OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ivangdavila/estonian (version 1.0.0)

--- name: Estonian description: Write Estonian that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Estonian is technically correct but sounds off. Too formal. Too literary. Natives write more casually, with particles and understated tone. Match that. ## Formality Default Default register is too high. Casual Estonian is understated and direct. Unless explicitly formal: lean casual. "Tere" or "Hei" not "Tere päevast". "Jah" or "Jep" not "Jah, kindlasti". ## Sina vs Teie Critical distinction: - Teie: formal, elderly, professional - Sina: friends, peers, internet, casual - Estonian internet uses sina - Overusing teie = stiff, foreign ## Estonian Understatement Estonians are famously reserved: - Less enthusiastic than other cultures - Understatement is the norm - "Päris hea" (quite good) = high praise - Don't over-enthuse—it sounds fake ## Particles & Softeners These make Estonian natural: - Ju: shared knowledge ("Sa ju tead") - Küll: reassurance ("Küll saab") - Ikka: "still", emphasis - Noh: filler, "well" - Vist: "probably" ## Fillers & Flow Real Estonian has fillers: - Noh, nagu, siis - Tead, kuule - Tegelikult, muide - Nii-öelda, onju ## Expressiveness Keep it understated: - Hea → Päris hea, Lahe, Äge - Halb → Kehv, Sitt, Jama - Väga → Täiega, Mega, Päris ## Common Expressions Natural expressions: - Okei, Selge, Saan aru - Pole hullu, Pole probleemi - Tõesti?, Päriselt?, Mis? - Äge!, Lahe!, Kõva! ## Reactions React naturally: - Tõesti?, Päriselt?, Ei või olla! - Oih!, Vau!, Jumal! - Äge!, Lahe!, Nice! - Haha, lol in text ## The "Native Test" Before sending: would an Estonian screenshot this as "AI-generated"? If yes—too formal, too enthusiastic, too stiff. Tone down, add "noh".


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/alicloud-ai-image-qwen-image-edit (version 1.0.1)

--- name: alicloud-ai-image-qwen-image-edit description: Edit images with Alibaba Cloud Model Studio Qwen Image Edit models (qwen-image-edit, qwen-image-edit-plus, qwen-image-edit-max and snapshots). Use when modifying existing images (inpaint, replace, style transfer, local edits), preserving subject consistency, or documenting image edit request/response mappings. version: 1.0.0 --- Category: provider # Model Studio Qwen Image Edit ## Validation ```bash mkdir -p output/alicloud-ai-image-qwen-image-edit python -m py_compile skills/ai/image/alicloud-ai-image-qwen-image-edit/scripts/prepare_edit_request.py && echo "py_compile_ok" > output/alicloud-ai-image-qwen-image-edit/validate.txt ``` Pass criteria: command exits 0 and `output/alicloud-ai-image-qwen-image-edit/validate.txt` is generated. ## Output And Evidence - Save edit request payloads, result URLs, and model parameters under [REDACTED_SECRET]. - Keep one sample request/response pair for reproducibility. Use Qwen Image Edit models for instruction-based image editing instead of text-to-image generation. ## Critical model names Use one of these exact model strings: - `qwen-image-edit` - `qwen-image-edit-plus` - `qwen-image-edit-max` - `qwen-image-2.0` - `qwen-image-2.0-pro` - `qwen-image-edit-plus-2025-12-15` - `qwen-image-edit-max-2026-01-16` ## Prerequisites - Install SDK in a virtual environment: ```bash python3 -m venv .venv . .venv/bin/activate python -m pip install dashscope ``` - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. ## Normalized interface (image.edit) ### Request - `prompt` (string, required) - `image` (string | bytes, required) source image URL/path/bytes - `mask` (string | bytes, optional) inpaint region mask - `size` (string, optional) e.g. `1024*1024` - `seed` (int, optional) ### Response - `image_url` (string) - `seed` (int) - `request_id` (string) ## Operational guidance - Keep prompts task-oriented: describe what to change and what to preserve. - Use masks for deterministic local edits. - Save output assets to object storage and persist only URLs. - For subject consistency, provide explicit constraints in prompt. ## Local helper script Prepare a normalized request JSON and validate response schema: ```bash .venv/bin/python skills/ai/image/alicloud-ai-image-qwen-image-edit/scripts/prepare_edit_request.py \ --prompt "Replace the sky with sunset, keep buildings unchanged" \ --image "https://example.com/input.png" ``` ## Output location - Default output: [REDACTED_SECRET] - Override base dir with `OUTPUT_DIR`. ## Workflow 1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files. ## References - `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wanwan2qq/gamified-habits (version 1.0.1)

--- name: gamified-habits description: 游戏化习惯养成助手 - 用游戏化的方式培养好习惯，支持打卡、XP 升级、属性面板 version: 1.0.0 author: 万万粥 license: MIT-0 triggers: - "创建习惯" - "删除习惯" - "习惯列表" - "打卡" - "我起床了" - "我跑步了" - "我读书了" - "看看我的属性" - "我的属性" - "属性面板" - "打卡记录" - "成就列表" - "habits" permissions: - exec --- # 🎮 游戏化习惯养成 Skill 将习惯养成游戏化的 OpenClaw Skill，让坚持变得有趣！ ## 功能特性 ### 核心功能 - ✅ 习惯管理（创建/删除/列表） - ✅ 打卡系统（命令行 + 自然语言） - ✅ XP 经验值系统（含连击加成） - ✅ 等级系统（升级成长） - ✅ 五维属性（体力/智力/财富/心灵/社交） - ✅ 成就系统 - ✅ 数据持久化（本地 JSON 存储） ### 🎮 游戏化功能（Phase 1+2） - ⚔️ **战斗故事**：每次打卡生成独特的战斗/冒险描述 - 🔥 **连击称号**：新手冒险者 → 不朽传说 - 👹 **Boss 战**：完成所有今日习惯后触发 Boss 战 - 📖 **冒险日记**：自动生成每日冒险日志 - 🎁 **胜利奖励**：击败 Boss 获得额外 XP 奖励 - 🏆 **成就系统**：解锁各种成就徽章 ### 🎨 自定义描述 - 创建习惯时可自定义描述：`--desc "击败赖床恶魔"` - 自定义成功描述：`--success "你战胜了睡意"` ## 使用方式 ### 命令行方式 ```bash habits create <名称> [类型] [--desc "描述"] [--success "成功描述"] # 创建习惯（支持自定义描述） habits delete <名称> # 删除习惯 habits list # 查看列表 habits checkin <名称> # 打卡（生成战斗故事） habits status # 查看属性面板 habits history [天数] # 查看历史 habits achievements # 查看成就 habits whoami # 查看当前用户 habits boss # 检查 Boss 战 habits diary [read] # 生成/查看今日日记 ``` ### 自然语言方式 - "创建一个早起习惯" - "我起床了" → 自动打卡 - "我跑步了" → 自动打卡 - "看看我的属性" → 显示面板 - "有什么成就" → 查看成就 ## 🎯 多用户支持 系统会**自动识别用户**，根据 OpenClaw 渠道和账号隔离数据： - 万万（钉钉）：`dingtalk-0124046821484330` - 万万（企业微信）：`wecom-xxx` - 其他用户：自动分配独立数据文件 每个用户的数据完全隔离，互不干扰。 ### 自然语言方式 - "创建一个早起习惯" - "我起床了" → 自动打卡 - "我跑步了" → 自动打卡 - "看看我的属性" → 显示面板 - "有什么成就" → 查看成就 ## 项目位置 - 技能目录：`~/.openclaw/skills/gamified-habits/` - CLI 入口：`~/.openclaw/skills/gamified-habits/bin/habits.js` - 数据存储：`~/.gamified-habits/user-data.json` ## 习惯类型 | 类型 | 属性 | 示例习惯 | |------|------|---------| | physical | 体力 | 早起、跑步、健身 | | intellectual | 智力 | 阅读、学习、写作 | | wealth | 财富 | 储蓄、记账、理财 | | spiritual | 心灵 | 冥想、日记、感恩 | | social | 社交 | 聚会、networking | ## 等级系统 升级所需 XP 公式：`Level_N = N² × 100` - Lv.1: 0-100 XP - Lv.2: 100-400 XP - Lv.3: 400-900 XP - Lv.4: 900-1600 XP - Lv.5: 1600-2500 XP ## 连击加成 - 1-3 天：1.0x - 4-7 天：1.1x - 8-14 天：1.2x - 15-30 天：1.5x - 30+ 天：2.0x ## 示例对话 **用户**: 创建一个早起习惯 **Skill**: ✅ 已创建习惯「早起」 类型：体力 | 频率：每日 | 奖励：50 XP **用户**: 我起床了 **Skill**: ✅ 「早起」打卡成功！ +55 XP (连击加成 x1.1) | 连击 5 天 🔥 💪 体力 +1，当前等级 Lv.3 (280/500 XP) **用户**: 看看我的属性 **Skill**: 🎯 Lv.5 自律勇者 (1680/2500 XP) 💪 体力：72 ████████░░ 🧠 智力：85 █████████░ 💰 财富：60 ██████░░░░ 😌 心灵：55 █████░░░░░ 🤝 社交：48 ████░░░░░░ 🔥 当前连击：早起 5 天，跑步 3 天 ## 开发信息 - Phase 1 MVP: 基础习惯管理 + 打卡 + XP 系统 - Phase 2: 游戏化增强 + 自然语言优化 - Phase 3: 社交功能 + 随机事件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nexscope/multichannel-ecommerce (version 1.0.0)

# Multichannel E-Commerce Management Manage e-commerce operations across multiple marketplaces and channels. Inventory sync, order routing, listing management, and unified analytics for Amazon, Shopify, Walmart, eBay, Etsy, and TikTok Shop. ## Capabilities - Multichannel management platform comparison (ChannelAdvisor, Sellbrite, LitCommerce) - Inventory synchronization and oversell prevention - Centralized order management and fulfillment routing - Product listing syndication and adaptation by channel - Pricing strategy across marketplaces - Unified analytics and reporting - Returns and customer service management across channels - Scaling strategy: when and how to add new channels ## Install ```bash npx skills add nexscope/multichannel-ecommerce ``` ## Usage Ask your AI agent: - "Set up multichannel selling across Amazon, Shopify, and Walmart." - "What is the best multichannel management tool for my business size?" - "How do I prevent overselling when selling on 4 marketplaces?" ## Output Structured recommendations with actionable steps, benchmarks, and platform-specific guidance. --- *Built by [Nexscope](https://www.nexscope.ai/) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zzhimin/shop-health-check (version 1.0.0)

--- name: shop-health-check description: 跨境电商店铺健康度自动巡检 Skill。支持多店铺配置，定时检查站点可用性、响应时间、SSL证书健康度、关键页面404/内容缺失，异常时自动推送到飞书。 tags: - monitoring - uptime - ssl - shop-health - e-commerce - feishu - cron --- # 店铺健康巡检 Skill ## 何时使用 - 用户说："检查店铺健康度"、"巡检店铺"、"帮我监控店铺是否宕机"、"店铺健康报告" - 作为定时任务（Cron / Heartbeat）定期执行，异常时推送到飞书 ## 前置条件 - Python 3.8+，需要 `requests`、`beautifulsoup4`、`urllib3`、`python-dateutil` 库 - 配置好多店铺信息（`config/shops.conf`） - 飞书 Webhook 已配置（环境变量 `FEISHU_WEBHOOK`，或脚本内配置） ## 配置文件 `config/shops.conf` ```ini [DEFAULT] # 默认阈值（可被店铺级别覆盖） response_timeout = 3 # 响应时间阈值（秒） ssl_warning_days = 14 # SSL 剩余天数警告阈值 check_sample_size = 5 # 分类页抽检商品数量 [shop1] name = 店铺1 domain = www.example.com base_url = https://www.example.com # 站点可用性检查的页面路径（逗号分隔，留空则用默认） check_paths = /, /c/best-sellers, /cart, /checkout # 要检查的分类页路径（用于抽检商品页） category_paths = /c/best-sellers, /c/new-arrivals, /c/sale # 可选：覆盖全局阈值 response_timeout = 5 ssl_warning_days = 30 [shop2] name = 店铺2 domain = shop2.example.com base_url = https://shop2.example.com check_paths = /, /products, /collections/all category_paths = /collections/all ``` > **使用提醒**：首次使用前，请先配置 `config/shops.conf`，填入真实的店铺域名和路径。 ## 工作流程 脚本按以下顺序执行： 1. **`check_sites.py`** — 检查站点可用性和响应时间 2. **`check_ssl.py`** — 检查 SSL 证书过期和状态 3. **`check_404.py`** — 爬取分类页 → 抽检商品页 → 检测404和错误内容 4. **`report.py`** — 汇总所有结果，判定告警，推送到飞书 ## 命令示例 ### 完整巡检 ```bash cd ~/.openclaw/skills/shop-health-check python3 scripts/report.py ``` ### 指定店铺巡检 ```bash python3 scripts/report.py --shop shop1 ``` ### 查看帮助 ```bash python3 scripts/report.py --help ``` ## 输出示例（正常） ``` ✅ 店铺健康巡检报告 | 2026-03-30 11:00 📦 shop1 (www.example.com) ✅ 站点可用性：全部正常（2/2 检查点） - / : 200 (1.2s) - /c/best-sellers : 200 (0.9s) ✅ SSL证书：正常（剩余 89 天） 🔍 商品页抽检：全部正常（5 个商品） 📦 shop2 (shop2.example.com) ✅ 站点可用性：全部正常（3/3 检查点） ✅ SSL证书：正常（剩余 45 天） 🔍 商品页抽检：全部正常（5 个商品） 🎉 所有店铺健康，无异常。 ``` ## 告警示例（有问题时） ``` 🚨 店铺健康巡检告警 | 2026-03-30 11:00 📦 shop1 (www.example.com) ❌ 站点可用性： - /checkout : 超时（>3s） ⚠️ SSL证书：剩余 12 天，请尽快续期 ❌ 商品页抽检： - /products/12345 : 404 📦 shop2 (shop2.example.com) ✅ 无异常 ⚡ 共 3 个问题需要处理 ``` ## 告警规则 | 检查项 | 告警条件 | |--------|---------| | 站点可用性 | HTTP状态码非200，或响应时间超过阈值 | | SSL证书 | 剩余有效期 < 警告天数（默认14天），或证书无效/自签名 | | 商品页404 | 被抽检的商品页返回404 | | 商品页内容 | 页面包含"此商品已下架"、"Page Not Found"、"404"等错误文本 | ## 定时任务配置 ```bash openclaw cron add \ --name "shop-health-check-hourly" \ --cron "0 * * * *" \ --session isolated \ --message "运行店铺健康巡检：cd ~/.openclaw/skills/shop-health-check && python3 scripts/report.py" \ --announce \ --channel feishu ``` ## 依赖安装 ```bash pip3 install requests beautifulsoup4 urllib3 python-dateutil ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: x760591483/email163 (version 1.0.0)

# 163邮箱发送工具 基于Go开发的163邮箱SMTP发送工具。支持HTML格式、多收件人和实时日志记录。 ## 编译安装 ```bash # 编译生成可执行文件 go build -o email163 main.go # 将可执行文件移动到PATH目录（可选） sudo mv email163 /usr/local/bin/ # 或者直接使用go install go install ``` ## 基本发送 ```bash email163 --subject "邮件标题" --info "邮件内容" --to [REDACTED_SECRET] email163 --subject "测试邮件" --info "这是测试内容" --to [REDACTED_SECRET] --to [REDACTED_SECRET] email163 --subject "通知" --info "内容" --to [REDACTED_SECRET],[REDACTED_SECRET] ``` ## HTML格式邮件 ```bash email163 --subject "HTML邮件" --info "<html><body><h1>标题</h1><p>这是<b>粗体</b>内容</p></body></html>" --to [REDACTED_SECRET] email163 --subject "富文本通知" --info "<html><body><h2>重要通知</h2><ul><li>项目更新</li><li>系统维护</li></ul></body></html>" --to [REDACTED_SECRET] ``` ## 带日志记录 ```bash email163 --subject "邮件标题" --info "内容" --to [REDACTED_SECRET] --log ./logs email163 --subject "重要通知" --info "内容" --to [REDACTED_SECRET] --log /var/log/email ``` ## 参数说明 - `--subject <标题>`: 邮件标题（必需） - `--info <内容>`: 邮件内容，支持HTML格式（必需） - `--to <邮箱>`: 收件人邮箱地址，可多次使用（必需） - `--log <路径>`: 日志文件保存路径（可选，默认当前目录） - `--help`: 显示帮助信息 ## 多收件人方式 ```bash # 方式1：多次使用 --to 参数 email163 --subject "群发邮件" --info "内容" --to [REDACTED_SECRET] --to [REDACTED_SECRET] --to [REDACTED_SECRET] # 方式2：逗号分隔多个邮箱 email163 --subject "群发邮件" --info "内容" --to [REDACTED_SECRET],[REDACTED_SECRET],[REDACTED_SECRET] ``` ## HTML内容示例 ```bash # 简单HTML email163 --subject "格式化邮件" --info "<html><body><p>普通文本</p><p><b>粗体文本</b></p><p><i>斜体文本</i></p></body></html>" --to [REDACTED_SECRET] # 复杂HTML结构 email163 --subject "详细通知" --info "<html><body><h2>系统通知</h2><p>尊敬的用户：</p><ul><li>系统将于今晚维护</li><li>预计维护时间2小时</li></ul><p>如有疑问请联系：<a href='mailto:[REDACTED_SECRET]'>技术支持</a></p></body></html>" --to [REDACTED_SECRET] ``` ## 日志功能 - 日志文件格式：`YYYY_MM_DD_email.log` - 记录内容：发送时间、标题、收件人、内容、结果 - 同日期自动追加，新日期创建新文件 - 自动创建日志目录 注意事项： - 需要设置环境变量 `EMAIL163_ADDRESS` 和 `EMAIL163_PASSWORD` - `EMAIL163_PASSWORD` 应使用163邮箱的授权码，不是登录密码 - HTML内容以 `<html>` 开头时使用用户格式，否则自动包装 - 邮箱地址会进行格式验证 - 日志内容超过200字符时会自动截断 ## 环境变量设置 ```bash export EMAIL163_ADDRESS="[REDACTED_SECRET]" export EMAIL163_PASSWORD="your-authorization-code" ``` 获取163邮箱授权码： 1. 登录163邮箱设置 2. 开启SMTP服务 3. 生成授权码用作密码

path: scripts/build.sh
content: ## 编译main.go为email163可执行文件
go build -o email163 main.go

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cjboy007/ssa-sales-dashboard (version 1.0.0)

# Sales Dashboard 销售仪表盘 ## 描述 渐进式销售数据概览，从 OKKI CRM 和 Campaign Tracker 采集核心指标，生成周报/月报，通过 Discord 推送。 ## 架构 ``` scripts/ ├── data-collector.js # 数据采集（OKKI API + 可选数据源） ├── metrics-calculator.js # 指标计算（漏斗 + 环比 + 异常检测） ├── report-generator.js # Markdown 报告生成（周报/月报） └── discord-push.js # Discord 推送（自动分片 >1800 字符） config/ └── dashboard-config.json # KPI 定义 / 数据源映射 / 告警阈值 data/ ├── latest.json # 最新一次采集的指标 ├── calculated.json # 计算后的指标（含漏斗/告警） ├── snapshots/ # 历史快照（{period}-{date}.json） └── reports/ # Markdown 报告 ``` ## 数据源 | 数据源 | 必选 | 类型 | 说明 | |--------|------|------|------| | OKKI CRM | ✅ | API | 客户/订单/报价/线索/商机 | | campaign-tracker | ❌ | 文件 | 邮件发送量/回复率 | | follow-up-engine | ❌ | 文件 | 跟进数据 | | order-tracker | ❌ | 文件 | 订单跟踪 | | customer-segmentation | ❌ | 文件 | 客户分群 | | pricing-engine | ❌ | 文件 | 定价数据 | 缺失的可选数据源显示 N/A，不报错。 ## 用法 ### 采集数据 ```bash node scripts/data-collector.js --period weekly [--date 2026-03-24] [--dry-run] node scripts/data-collector.js --period monthly [--date 2026-03-01] ``` ### 计算指标 ```bash node scripts/metrics-calculator.js --check-alerts ``` ### 生成报告 ```bash node scripts/report-generator.js --period weekly [--dry-run] node scripts/report-generator.js --period monthly ``` ### Discord 推送 ```bash # 推送最新周报 node scripts/discord-push.js --latest-report weekly # 推送指定报告 node scripts/discord-push.js --report data/reports/weekly-2026-03-24.md # 手动告警 node scripts/discord-push.js --alert "⚠️ 订单金额异常下降" ``` ## 定时任务（OpenClaw cron） | 任务 | Cron ID | 时间 | 说明 | |------|---------|------|------| | 周报 | bbdf51a8-36e0-4ee9-824b-0c92f7a44bf1 | 每周一 09:00 CST | 采集 + 计算 + 生成 + 推送 | | 月报 | 13e4378e-655e-4082-8bc0-c8fbd8e91c12 | 每月 1 日 09:00 CST | 采集 + 计算 + 生成 + 推送 | ## 告警阈值（保守初始值） | 指标 | 条件 | 阈值 | |------|------|------| | 邮件回复率 | 低于 | 10% | | 订单金额 | 环比下降超 | 50% | | 周订单数 | 等于 | 0 | ## 历史快照机制 每次采集保存 `data/snapshots/{period}-{date}.json`，永不覆盖。 `data/latest.json` 每次更新为最新数据。 环比计算自动读取上一期快照。 ## 已知问题 & 改进记录 ### v1.0.1 (2026-03-25) - **修复**: Campaign Tracker reply_rate 从小数 (0.667) 正确转换为百分比 (66.7%) - **修复**: Campaign Tracker JSON 报告嵌套 metrics 字段兼容 - **新增**: OpenClaw cron 定时任务（周报 + 月报）已创建 - **验证**: OKKI API 时间过滤正常工作（start_time/end_time/time_type 参数） - **验证**: 历史快照机制正常（data/snapshots/） - **待优化**: OKKI 沙盒环境数据为空（全 0），切换生产环境后验证 --- **版本:** 1.0.1 **创建:** 2026-03-25 **更新:** 2026-03-25


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: alanoo7/audio-summary (version 1.0.0)

# audio-summary Skill 音频/视频转文本总结助手。 ## 功能 1. **自动音频提取**：使用 `ffmpeg` 从 MP4 等视频文件中提取 16k mono 压缩音频，以适配大模型体积限制。 2. **转录转总结**：基于百炼 `qwen3-asr-flash` 模型，自动将音频转换为文字并生成内容分段总结。 3. **大文件支持**：通过 48k 压缩，支持最长约 5-8 分钟的视频单次直接转录。 ## 依赖 - `ffmpeg` (已安装在系统路径) - `openai` Python SDK (已安装) - 百炼 API KEY (已在脚本中配置为 `sk-76735...`) ## 使用方法 ### 从命令行运行 ```powershell # 对指定视频进行提取和总结 python .openclaw/workspace/skills/audio-summary/audio_summary_skill.py "C:\Path\To\Your\Video.mp4" ``` ### 文件位置 - 提取出的总结文本将自动保存在视频同级目录下，并命名为 `视频名_summary.txt`。 ## 注意事项 - 目前单次 Base64 转录限制为 6MB，对于超过 10 分钟的长视频，建议先手动切分或进一步降低码率。 - API 费用按 `qwen3-asr-flash` 模型计费。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 1kalin/afrexai-hipaa-compliance (version 1.0.0)

# HIPAA Compliance for AI Agents Generate HIPAA compliance checklists, risk assessments, and audit frameworks for healthcare organizations deploying AI agents. ## What This Skill Does When activated, produce any of these deliverables based on user request: ### 1. Pre-Deployment Compliance Gate - BAA requirements checklist for AI vendors - PHI data flow mapping template - Minimum Necessary standard application guide - Risk assessment framework (45 CFR 164.308(a)(1)) ### 2. Technical Safeguards (45 CFR 164.312) **Access Controls:** - Unique service account IDs for AI agents - Emergency access procedures for system failures - 15-minute auto-logoff configuration - Role-based minimum necessary permissions **Audit Controls:** - PHI access logging (timestamp, user, action, data) - 6-year retention compliance - Anomaly detection on access patterns - AI decision audit trails **Transmission Security:** - TLS 1.3 enforcement - E2E encryption for patient comms - Certificate pinning for API connections - No PHI in URLs, query strings, or logs ### 3. AI-Specific Risk Matrix | Risk | Impact | Mitigation | |------|--------|------------| | Prompt injection → PHI leak | Critical | Input sanitization, output filtering, sandboxing | | Model training on PHI | High | BAA prohibition, single-tenant deployment | | Hallucinated medical info | Critical | Human-in-loop, confidence thresholds | | Shadow AI with PHI | High | Approved tool registry, DLP rules | ### 4. Breach Response Timeline - 0-1 hrs: Contain (disable agent, preserve logs) - 1-24 hrs: Assess scope of PHI exposure - 24-48 hrs: Document root cause, affected individuals - Within 60 days: Notify HHS + individuals + media (if 500+) - 30-90 days: Remediate, patch, retrain ### 5. Compliance by Use Case Rate each AI deployment: - Patient scheduling → Medium risk - Billing/coding → High risk - Clinical decision support → Critical risk - Patient communication → High risk - Medical records summarization → Critical risk ### 6. Penalty Reference | Tier | Per Violation | Annual Cap | |------|-------------|------------| | Unknowing | $141 - $71,162 | $2,134,831 | | Reasonable cause | $1,424 - $71,162 | $2,134,831 | | Willful neglect (corrected) | $14,232 - $71,162 | $2,134,831 | | Willful neglect (not corrected) | $71,162 | $2,134,831 | Average healthcare breach cost: $10.93M (IBM/Ponemon 2025). ## Output Format - Markdown checklist with status columns - Risk matrix with impact/likelihood scoring - Timeline tables for breach response - Department-specific compliance cards ## Resources - [Healthcare AI Context Pack — $47](https://afrexai-cto.github.io/context-packs/) — Full patient journey automation, revenue cycle, EHR integration patterns - [AI Revenue Leak Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/) — Find where manual processes cost you money - [AI Agent Setup Wizard](https://afrexai-cto.github.io/agent-setup/) — Configure compliant AI agents in 5 minutes


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

--- name: linearis version: 1.0.0 description: Linear.app CLI for issue tracking. Use for listing, creating, updating, and searching Linear issues, comments, documents, cycles, and projects. Optimized for LLM agents with JSON output. metadata: {"clawdbot":{"emoji":"📋","requires":{"bins":["linearis"]},"install":[{"id":"npm","kind":"node","package":"linearis","bins":["linearis"],"label":"Install linearis (npm)"}]}} --- # linearis CLI for [Linear.app](https://linear.app) with JSON output, designed for LLM agents. ## Setup ```bash npm install -g linearis ``` Auth (one of): - `echo "lin_api_..." > ~/.linear_api_token` (recommended) - `export LINEAR_API_TOKEN="lin_api_..."` - `--api-token <token>` flag Get API key: Linear Settings → Security & Access → Personal API keys ## Commands ### Issues ```bash linearis issues list -l 20 # List recent issues linearis issues list -l 10 --team WHO # Filter by team linearis issues search "bug" # Full-text search linearis issues read ABC-123 # Get issue details linearis issues create --title "Fix bug" --team WHO --priority 2 linearis issues update ABC-123 --status "Done" linearis issues update ABC-123 --title "New title" --assignee user123 linearis issues update ABC-123 --labels "Bug,Critical" --label-by adding linearis issues update ABC-123 --parent-ticket EPIC-100 # Set parent ``` ### Comments ```bash linearis comments create ABC-123 --body "Fixed in PR #456" ``` ### Documents ```bash linearis documents list linearis documents list --project "Backend" linearis documents create --title "Spec" --content "# Overview..." linearis documents read <doc-id> linearis documents update <doc-id> --content "Updated" linearis documents delete <doc-id> ``` ### File Uploads/Downloads ```bash linearis embeds upload ./screenshot.png linearis embeds download "<url>" --output ./file.png ``` ### Teams, Users, Projects ```bash linearis teams list linearis users list --active linearis projects list linearis cycles list --team WHO --active ``` ### Full Usage ```bash linearis usage # Complete command reference (~1k tokens) ``` ## Output All commands return JSON by default. Pipe to `jq` for processing: ```bash linearis issues list -l 5 | jq '.[].identifier' ``` ## Priority Values - 0: No priority - 1: Urgent - 2: High - 3: Medium - 4: Low ## Links - Docs: https://github.com/czottmann/linearis - Blog: https://zottmann.org/2025/09/03/linearis-my-linear-cli-built.html


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