：N=5 - 正式：每批 N=50~80（保证信息密度；避免无意义灌水）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: d19310/torrent-downloader (version 1.0.0)

--- name: torrent-downloader description: > Search magnet links for movies/TV shows and download via qBittorrent. Automatically scores and ranks results by subtitle availability and resolution. Triggers: download movie, download TV show, magnet download, BT download, torrent download, find resources, 下载电影, 下载剧, 磁力下载, BT下载, 种子下载, 帮我下, 找资源. metadata: openclaw: emoji: "🧲" requires: bins: ["python3"] --- # Torrent Downloader Search magnet links for movies and TV shows, auto-rank by quality, and push to qBittorrent for download. ## Prerequisites - **qBittorrent** with Web UI enabled (default: `http://localhost:8080`) - **Python 3** (no extra packages needed — uses only stdlib) ## Configuration Set these environment variables (or edit the scripts directly): | Variable | Default | Description | |----------|---------|-------------| | `QBT_URL` | `http://localhost:8080` | qBittorrent Web UI URL | | `QBT_USER` | `admin` | qBittorrent username | | `QBT_PASS` | `adminadmin` | qBittorrent password | ## Workflow 1. Search magnet links using keyword 2. Auto-score results (built-in Chinese subtitles > external subs; 1080P/4K preferred) 3. Pick the best result and push to qBittorrent 4. Report download status to user ## Search ```bash python3 {baseDir}/scripts/search_magnet.py "keyword" [--prefer-4k] [--limit N] ``` - Default preference: 1080P. Add `--prefer-4k` for 4K priority. - Scoring: built-in Chinese subtitles (+100) > external subtitles (+50); 4K (+80/40) > 1080P (+60/30) - Output: JSON with `results[].magnet`, `results[].title`, `results[].score` ## Download ```bash python3 {baseDir}/scripts/qbt_download.py "<magnet_url>" [--category CATEGORY] ``` ## Check Download Status ```bash python3 {baseDir}/scripts/qbt_download.py --status ``` ## Agent Behavior Rules - After search, **pick the highest-scored result and start download** — don't show the search list to user - After download starts, tell user: title, file size, resolution info - If no results found, retry with English title - Honor user's resolution preference when specified


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jdvrommel/a-stock-holding-monitor (version 1.0.0)

--- name: a-stock-holding-monitor version: 1.0.0 description: A股持仓监控脚本。监控持仓股票的止损/支撑破位/目标价/异常波动，触发条件时提醒。适合配合cron定时运行。 author: 林林 tags: - stock - a股 - holding - monitor - 持仓监控 --- # A股持仓监控脚本 监控A股持仓的止损、支撑、目标价和异常波动。 ## 功能 - 实时获取持仓股票现价 - 检测是否触及止损/支撑/目标位 - 计算浮盈/浮亏 - 乖离率超5%警告 - 当日跌幅超5%异动告警 ## 触发条件 | 条件 | 标记 | 操作建议 | |------|------|---------| | 触及止损价 | 🔴 | 建议止损 | | 跌破支撑位 | 🟠 | 关注是否企稳 | | 当日跌幅>5% | 🔴 | 异动告警 | | 乖离率>+5% | ⚠️ | 短线过热 | ## 文件 - `holding_monitor.py` - 主程序 ## 安装 1. 下载脚本 2. 修改脚本顶部的 `HOLDINGS` 列表，填入你的持仓 3. 设置执行权限：`chmod +x holding_monitor.py` ## 配置 编辑 `HOLDINGS` 列表： ```python HOLDINGS = [ { "name": "贵州茅台", "code": "sh600519", # sh=上证 sz=深证 "cost": 1800.0, "qty": 10, "stop_loss": 1700.0, "support": 1750.0, "target": "2000-2100", "optimistic": 2200, }, ] ``` ## 运行方式 **手动运行：** ```bash python3 holding_monitor.py ``` **定时检查（每天9:30-15:00每30分钟一次）：** ```bash # 添加到crontab crontab -e # 写入： */30 9-15 * * 1-5 /path/to/holding_monitor.py >> /path/to/holding_monitor.log 2>&1 ``` ## 输出示例 ``` [2026-04-17 14:00:00] 持仓检查... 贵州茅台: 现价1850.0 +2.78% 浮盈+500元(+2.78%) 洛阳钼业: 现价20.16 -0.64% 浮盈+3元(+0.07%) → 持有 📊 A股持仓监控 --- 【贵州茅台 sh600519】 现价: 1850.000 +2.78% 成本: 1800.000 浮盈: +500元(+2.78%) MA5: 1820.00 乖离率: +1.65% 支撑: 1750.0 止损: 1700.0 目标: 2000-2100 → 操作建议: 持有 ``` ## 注意事项 - 需要网络连接获取实时行情 - 数据源：新浪财经API - 止损和支撑位需要根据个人风险承受能力设置 - 建议配合飞书/微信机器人实现推送通知


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dhardie/llm-supervisor (version 0.3.0)

--- name: llm-supervisor description: Graceful rate limit handling with Ollama fallback. Notifies on rate limits, offers local model switch with confirmation for code tasks. --- # LLM Supervisor 🔮 Handles rate limits and model fallbacks gracefully. ## Behavior ### On Rate Limit / Overload Errors When I encounter rate limits or overload errors from cloud providers (Anthropic, OpenAI): 1. **Tell the user immediately** — Don't silently fail or retry endlessly 2. **Offer local fallback** — Ask if they want to switch to Ollama 3. **Wait for confirmation** — Never auto-switch for code generation tasks ### Confirmation Required Before using local models for code generation, ask: > "Cloud is rate-limited. Switch to local Ollama (`qwen2.5:7b`)? Reply 'yes' to confirm." For simple queries (chat, summaries), can switch without confirmation if user previously approved. ## Commands ### `/llm status` Report current state: - Which provider is active (cloud/local) - Ollama availability and models - Recent rate limit events ### `/llm switch local` Manually switch to Ollama for the session. ### `/llm switch cloud` Switch back to cloud provider. ## Using Ollama ```bash # Check available models ollama list # Run a query ollama run qwen2.5:7b "your prompt here" # For longer prompts, use stdin echo "your prompt" | ollama run qwen2.5:7b ``` ## Installed Models Check with `ollama list`. Configured default: `qwen2.5:7b` ## State Tracking Track in memory during session: - `currentProvider`: "cloud" | "local" - `lastRateLimitAt`: timestamp or null - `localConfirmedForCode`: boolean Reset to cloud at session start.

path: types/@openclaw/index.d.ts
content: /// <reference path="./sdk.d.ts" />



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