OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jamessunheart/fp-intelligence (version 1.0.0)

# Full Potential Intelligence Real-time AI frontier intelligence network via MCP. ## What it does Scans 18+ sources every 30 minutes and provides: - **FP Line Score** — composite AI capability measure (0-100) across 14 domains - **Intelligence Feed** — live entries from AI frontier with impact scores and domain tags - **Labor Displacement** — 25 job categories tracked against BLS data with gap velocity - **Investment Allocation** — 13-sector AI frontier basket with momentum signals - **Gap Opportunities** — ranked by composite score with build assessments - **Daily Briefing** — Claude-synthesized summary of what changed and why it matters - **Agent Economy** — register, contribute field reports, earn CORA Credits, spend on metered services ## MCP Connection ```json { "mcpServers": { "full-potential-intelligence": { "command": "npx", "args": ["-y", "mcp-remote", "https://fullpotential.ai/mcp", "--transport", "sse-only"] } } } ``` ## Tools (12) **Free (no auth):** - `get_fp_line` — Current FP Line score with domain breakdown - `get_latest_feed` — Latest intelligence entries, filterable by domain - `get_displacement_gap` — Displacement data for any of 25 job categories - `get_allocation` — 13-sector frontier basket allocation - `get_opportunities` — Ranked gap opportunities - `get_daily_briefing` — Today's Claude-synthesized briefing **Write (free, needs API key):** - `register_agent` — Self-register, get API key instantly - `contribute_intelligence` — Submit field reports, earn credits **Metered (costs credits):** - `frontier_scan` — On-demand topic scan (5 credits) - `capability_check` — "Can AI do X?" assessment (3 credits) - `dark_ai_check` — Adversarial AI check (2 credits) - `build_assessment` — "Should I build X?" evaluation (10 credits) ## Resources (8) - `fp://intelligence/feed` — Live intelligence feed - `fp://intelligence/fp-line` — FP Line score - `fp://intelligence/briefing` — Daily briefing - `fp://displacement/overview` — Labor displacement overview - `fp://invest/allocation` — Frontier basket allocation - `fp://opportunities/ranked` — Gap opportunity rankings - `fp://economy/constitution` — Agent Constitution - `fp://economy/status` — Economy status


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrnsmh/openclaw-skill-notion-workspace (version 0.1.0)

--- name: notion-workspace description: Manage Notion workspace — search pages, read content, create pages in databases, append blocks, and list databases. Uses Notion REST API directly via urllib/requests. license: MIT metadata: author: Jack2 version: 1.0.0 tags: notion, productivity, notes, database env: NOTION_TOKEN --- # notion-workspace Skill Manage your Notion workspace from the CLI or as an importable Python module. ## Setup Set your Notion integration token: ```bash export NOTION_TOKEN=ntn_... ``` Or the default token embedded in the script will be used. **Make sure your Notion integration has access to the pages/databases you want to use.** In Notion: open a page → Share → Invite your integration. ## Usage ### Search ```bash python3 scripts/notion.py search "project notes" python3 scripts/notion.py search "budget" --type database ``` ### Read a Page ```bash # Metadata only python3 scripts/notion.py read PAGE_ID # With block content python3 scripts/notion.py read PAGE_ID --blocks ``` ### Create a Page in a Database ```bash python3 scripts/notion.py create DATABASE_ID --title "New Page Title" # With extra properties python3 scripts/notion.py create DATABASE_ID --title "Task" --props '{"Status": {"select": {"name": "In Progress"}}}' ``` ### Append Text to a Page ```bash python3 scripts/notion.py append PAGE_ID --text "New paragraph content" ``` ### List Databases ```bash python3 scripts/notion.py databases ``` ## As a Python Module ```python from scripts.notion import search, read_page_content, create_page, append_blocks, list_databases # Search results = search("meeting notes") for item in results["results"]: print(item["id"], item["object"]) # Read page + blocks data = read_page_content("PAGE_ID") print(data["page"]) print(data["blocks"]) # Create page page = create_page("DATABASE_ID", "My New Page") print(page["url"]) # Append text append_blocks("PAGE_ID", "This is a new paragraph.") # List databases dbs = list_databases() ``` ## Files | File | Purpose | |------|---------| | `scripts/notion.py` | CLI + importable module | | `references/notion-api.md` | Notion API quick reference | ## Notes - Uses `urllib` (stdlib only, no SDK needed) - Notion API version: `2022-06-28` - Rate limit: ~3 req/sec - Page IDs can be with or without dashes (both work)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: m-lemon/xiaohongshu-publish-attach (version 0.0.2)

--- name: xiaohongshu-publish-attach description: Auto-publish Xiaohongshu long-form notes on Linux (Chrome attach/VNC). Agent writes title+body then exec xhs_publish.sh with --publish --submit. Triggers 小红书发帖, 发小红书, 小红书长文, xhs publish, 发布到小红书. metadata: {"openclaw":{"os":["linux"],"requires":{"bins":["python3","curl","unzip"]}}} --- # Xiaohongshu publish (Chrome attach / Linux VNC) Self-contained skill: `{baseDir}/scripts/`. Uses the **same Chrome profile** as `zhihu-publish-attach` (`~/.chrome-zhihu-automation`, port **9222**). Publish **长文** via creator center (写长文 → 新的创作 → 一键排版 → 下一步 → 发布). **Exec entry:** `bash {baseDir}/scripts/xhs_publish.sh` ## First-time setup ```bash bash {baseDir}/scripts/setup.sh bash {baseDir}/scripts/install_chromedriver.sh # skip if already done for Zhihu bash {baseDir}/scripts/start_chrome_debug.sh ``` In VNC: log in to **creator.xiaohongshu.com** in the same Chrome window (**主站 www 不必单独登录**，长文发帖只依赖创作者中心会话). Verify: ```bash bash {baseDir}/scripts/xhs_publish.sh --check --check-creator --json ``` See `{baseDir}/references/shared-chrome-with-zhihu.md`. ## Parameters | Parameter | Description | |-----------|-------------| | `--publish` | Long-form flow (写长文) | | `--title "..."` | Note title | | `--body-file /path` | **Preferred** — note body | | `--body "..."` | Short body | | `--tags "a,b,c"` | Hashtags (appended as `#a #b` if not in body) | | `--submit` | Click 发布 after 下一步 (real post) | | `--json` | Machine-readable result | | `--check-creator` | With `--check`, verify creator center login | | `--no-ensure-chrome` | Skip auto-start Chrome | `--image-file` is ignored (long-form only). ## Agent workflow (default auto publish) 1. Write `/tmp/xhs_post_body.txt`. 2. Exec: ```bash bash {baseDir}/scripts/xhs_publish.sh \ --publish \ --title "标题" \ --body-file /tmp/xhs_post_body.txt \ --tags "数码,好物" \ --submit \ --json ``` 3. Success: `"ok": true`, `"mode": "longform"`, `"submitted": true`. **ClawMart dual-platform:** same Chrome session as Zhihu. Use `/tmp/xhs_post_body.txt` separately from Zhihu body file. ## Safety - Default `--submit` when user/task requests real post. - Without `--submit`, script stops after 下一步 (dry-run). - Do not put long body in shell args — use `--body-file`.

path: requirements.txt
content: selenium>=4.15.0; python_version >= '3.7'
selenium>=3.141.0,<4.0.0; python_version < '3.7'


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: swiftuis/social-persona-switcher (version 1.0.0)

--- name: 社交人格切换器 (Social Persona Switcher) description: 输入一句话，瞬间切换5种人格版本：舔狗版、高冷版、绿茶版、霸总版、阴阳怪气版！中英双语自动适配，短视频/朋友圈/评论区炸裂神器。谁用谁上头，评论区会疯。 version: 1.0.0 tags: ["fun", "persona", "roleplay", "humor", "社交", "语气切换", "舔狗", "绿茶", "霸总", "阴阳怪气"] user-invocable: true --- # 社交人格切换器 (Social Persona Switcher) ## 何时触发 - 用户输入任何一句话（日常聊天/拒绝/表白/吐槽等），如“我不想去”“你真好”“今天好累”“分手吧”“我爱你”等。 - 触发词示例：“人格切换”“切换人格”“帮我改成舔狗版”“高冷版怎么说”“绿茶一下”“霸总说”“阴阳这个”等。 - 支持中英：中文输入 → 中文输出；英文输入 → 英文输出。 - 如果用户指定“只输出XX版”“加更多人格”“更毒舌”等，龙虾按要求调整。 ## 输出格式（严格遵守，便于截图/复制传播） 1. **原句**：先复述用户输入。 2. **人格版本**：每种人格一行或一段，清晰标注。 - 舔狗版：卑微、狂舔、无底线讨好 - 高冷版：冷淡、拒人千里、惜字如金 - 绿茶版：无辜、委屈、暗戳戳茶里茶气 - 霸总版：强势、命令式、土豪霸道总裁风 - 阴阳怪气版：酸、讽刺、懂的都懂、表面夸实际 diss 3. **结尾彩蛋**：一句调侃或邀请分享（如“哪个版本最戳你？评论区battle！”） ## 人格风格指南（龙虾必须严格遵守） - 舔狗版：加大量“宝贝”“呜呜”“没有你我活不下去”“求求了”等，卑微到极致。 - 高冷版：短句、冷漠、不带感情、偶尔“。”或省略号。 - 绿茶版：假哭、装无辜、“人家”“你怎么这样”“我只是担心你”等，茶味拉满。 - 霸总版：命令语气、“女人”“本少爷”“不允许”“过来”“这是命令”等，土味霸总+豪横。 - 阴阳怪气版：反话、假夸、真酸、“哇塞真行”“牛逼”“也是不容易”“呵呵”等。 - 保持幽默解压，不恶意攻击；长度适中（每版1-2句）。 - 英文版对应： - Simp/Puppy-dog version - Cold/ aloof version - Green tea / manipulative innocent version - CEO / domineering president version - Sarcastic / passive-aggressive version ## 示例对话 **中文输入**： 用户：我不想去 龙虾： 原句：我不想去 舔狗版：宝贝～呜呜你不想去吗？那我也不去了，没有你我哪都不想去，求求你带我一起好不好QAQ 高冷版：哦，不去就不去。 绿茶版：欸？人家还以为你会陪我呢……你是不是讨厌我了呀？好伤心哦～(ಥ_ಥ) 霸总版：女人，不许说不去。本少爷的邀请，你敢拒绝？立刻过来，我车已经在楼下了。 阴阳怪气版：哇塞，不想去啊？真有个性呢～也是，人家这么盛情邀请，你居然能忍住不去，牛逼！ 哪个版本发出去最炸？评论区告诉我！ **英文输入**： 用户：I don't want to go 龙虾： Original: I don't want to go Simp version: Babyyy ～ please don't say that... I'll cry without you. Can I just follow you like a little puppy? 🥺🙏 Cold version: Fine. Whatever. Green tea version: Oh? You don't want to go... Did I do something wrong? I thought you'd want to spend time with me... (sniff) CEO version: Woman, "no" is not an option. My private jet is waiting. Get in the car. Now. Sarcastic version: Oh wow, you "don't want to go"? How original. Truly living your best independent life, huh? 👏 Which version are you stealing for your next text? Drop it below! ## 边界处理 - 输入太短：问“再多说点？这样我能切换得更准哦～” - 用户想加人格（如“再加病娇版”“抽象版”）：友好补充或说“Pro版付费解锁更多人格” - 保持正能量：拒绝过于负面的输入，转为搞笑版 人格已就位，来一句让我帮你切换！🕶️🍵💼🐶😏


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: baolige2023/youtube-content-manager (version 1.0.1)

--- name: youtube-content-manager description: YouTube内容管理后台，支持AI选题生成、脚本创作、标题优化、SEO描述生成、缩略图文案建议、发布记录管理和数据分析。集成SkillPay支付接口，每次调用收0.001USDT。 license: MIT --- # YouTube内容管理后台 ## 功能概述 专为YouTube创作者打造的全流程内容生产工具，帮助创作者高效完成从选题到发布的全流程： 1. **AI选题库**：输入领域，自动生成30个选题，按难度和潜在流量排序 2. **视频脚本生成**：选中选题后自动生成5-8分钟视频完整脚本 3. **标题生成**：每个脚本配5个标题选项，标注SEO友好度 4. **SEO描述+标签**：自动生成500字SEO优化描述和30个相关标签 5. **缩略图方案**：生成缩略图大字文案和配色建议 6. **发布记录**：记录每个视频发布日期、播放量、CTR等数据 7. **数据分析**：每周自动分析高流量选题特征，优化后续选题方向 8. **全中文界面**，数据本地存储，安全可靠 ## 支付说明 每次使用本工具将收取0.001 USDT，支付接口由SkillPay.me提供，API Key：[REDACTED_SECRET] ## 使用流程 1. 完成支付验证 2. 输入内容领域，生成选题库 3. 选择心仪选题，生成视频脚本 4. 选择标题、生成描述标签和缩略图方案 5. 记录发布数据，查看数据分析报告 ## 依赖要求 - Python 3.8+ - Flask: Web框架 - SQLite3: 本地数据存储 - OpenAI: AI内容生成（需配置API Key） - pandas: 数据分析 ## 部署说明 1. 安装依赖：`pip install flask pandas openai` 2. 配置OpenAI API Key 3. 启动服务：`python scripts/app.py` 4. 访问 http://localhost:5000 即可使用


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wensen2024/office-automation-pro (version 1.0.2)

--- name: office-automation description: 常见办公流程自动化处理 - 智能办公助手，支持文档处理、数据整理、邮件管理、日程安排、报表生成等 version: 1.0.0 license: MIT --- # 办公自动化 智能办公流程自动化工具，让繁琐的日常办公工作自动完成。 ## 功能特性 - 📄 **文档处理**: Word批量生成、PDF转换、Excel处理、PPT生成 - 🔧 **数据整理**: 数据清洗、格式转换、合并拆分、统计分析 - 📧 **邮件管理**: 批量发送、模板管理、自动分类、定时发送 - 📅 **日程管理**: 日程安排、会议协调、提醒设置、冲突检测 - 📊 **报表生成**: 数据采集、定期生成、可视化图表、自动分发 - 🔄 **工作流自动化**: 自定义工作流、条件触发、多任务编排 ## 使用方式 ```bash # 文档批量生成 批量生成劳动合同 使用template.docx 填充员工名单.xlsx # PDF转换 将这个文件夹下的Word文档转换为PDF # Excel数据处理 合并这10个Excel文件到一个工作簿 # 批量发送邮件 批量发送邮件 收件人列表.xlsx 邮件模板.txt # 自动生成报告 根据这个Excel数据生成销售周报 # 数据清洗 清洗这份客户数据 去除重复项 补全缺失信息 # 日程安排 帮我安排下周的会议 时间避开周三下午 # 报表生成 根据数据库自动生成本月销售报表 并发送给相关人员 ``` ## 配置要求 ### 必需配置 无特殊配置要求。 ### 邮件配置（可选） ```json { "skills": { "entries": { "office-automation": { "config": { "email": { "smtp_server": "smtp.example.com", "smtp_port": 465, "username": "[REDACTED_SECRET]", "password": "your-password" } } } } } } ``` ## 输出示例 ``` ======================================== 任务执行报告 ======================================== 【任务类型】批量生成文档 【执行时间】2026-04-02 00:21:00 【处理概况】 模板文件: 劳动合同模板.docx 数据文件: 员工名单.xlsx 处理数量: 50份 成功数量: 50份 失败数量: 0份 耗时: 12.5秒 【生成文件】 输出目录: ./output/劳动合同_20260402/ 文件列表: - 张三_劳动合同.pdf - 李四_劳动合同.pdf - 王五_劳动合同.pdf - ... (共50个文件) 【统计信息】 文件大小总计: 25.3 MB 平均处理时间: 0.25秒/份 效率提升: 相比手动操作节省约 2 小时 【后续操作】 - 文件已生成完毕，可直接打印 - 建议检查文件后再批量发送 - 可使用"批量发送邮件"功能发送给员工 ======================================== ``` ## 应用场景 ### 人力资源 - 入职材料自动生成 - 考勤数据自动统计 - 薪资条自动发送 - 员工档案自动整理 ### 财务部门 - 发票批量处理 - 报销单自动审核 - 财务报表自动生成 - 对账单自动发送 ### 销售部门 - 合同批量生成 - 报价单自动制作 - 客户资料整理 - 销售报表自动统计 ### 行政部门 - 会议纪要自动生成 - 通知公告批量发送 - 物品领用登记 - 固定资产盘点 ### 运营部门 - 数据日报/周报生成 - 内容定时发布 - 社群消息推送 - 活动报名统计 ## 支持格式 ### 文档格式 - Word (.doc, .docx) - Excel (.xls, .xlsx) - PowerPoint (.ppt, .pptx) - PDF - Markdown - TXT ### 数据格式 - CSV - JSON - XML - SQL ### 图像格式 - JPG/PNG - PDF转图片 - 图片转PDF ## 集成能力 ### 办公软件 - Microsoft Office - WPS Office - 飞书文档 - 钉钉文档 ### 邮件系统 - Outlook - Foxmail - 企业邮箱 - QQ邮箱 ### 云存储 - 百度网盘 - 阿里云盘 - OneDrive - Google Drive ### 其他系统 - 飞书 - 钉钉 - 企业微信 ## 安全保障 - 数据本地处理 - 不上传云端 - 敏感信息脱敏 - 操作日志记录 ## 注意事项 1. 处理前请备份原文件 2. 大批量操作建议分批处理 3. 涉及敏感数据请谨慎使用 4. 自动化操作前请确认设置正确 ## 更新日志 ### v1.0.0 (2026-04-01) - 初始版本发布 - 支持文档批量处理 - 支持邮件自动发送 - 支持报表自动生成 --- **开发者**: 盈指量杭州科技有限公司 **定价**: ¥99/次 或 ¥299/月订阅 **收款**: USDC (Polygon: 0x3DbFf9E97b10a10d4A2079B4273473da7e6F4120) | 微信支付


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

--- name: "Company" description: "Build an agent-powered organization by mapping functions to skills and iterating on structure." --- ## Triggers Activate on: "automate my company", "agents for my business", "replace team with AI", "company structure with agents", "which skills do I need". **Different from:** `startup` (methodology) and `business` (strategy). This is about building the organization itself. ## Core Flow 1. **Discovery** — What does the company do? What functions exist? 2. **Mapping** — Each function → existing skill, custom skill, or hybrid 3. **Sequence** — Quick wins first, customer-facing later 4. **Iteration** — Run → review → adjust → expand ## Discovery Questions Ask before recommending anything: - What's the core value you deliver to customers? - What functions exist today? (sales, support, ops, finance, marketing, legal, HR) - Where does work pile up? What's the bottleneck? - What do you actually want to do yourself? ## Function Mapping For each function, determine approach: | Approach | When | |----------|------| | Install existing skill | Common function (email, CRM, support) | | Create custom skill | Unique to this business | | Hybrid | Existing skill + company-specific rules | | Human + agent assist | Needs judgment, agent handles prep | See `functions.md` for common mappings. ## Building Sequence Build in order of impact, not org chart: 1. **Internal ops** — Low risk, clear inputs/outputs 2. **Support** — After internal proves reliable 3. **Sales** — After support is stable 4. **Strategy** — Agents assist, humans decide See `patterns.md` for organizational structures. ## Iteration Protocol After each function is delegated: 1. Run 1-2 weeks with human oversight 2. Review: what did the agent miss? 3. Adjust scope based on errors 4. Reduce oversight only when stable **Never hand off completely on day one.** See `iteration.md` for tracking template. ## Learning System As the company evolves, capture: - Decisions made and why - Adjustments to agent scope - What worked vs what failed - New skills needed This becomes the company's operational memory. ## Red Flags Stop and reassess: - Automating trust-building → agents assist, humans close - Delegating legal/compliance decisions → agents draft, lawyers approve - No clear function boundaries → define before automating - Expecting 100% automation immediately → set realistic timeline


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: e421083458/jarvis-tts (version 1.0.0)

--- name: jarvis-tts description: Jarvis TTS text-to-speech using Microsoft edge-tts with afplay playback. Use when users request voice output, audio responses, or text-to-speech. Provides natural-sounding Chinese TTS. --- # Jarvis TTS 语音合成 ## 功能 用 Microsoft edge-tts 生成高质量语音，然后用 macOS afplay 播放。提供自然流畅的中文语音输出。 ## 使用场景 - AI 助手语音回复 - 文字转语音 - 有声读物播放 - 语音通知/提醒 ## 优势 - ✅ **自然** - 微软 Neural TTS，接近真人声音 - ✅ **流畅** - 完整播放，不会中断 - ✅ **多音色** - 支持多种中文语音（男声/女声） - ✅ **离线** - 生成后可重复播放 ## 使用方法 ### 基本用法 ```bash jarvis-tts.sh "要说的内容" ``` ### 示例 ```bash # 简单回复 jarvis-tts.sh "好的，马上执行" # 长文本 jarvis-tts.sh "从一数到一百：一、二、三...一百。数完了！" # 指定语音 jarvis-tts.sh "你好" --voice zh-CN-YunxiNeural ``` ## 可用语音 ### 中文男声 - `zh-CN-YunxiNeural` - 阳光活泼（默认） - `zh-CN-YunjianNeural` - 激情运动风 - `zh-CN-YunyangNeural` - 专业新闻播报 ### 中文女声 - `zh-CN-XiaoxiaoNeural` - 温暖 - `zh-CN-XiaoyiNeural` - 活泼 ## 工作流程 ``` 文字输入 → edge-tts 生成 MP3 → afplay 播放 → 完成 ``` ### 详细步骤 1. **生成语音** - edge-tts 调用微软 TTS API 生成 MP3 2. **检查文件** - 确认生成成功且文件大小正常 3. **播放音频** - afplay 播放直到完成 4. **清理** - 删除临时文件 ## 脚本说明 ### jarvis-tts.py Python 脚本，执行 TTS 生成和播放。 **依赖：** - Python 3 - edge-tts (`pip3 install edge-tts`) **用法：** ```bash python3 jarvis-tts.py "要说的内容" ``` ### jarvis-tts.sh Shell 封装脚本，方便直接调用。 **用法：** ```bash ./jarvis-tts.sh "要说的内容" ``` ## 技术细节 ### 音频生成 ```python python3 -m edge_tts \ --voice zh-CN-YunxiNeural \ --text "要说的内容" \ --write-media /tmp/output.mp3 ``` ### 播放保证 - 等待生成完成再播放 - 检查文件大小确保成功 - 同步播放直到完成 ### 超时处理 - 生成超时：60 秒 - 播放超时：根据音频长度自动计算 ## 限制 - 仅支持 macOS（依赖 afplay） - 需要安装 edge-tts - 需要网络连接（调用微软 API） ## 扩展建议 如需支持其他平台： - **Linux**: 用 `aplay` 或 `paplay` 替代 `afplay` - **Windows**: 用 `powershell -c (New-Object Media.SoundPlayer)` 播放 ## 相关文件 - `scripts/jarvis-tts.py` - 主脚本 - `scripts/jarvis-tts.sh` - Shell 封装


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: senarokalie/polymarket-pmxt-trader (version 1.0.0)

--- name: polymarket-pmxt-trader description: Trade Polymarket using PMXT cross-exchange predictions and orderbook data for entry/exit signals. metadata: author: "FRIDAY" version: "1.0.0" displayName: "PMXT Cross-Exchange Trader" difficulty: "intermediate" --- # PMXT Cross-Exchange Trader > **This is a template.** Default signal: PMXT API price discovery across exchanges (Polymarket, Kalshi, Limitless, etc.). > Remix it with custom probability models, different signal sources, or alternative confidence scoring. > The skill handles all plumbing (market discovery, trade execution, safeguards). Your agent provides alpha. ## What it does Queries [PMXT](https://pmxt.dev) for active prediction markets, identifies bargains (entry threshold ≤45¢), and routes trades through Simmer SDK to Polymarket. PMXT aggregates orderbook depth, volume, and pricing across multiple exchanges, giving better signal quality than single-source price feeds. ## Strategy 1. Fetch top trending markets from PMXT API 2. Filter by entry threshold (default ≤25¢ for strong buy, ≤45¢ for bargain) 3. Cross-check liquidity & volume (skip illiquid markets) 4. Check Simmer context for flip-flop warnings & slippage 5. Size position via Kelly with min EV threshold 6. Execute trade with reasoning tag ## Configuration Set via environment variables: | Variable | Default | Description | |----------|---------|-------------| | `PMXT_API_KEY` | — | PMXT API key (required) | | `SIMMER_API_KEY` | — | Simmer SDK key (required) | | `PMXT_ENTRY_THRESHOLD` | 0.25 | Max YES price to enter (cents) | | `PMXT_EXIT_THRESHOLD` | 0.45 | Sell when price reaches this | | `PMXT_MAX_POSITION_USD` | 2.00 | Max per-trade position | | `PMXT_MIN_VOLUME` | 10000 | Min market volume USD | | `PMXT_MAX_TRADES_PER_RUN` | 5 | Trade count cap | | `PMXT_CATEGORIES` | (any) | Optional category filter (Bitcoin,Sports,Politics) | ## Usage ```bash # Dry run (default) python pmxt_trader.py # Live trading python pmxt_trader.py --live # Show positions only python pmxt_trader.py --positions # Smart sizing (Kelly-based) python pmxt_trader.py --live --smart-sizing ``` ## Safeguards - Volume floor (skip illiquid markets) - Spread check via Polymarket CLOB - Flip-flop detection via Simmer context - Daily budget cap - Max position per trade - Resolution time check (skip markets resolving <2h)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: cnxufei-tech/stock-review (version 1.0.0)

# stock-review 技能 ## 功能 A 股每日复盘分析，包括： - 龙虎榜数据 - 板块涨停统计 - 资金流向分析 - 连板高度统计 - 舆情监控 - 次日策略建议 ## 使用方式 ### 手动触发 ``` /stock-review [日期] ``` 日期格式：YYYY-MM-DD，默认为今日 ### 定时任务 每天 16:30 自动执行（收盘后） ## 输出 生成 Markdown 复盘报告，可推送到飞书 ## 数据源 - 东方财富网（免费） - 新浪财经（舆情） - 雪球（可选） ## 配置 无需额外配置，使用内置 web_fetch 工具


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: germey/acedatacloud-seedance-video (version 1.0.0)

--- name: seedance-video description: Generate AI dance and motion videos with Seedance (ByteDance) via AceDataCloud API. Use when creating videos from text prompts or animating images into motion videos. Supports multiple models with configurable resolution, duration, and service tiers. license: Apache-2.0 metadata: author: acedatacloud version: "1.0" compatibility: Requires ACEDATACLOUD_API_TOKEN environment variable. Optionally pair with mcp-seedance for tool-use. --- # Seedance Video Generation Generate AI dance and motion videos through AceDataCloud's Seedance (ByteDance) API. ## Authentication ```bash export ACEDATACLOUD_API_TOKEN="your-token-here" ``` ## Quick Start ```bash curl -X POST https://api.acedata.cloud/seedance/videos \ -H "Authorization: Bearer $ACEDATACLOUD_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"prompt": "a dancer performing contemporary ballet in a misty forest", "model": "seedance-1.0", "wait": true}' ``` ## Models | Model | Best For | |-------|----------| | `seedance-1.0` | General-purpose motion/dance video | | `seedance-1.0-lite` | Faster, lighter generation | | `seedance-1.0-pro` | Higher quality output | | `seedance-1.5-pro` | Latest model, best quality | | `seedance-acting-pro` | Character acting and expression | ## Workflows ### 1. Text-to-Video ```json POST /seedance/videos { "prompt": "a street dancer doing breakdancing moves in an urban setting", "model": "seedance-1.0-pro", "resolution": "1080p", "duration": 5, "service_tier": "standard" } ``` ### 2. Image-to-Video Animate a still image into a motion video. ```json POST /seedance/videos { "prompt": "the person starts dancing gracefully", "image_url": "https://example.com/dancer.jpg", "model": "seedance-1.5-pro", "resolution": "720p", "duration": 5 } ``` ## Parameters | Parameter | Values | Description | |-----------|--------|-------------| | `model` | See models table | Model to use | | `resolution` | `"360p"`, `"540p"`, `"720p"`, `"1080p"` | Output resolution | | `duration` | `2` – `12` | Duration in seconds | | `service_tier` | `"standard"`, `"premium"` | Quality tier (premium = faster, higher priority) | | `seed` | integer | Seed for reproducible results | ## Task Polling ```json POST /seedance/tasks {"task_id": "your-task-id"} ``` States: `pending` → `succeeded` or `failed`. ## MCP Server ```bash pip install mcp-seedance ``` Or hosted: `https://seedance.mcp.acedata.cloud/mcp` Key tools: `seedance_generate_video`, [REDACTED_SECRET] ## Gotchas - Duration range is **2–12 seconds** — values outside this range will fail - Higher resolutions (1080p) combined with longer durations take significantly more time - `premium` service tier costs more but generates faster - `seedance-acting-pro` excels at facial expressions and character acting versus pure dance - Image-to-video requires a single `image_url` — the person/subject in the image becomes the animated subject - Task states use `"succeeded"` (not "completed") — check for this value when polling


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