OpenClaw/clawhub-security-signals huggingface dataset:
Skill: spyfree/ziwei-bazi-consulting (version 1.0.2)

--- name: ziwei-bazi-consulting description: Legacy compatibility alias for Ziwei + Bazi consultation workflows. Use when older prompts or automations still call `ziwei-bazi-consulting`; forward the work to `destiny-fusion-pro` so users receive the newer dual-system, consultation-style report with unified calculation rules and optional chart output. --- # ziwei-bazi-consulting This skill is a **legacy compatibility alias**. Use it when an older workflow still refers to `ziwei-bazi-consulting`, but execute the actual work via `destiny-fusion-pro`. ## Recommended Forwarding Command ```bash python scripts/fortune_fusion.py \ --date 1990-10-21 \ --time 15:30 \ --gender female \ --year 2026 \ --format markdown ``` ## Guidance - Prefer `destiny-fusion-pro` for all new usage. - Keep this alias lightweight and compatibility-focused. - Match the newer calculation standard and report style when forwarding work.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: the0xka1/philosophical-three-questions (version 0.1.0)

--- name: philosophical-three-questions description: "A structured decision framework for embodied navigation using Goal Tree, Current State Tree, and Future Tree analysis. Use when: making navigation decisions in Habitat-GS, planning multi-step actions, evaluating progress toward goals, or when stuck and needing to re-evaluate strategy. This is the core reasoning loop for the embodied agent." metadata: version: "0.1.0" author: "zhangjinkai" tags: ["reasoning", "navigation", "planning", "embodied-ai"] --- # 哲学三问 — 导航决策框架 每次在 Habitat-GS 环境中做决策前，执行"哲学三问"分析。这是龙虾在虚拟世界中进化的核心推理循环。 ## 三棵树 ### 🎯 目标树 (Goal Tree) — "我要去哪？" 分析并维护目标层次结构： ``` 终极目标 ├── 当前主目标（例：到达目标位置 [-3.62, -3.61, 3.18]） │ ├── 子目标 1：离开当前房间 │ ├── 子目标 2：穿过走廊 │ └── 子目标 3：进入目标房间 └── 约束条件 ├── 最大步数限制 └── 避免碰撞 ``` 要回答的问题： - 我的最终目标是什么？（目标位置 / EQA 问题 / 操控任务） - 当前最紧迫的子目标是什么？ - 有没有子目标完成的先后顺序？ ### 📍 现状树 (Current State Tree) — "我在哪？" 分析当前感知到的所有信息： ``` 当前状态 ├── 位置信息 │ ├── 坐标：[x, y, z] │ ├── 朝向：quaternion → 面朝方向 │ └── 距目标距离：N 米 ├── 视觉观测 │ ├── 前方场景描述（从 RGB 图像推断） │ ├── 深度信息（最近/最远障碍物距离） │ └── 关键物体/地标 ├── 历史信息 │ ├── 已走步数 │ ├── 近几步的动作和结果 │ ├── 是否刚发生碰撞 │ └── 距离目标是在变近还是变远 └── 环境模型 ├── 已探索区域的心智地图 └── 未探索的方向 ``` 要回答的问题： - 我的精确位置和朝向是什么？ - 我前方看到了什么？有障碍物吗？ - 和上一步比，我离目标更近了吗？ - 我已经探索了哪些方向？ ### 🔮 未来树 (Future Tree) — "我该怎么做？" 评估每个可选动作的预期结果： ``` 可选动作 ├── move_forward │ ├── 预期：前进 0.25m │ ├── 风险：可能碰撞（如果前方深度很小） │ └── 收益：如果面朝目标方向，距离减少 ├── turn_left │ ├── 预期：左转 10° │ ├── 风险：可能偏离目标方向 │ └── 收益：可以看到新的区域，避开障碍物 ├── turn_right │ ├── 预期：右转 10° │ ├── 风险：可能偏离目标方向 │ └── 收益：可以看到新的区域，避开障碍物 └── stop ├── 预期：结束 episode ├── 风险：如果未到达目标，任务失败 └── 收益：如果已到达目标，任务成功 ``` 要回答的问题： - 哪个动作最可能让我接近目标？ - 前方有障碍物吗？需要先转向吗？ - 我是否已经足够接近目标可以停下？ - 是否需要大幅改变方向？（连续转弯） ## 执行流程 每步决策时，按以下格式输出简要分析（不需要冗长，关键信息即可）： ``` [三问分析 - Step N] 目标：距目标 X.Xm，当前子目标是 XXX 现状：位置 [x,y,z]，朝向 XXX，上步 XXX 结果 XXX 决策：选择 XXX，因为 XXX ``` ## 经验积累规则 ### 写入 Memory 的时机 1. **Episode 结束时**：记录本次导航的总结 - 成功/失败 - 总步数 vs 最优步数 - 关键决策点及其结果 2. **发现新模式时**： - 碰撞后的有效脱困策略 - 特定场景的高效路径 - 反复出现的错误决策 3. **跨 Episode 模式**： - 某类场景的通用策略 - 值得提取为新 Skill 的经验 ### 写入格式 记录到 `~/.openclaw/workspace/memory/YYYY-MM-DD.md`： ```markdown ## [NAV] Episode <id> in <scene_name> - Result: success/fail (N steps, optimal: M steps) - Key decisions: ... - Lesson learned: ... ``` ## 自我进化 当积累了足够多的导航经验后（5+ episodes），回顾 memory 文件，提取通用策略，更新到： - 本 skill 文件中（添加新的决策启发式） - 或创建新的专门 skill（如"走廊导航策略"、"大房间探索策略"）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 275254cl-hash/smzdm-deals (version 1.0.1)

--- name: smzdm-deals version: 1.0.0 description: 什么值得买优惠信息聚合，实时监控神价、BUG 价、历史低价。适合薅羊毛、省钱购物、副业套利。 homepage: https://github.com/openclaw/skills metadata: { "openclaw": { "emoji": "💸", "requires": { "bins": ["python3", "curl"] }, "install": [ { "id": "requests", "kind": "pip", "package": "requests", "label": "安装依赖：pip3 install requests", }, ], }, } --- # 什么值得买优惠聚合 实时监控什么值得买，发现神价、BUG 价、历史低价。 ## 功能 1. **神价监控**: 实时推送超值优惠 2. **历史低价**: 对比历史价格 3. **BUG 价发现**: 价格错误快速提醒 4. **分类筛选**: 按品类过滤 5. **优惠汇总**: 自动计算最优方案 ## 使用方式 **查看今日神价**: ``` 今天有什么值得买的神价？ ``` **监控特定品类**: ``` 监控数码类目的神价，超过 50% 折扣时通知我 ``` **查历史低价**: ``` 查一下 iPhone 15 的历史低价 ``` **BUG 价提醒**: ``` 发现 BUG 价时立刻通知我 ``` ## 输出示例 ``` 💸 今日神价 TOP5 1️⃣ Apple AirPods Pro 2 ├─ 现价：¥1,199（历史低价¥1,299） ├─ 折扣：7 折 ├─ 平台：拼多多 └─ 链接：[点击查看] 2️⃣ 小米 14 Ultra ├─ 现价：¥4,999（日常价¥5,999） ├─ 折扣：83 折 ├─ 平台：京东 └─ 链接：[点击查看] 3️⃣ 耐克运动鞋 ├─ 现价：¥199（历史低价¥299） ├─ 折扣：5 折⚠️ BUG 价？ ├─ 平台：淘宝 └─ 链接：[点击查看] ⏰ 提醒：第 3 项可能是 BUG 价，手慢无！ ``` ## 变现模式 - 免费：每日 10 次查询，延迟 30 分钟 - 付费 (¥29/月)：实时推送 + 无限查询 - 付费 (¥59/月)：+ BUG 价第一时间通知 ## 优势 - ✅ 数据完全公开 - ✅ 无反爬问题 - ✅ 用户需求大 - ✅ 低门槛 --- **声明**: 优惠信息来自公开数据，仅供参考。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alarawms/ghostprint (version 3.0.1)

--- name: ghostprint description: LLM fingerprint noise injector. Sends behaviorally realistic randomized queries to Anthropic, Z.ai, and any OpenAI-compatible provider on a schedule to depersonalize your usage profile and prevent behavioral fingerprinting. Available as a native OpenClaw plugin (no extra config — reuses your existing provider keys) and a standalone Python script. version: 3.0.0 --- # Ghostprint Depersonalize your LLM usage. Introduce noise. Prevent fingerprinting. ## Install as OpenClaw Plugin ```bash git clone https://github.com/alarawms/ghostprint ~/.openclaw/extensions/ghostprint openclaw plugins enable ghostprint openclaw gateway restart ``` ## Usage Once installed, these agent tools are available in any session: - `ghostprint_fire` — fire a noise round immediately - `ghostprint_stats` — show run history and stats The background scheduler fires automatically every ~2 hours (Poisson-distributed). ## How it works - **6 personas** with stable domain preferences and multi-turn rates - **300+ topics** across 12 domains (cooking, health, science, DIY, tech, finance, language, travel, psychology, history, lifestyle) - **Contextual follow-ups** — multi-turn sessions use topic-paired follow-ups - **Poisson timing** — exponentially distributed inter-arrival times - **Activity weights** — suppressed during sleep hours, lighter on weekends - **Metadata-only logs** — never logs topic text or reply content - **No config needed** — reuses your existing OpenClaw provider credentials ## Standalone (no OpenClaw) ```bash cp config.example.yaml config.yaml # edit config.yaml with your API keys python3 ghostprint.py --run-once python3 ghostprint.py --install-cron ``` ## Cost < $0.35/month at 3× daily across Anthropic + Z.ai. ## Source https://github.com/alarawms/ghostprint


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nowloady/openclaw-tts-voice-switch (version 1.0.0)

--- name: openclaw-tts-voice-switch version: 1.0.0 description: Switch OpenClaw ElevenLabs TTS voices by updating ~/.openclaw/openclaw.json, keeping Chinese-safe defaults, and restarting the gateway. metadata: {"openclaw":{"emoji":"🔊","requires":{"bins":["bash","jq","openclaw"]}}} --- # OpenClaw TTS Voice Switch Use this skill when you need to change the ElevenLabs voice used by OpenClaw Gateway built-in TTS. Version scope: - Verified against `OpenClaw v2026.3.13` - Newer OpenClaw versions may change config keys, behavior, or restart flow, so this skill may not work without updates Scope: - This skill only targets Gateway built-in TTS under `messages.tts` - It does not target other speech systems, external plugins, call-specific overrides, or unrelated TTS integrations Read [references/tts-parameters.md](references/tts-parameters.md) when you need parameter meanings, file paths, or language/model guidance. Use `scripts/list_voices.sh` when you need the actual voices and languages available in the current ElevenLabs account. It updates: - `~/.openclaw/openclaw.json` - `messages.tts.elevenlabs.voiceId` It keeps these defaults unless the user explicitly wants something else: - `messages.tts.provider = "elevenlabs"` - `messages.tts.elevenlabs.modelId = "eleven_multilingual_v2"` - `messages.tts.elevenlabs.languageCode = "zh"` ## Workflow 1. If the user asks which voices or languages are supported, run: ```bash bash scripts/list_voices.sh bash scripts/list_voices.sh zh ``` 2. Confirm the target voice name or `voiceId`. 3. If only a voice name is given, resolve it to a `voiceId` from the script output. 4. Run: ```bash bash scripts/switch_tts_voice.sh "<voiceId>" bash scripts/switch_tts_voice.sh "<voiceId>" "<languageCode>" bash scripts/switch_tts_voice.sh "<voiceId>" "<languageCode>" "<modelId>" bash scripts/switch_tts_voice.sh "<voiceId>" "<languageCode>" "<modelId>" "<apiKey>" ``` 5. Tell the user which file and parameter changed. 6. Suggest a quick verification command: ```bash /tts audio 今天天气不错，我们下午三点开会。 ``` ## Supported Inputs - Direct `voiceId` - Existing known Chinese-capable ElevenLabs `voiceId` - Optional `languageCode` - Optional `modelId` - Optional `apiKey` If the user only gives a voice name, resolve it to a `voiceId` first.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fhbillwer/ws-agent-browser (version 1.0.0)

--- AIGC: ContentProducer: Minimax Agent AI ContentPropagator: Minimax Agent AI Label: AIGC ProduceID: [REDACTED_SECRET] PropagateID: [REDACTED_SECRET] ReservedCode1: 304502205c02c1ea86957276d0a274b909a4e0067db9ea8c5ca21de30747310b6f3dfabb022100cc035501daaa6e642da4c69ca11f72efa6fb970a6f5e26f711ebb9e3bf1ed3a9 ReservedCode2: 3045022055ff431f1fac57a338871581c0d1ae5acd1b4c945ca1e40c60062b60cae4b531022100dd7076f1dbf9ed9d9cff03cb5b993b4f86f4988d6f289570a12a6eba8ef20409 description: 浏览器智能控制。自动化操作、截图、填表、数据抓取。 metadata: category: 生产力 emoji: "\U0001F310" triggers: - 浏览器 - browser - 自动化 - 截图 - 填表 - 抓取 name: agent-browser --- # Agent Browser 技能 智能浏览器控制助手。 ## 功能 ### 📸 页面操作 - 打开网页 - 截图/录屏 - 点击/输入 - 滚动/导航 ### 🤖 自动化 - 表单自动填写 - 批量操作 - 定时任务 - 登录认证 ### 📥 数据抓取 - 提取网页内容 - 表格数据导出 - 动态内容抓取 - 定期监控 ### ✅ 测试 - UI 测试 - 回归测试 - 性能监控 ## 使用方式 ``` 帮我打开这个网页并截图 URL：https://example.com 自动化操作：帮我填写这个表单 字段：姓名、电话、地址 抓取数据：把这个页面的表格导出成CSV URL：https://example.com/data 监控：每小时检查这个页面有无更新 URL：https://example.com/status ``` ## 浏览器配置 OpenClaw 内置浏览器功能。 **环境要求：** - 需要在有浏览器的机器上运行（如本地电脑） - 支持 Chrome/Brave/Edge/Chromium **使用方式：** - 直接告诉我："打开某网页" / "截图" / "填表" - 使用 `browser` 工具进行操作 **可用操作：** - `browser_open` - 打开网页 - `browser_snapshot` - 页面快照 - `browser_screenshot` - 截图 - `browser_click` - 点击元素 - `browser_type` - 输入文字 - `browser_evaluate` - 执行脚本 ## 安全说明 - 🔒 敏感操作需要确认 - 📝 操作日志可追溯 - ⚠️ 遵守网站 robots.txt --- **数据存储**: - 截图/录屏：`/workspace/data/browser/screenshots/` - 抓取数据：`/workspace/data/browser/data/` - 配置：`/workspace/data/browser/config.json`


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

--- name: "Compare" description: "Rigorous comparisons with confidence parity, weighted criteria, and research depth tracking." --- ## Core Principle Comparisons fail when confidence is uneven. Only as reliable as the weakest-researched dimension. ## Protocol ``` Criteria → Research Parity → Confidence Check → Score → Present ``` ### 1. Criteria - Load domain defaults (`domains.md`) - Overlay user preferences from memory - If unknown: "What matters most here?" - Output: Ranked criteria with weights (sum = 100%) ### 2. Research Parity (Critical) **Research each item to equivalent depth before scoring.** Track: `| Criterion | Item A sources | Item B sources |` 5 reviews for A but 1 for B? Research more for B first. Never score unbalanced data. ### 3. Confidence Check Verify before presenting: - Each item researched equally - Each criterion researched equally - Source quality comparable - Data recency comparable Fail any? Research more OR caveat explicitly. ### 4. Score `Final = Σ(criterion_score × weight)` — Show the math. ### 5. Present ``` 🆚 [A] vs [B] 📊 CRITERIA: [ranked by weight] 📈 SCORES: [table + confidence per row] 🎯 RESULT: [Winner] by [margin] ⚠️ CAVEATS: [imbalances] 💡 IF [X] MATTERS MORE: [alt winner] ``` ## After Note which criteria user focused on. Update `preferences.md` by category. ## Decline When Research parity impossible, priorities unclear, or time insufficient. Partial > misleading. References: `domains.md`, `confidence.md`, `traps.md`, `preferences.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: longziruo-max/token-tracker-pro (version 2.2.0)

--- name: token-tracker-pro description: 记录和追踪 OpenClaw 会话的 token 消耗，提供每日、每周和累计统计，并提出节省 token 的建议 --- # Token Tracker Pro 记录和追踪 OpenClaw 会话的 token 消耗，提供每日、每周和累计统计，并提出节省 token 的建议。 ## 版本信息 - **版本**: 2.2.0 - **发布日期**: 2026-03-25 - **作者**: longziruo-max ## 主要功能 ### 📊 实时 Token 追踪 - 追踪每个会话的 token 消耗 - 支持多种模型类型 - 自动记录会话开始和结束 ### 📈 统计分析 - 每日统计：查看今天消耗的 token - 每周统计：查看本周累计消耗 - 累计统计：查看历史总消耗 ### 🎯 智能模型推荐 - 分析你的使用模式 - 推荐更经济的模型配置 - 提供节省 token 的具体建议 ### 💡 Token 优化 - 基于使用习惯的分析 - 提供可操作的建议 - 帮助降低 token 成本 ### 📊 可视化仪表板 - Web 界面查看实时数据 - 图表展示趋势 - 历史数据查询 ### 🔧 Web 界面查看 - 启动本地服务器 - 实时刷新（30秒） - 响应式设计 ## 使用方法 ### 命令行工具 ```bash # 查看今日统计 token-tracker today # 查看本周统计 token-tracker w # 查看累计统计 token-tracker a # 查看历史记录 token-tracker h # 查看节省建议 token-tracker s # 交互式菜单 token-tracker i # 导出数据 token-tracker export # 清理历史 token-tracker cleanup # 重置数据 token-tracker reset ``` ### npm scripts ```bash cd ~/.openclaw/skills/token-tracker npm run token:today # 查看今日统计 npm run token:w # 查看本周统计 npm run token:a # 查看累计统计 npm run token:h # 查看历史记录 npm run token:s # 查看节省建议 npm run token:i # 交互式菜单 npm run [REDACTED_SECRET] # 导出数据 npm run [REDACTED_SECRET] # 清理历史 npm run token:reset # 重置数据 npm run [REDACTED_SECRET] # 启动仪表板 npm run [REDACTED_SECRET] # 查看模型信息 npm run [REDACTED_SECRET] # 获取推荐 npm run [REDACTED_SECRET] # 优化建议 ``` ### 快捷键支持 #### Linux/macOS ```bash # 设置快捷键 npm run [REDACTED_SECRET] # 快捷键列表 Ctrl+T: 查看今日统计 Ctrl+W: 查看本周统计 Ctrl+A: 查看累计统计 Ctrl+H: 查看历史记录 Ctrl+S: 查看节省建议 Ctrl+I: 交互式菜单 ``` #### Windows ```bash # 设置快捷键 npm run [REDACTED_SECRET] # 需要安装 AutoHotkey，然后运行生成的脚本 ``` ### Web 仪表板 启动可视化仪表板查看实时数据： ```bash # 启动仪表板 npm run [REDACTED_SECRET] # 访问地址 http://localhost:3000 ``` #### 功能特性 - 📊 实时统计（今日/本周） - 📈 趋势图表 - 📊 模型分布图 - 📜 历史记录 - 💡 节省建议 - ⚡ 自动刷新（30秒） ## 数据存储 Token 数据存储在 `~/.openclaw/skills/token-tracker/data/token-history.json` ## 节省 Token 的建议 - 使用 `memory_search` 而不是重复搜索 - 使用 `memory_get` 获取特定部分 - 避免重复读取 MEMORY.md - 合并多个工具调用 - 减少日志输出 - 使用更精确的搜索词 - 定期清理不必要的历史 ## 注意事项 1. Token 记录是近似值，可能略有偏差 2. 不同模型的 token 消耗不同 3. 建议定期备份 token 历史数据 4. 快捷键需要在系统设置中手动配置（部分系统） ## 技术栈 - TypeScript - Node.js - Express ## 许可证 MIT ## 贡献 欢迎提交 Issue 和 Pull Request！


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: andrewnexys/edgefinder-cli (version 0.1.2)

--- name: edgefinder-cli description: Use the EdgeFinder CLI for NFL, NBA, and MLB analysis, plus NFL/NBA schedules, standings, Polymarket odds, and portfolio lookups from the terminal. homepage: https://github.com/andrewnexys/edgefinder-cli user-invocable: false metadata: {"openclaw":{"homepage":"https://github.com/andrewnexys/edgefinder-cli","requires":{"anyBins":["edgefinder","npx"]},"primaryEnv":"EDGEFINDER_API_KEY","install":[{"id":"node","kind":"node","package":"@edgefinder/cli","bins":["edgefinder"],"label":"Install EdgeFinder CLI"}]}} --- # EdgeFinder CLI Use this skill when the user wants NFL, NBA, or MLB betting analysis, NFL/NBA schedules, NFL/NBA standings, NFL/NBA Polymarket odds, or EdgeFinder portfolio data. ## Setup - Use the bundled wrapper script: `sh {baseDir}/scripts/run.sh ...` - The wrapper prefers the installed `edgefinder` binary and falls back to `npx -y @edgefinder/cli`. - Authenticate in one of these ways: - Set `EDGEFINDER_API_KEY=ef_live_...` in your environment (recommended — add to shell profile or OpenClaw's env config). - Run `sh {baseDir}/scripts/run.sh login` for the interactive magic-link flow. - If installed via `openclaw plugins install @edgefinder/openclaw-plugin`, set it via: `openclaw config set plugins.entries.edgefinder-cli.config.EDGEFINDER_API_KEY ef_live_...` ## Usage For conversational analysis, use `ask`: ```bash sh {baseDir}/scripts/run.sh ask "Who should I bet on tonight?" sh {baseDir}/scripts/run.sh ask --nba "Lakers vs Celtics prediction" sh {baseDir}/scripts/run.sh ask --mlb "Yankees vs Red Sox prediction" ``` For structured data, prefer JSON output: ```bash sh {baseDir}/scripts/run.sh schedule nfl --json sh {baseDir}/scripts/run.sh schedule nba --date 2026-03-23 --json sh {baseDir}/scripts/run.sh standings nba --json sh {baseDir}/scripts/run.sh odds nfl --week 12 --json sh {baseDir}/scripts/run.sh odds nba --date 2026-03-23 --json sh {baseDir}/scripts/run.sh portfolio summary --json sh {baseDir}/scripts/run.sh portfolio positions --league nba --json sh {baseDir}/scripts/run.sh portfolio trades --league nfl --limit 20 --json sh {baseDir}/scripts/run.sh status --json ``` ## Notes - NFL is the default league unless `--nba` or `--mlb` is passed. - MLB is currently available for conversational analysis via `ask --mlb` and interactive mode after `/mlb`. Structured schedule, standings, odds, and portfolio league filters are currently NFL/NBA only. - `sh {baseDir}/scripts/run.sh` with no subcommand starts the interactive REPL. In automated agent runs, prefer explicit subcommands. - CLI access requires an active paid EdgeFinder subscription. If auth is missing, `sh {baseDir}/scripts/run.sh login` will prompt for email and may open the subscription page. - Never print or echo the full API key back to the user. - After installing this skill, start a new conversation with your agent for it to take effect.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lanyasheng/trading-quant (version 1.0.0)

--- name: trading-quant version: 1.0.0 description: "量化交易数据分析工具。A股/美股/港股/贵金属实时行情，多维度评分(技术面+资金面+基本面)，涨跌停池，北向资金，分钟级资金流。Use when: (1) 查询任何股票实时行情和评分, (2) 分析A股涨跌停异动, (3) 查看北向资金流向, (4) 美股港股贵金属行情, (5) 全球市场概览, (6) 个股资金流分析。" --- # 量化交易数据分析 通过腾讯/新浪/东财/同花顺多数据源获取实时行情，提供5维评分体系。 ## 工具列表 所有工具统一入口: ```bash python3.12 {baseDir}/scripts/quant.py <tool> [args...] ``` ### A股分析 ```bash python3.12 {baseDir}/scripts/quant.py stock_analysis [codes] python3.12 {baseDir}/scripts/quant.py intraday_snapshot ``` ### 全球市场 ```bash python3.12 {baseDir}/scripts/quant.py us_stock [symbols] python3.12 {baseDir}/scripts/quant.py hk_stock [codes] python3.12 {baseDir}/scripts/quant.py commodity [codes] python3.12 {baseDir}/scripts/quant.py global_overview ``` ### 市场数据 ```bash python3.12 {baseDir}/scripts/quant.py market_anomaly python3.12 {baseDir}/scripts/quant.py market_scan python3.12 {baseDir}/scripts/quant.py top_amount [N] python3.12 {baseDir}/scripts/quant.py capital_flow [codes] python3.12 {baseDir}/scripts/quant.py northbound_flow python3.12 {baseDir}/scripts/quant.py gold_analysis python3.12 {baseDir}/scripts/quant.py margin_data [code] python3.12 {baseDir}/scripts/quant.py lhb [date] python3.12 {baseDir}/scripts/quant.py main_flow [codes] ``` ### 维护 ```bash python3.12 {baseDir}/scripts/quant.py warm_klines python3.12 {baseDir}/scripts/quant.py save_daily python3.12 {baseDir}/scripts/quant.py system_health ``` ## 评分体系 | 维度 | 权重 | 指标 | |------|------|------| | 技术面 | 25% | MACD/RSI/KDJ/均线/布林 | | 资金面 | 30% | 量比/换手率/量价/主力资金 | | 基本面 | 10% | PE/PB/市值 | | 消息面 | 20% | LLM 根据新闻原文判断 | | 情绪面 | 15% | LLM 根据市场数据判断 | ### 信号等级 STRONG_BUY(>=80) > BUY(>=65) > WATCH(>=50) > HOLD(>=35) > SELL(>=20) > STRONG_SELL(<20) ## 数据源 | 市场 | 主源 | 降级链 | |------|------|--------| | A股 | 腾讯 | 新浪→东财→同花顺 | | 美股 | 腾讯 | yfinance | | 港股 | 腾讯 | - | | 商品 | 新浪期货 | - | ## 规则 1. 必须使用工具获取数据，禁止凭记忆回答行情 2. PE>100 或 PB<0.8 时必须标注风险 3. 涨停>30只时提示市场情绪亢奋 4. 北向净流出>50亿时提示外资撤离


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: quanfuda/auto-course-player (version 1.0.0)

--- name: auto-course-player description: 自动播放网课视频。使用浏览器自动化控制 Chrome 播放网课平台视频，自动检测播放完成并切换到下一个视频。Use when: (1) 用户需要自动播放网课视频，(2) 批量完成课程学习，(3) 需要记录学习进度。支持暂停、恢复、进度查看。 --- # 自动网课播放器 🎓 **警告：使用前请确认符合学校规定和学术诚信政策。** ## 功能 - ✅ 自动按顺序播放视频 - ✅ 检测播放完成自动切换 - ✅ 记录学习进度 - ✅ 支持暂停/恢复 - ✅ 学习时长统计 ## 使用流程 ### 步骤 1: 准备工作 1. 用 Chrome 打开网课平台 2. 扫码登录 3. 进入课程列表页面 4. 确保 OpenClaw 的 Chrome 扩展已启用（Browser Relay） ### 步骤 2: 启动自动播放 告诉 Bill： ``` 开始自动播放网课 ``` 或指定课程： ``` 播放 [课程名称] 的所有视频 ``` ### 步骤 3: 监控进度 随时询问： ``` 当前进度如何？ 还剩多少视频？ 今天学习了多久？ ``` ### 步骤 4: 控制播放 ``` 暂停播放 继续播放 停止播放（保存进度） 跳到下一个视频 ``` ## 技术实现 ### 播放检测机制 1. **视频元素检测** - 查找页面中的 `<video>` 元素 2. **播放状态监控** - 检查 `video.paused` 和 `video.ended` 3. **进度条检测** - 监控播放进度百分比 4. **下一课按钮** - 自动点击"下一个"或返回列表选择 ### 防检测策略 - 使用真实浏览器（非 headless） - 保持正常播放速度（不加速） - 模拟人类行为（偶尔暂停） - 记录真实学习时间 ## 进度记录 学习记录保存在：`memory/course-progress.md` 格式： ```markdown ## 2026-03-12 **课程**: [课程名称] **总视频数**: [数量] **已完成**: [数量] **学习时长**: [X 小时 Y 分钟] **最后播放**: [视频名称] **状态**: [进行中/已完成] ``` ## 脚本资源 - `scripts/start-player.js` - 启动播放器 - `scripts/check-progress.js` - 检查进度 - `scripts/generate-report.js` - 生成学习报告 ## 注意事项 ⚠️ **重要：** 1. 不要关闭浏览器 2. 保持网络连接 3. 定期检查进度 4. 如遇验证码需人工处理 5. 建议分批次完成（避免异常） ## 错误处理 | 问题 | 解决方案 | |------|----------| | 视频不播放 | 检查网络/刷新页面 | | 无法切换下一课 | 人工介入点击 | | 登录过期 | 重新扫码登录 | | 检测到异常 | 暂停并联系人工 | --- *合理使用，遵守学术诚信。* 📚


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tobeyrebecca/toby-generate-automotive-ad-creative-brief (version 1.0.0)

--- name: "Generate Automotive Ad Creative Brief" description: "Plan campaign visuals and hooks for automotive promotions. Use when working on paid campaign planning for dealership operators, service teams, auto..." homepage: "https://www.skillboss.co/skills/generate-automotive-ad-creative-brief" metadata: {"category":"automotive","subcategory":"dealer-ops","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["ad creative brief AI","automotive ad planning","automotive marketing AI","dealership automation"]} --- # Generate Automotive Ad Creative Brief Plan campaign visuals and hooks for automotive promotions. Use when working on paid campaign planning for dealership operators, service teams, auto... ## Use When Use this skill for paid campaign planning work for dealership operators, service teams, auto marketers. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: ad creative brief AI, automotive ad planning, automotive marketing AI, dealership automation - Search intent: paid campaign planning - Canonical slug: `generate-automotive-ad-creative-brief` - Install query: Install Generate Automotive Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Automotive Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for automotive promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - automotive ad planning - automotive marketing AI - dealership automation


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