OpenClaw/clawhub-security-signals huggingface dataset:
Skill: anbangzhiguo/email-send-cn (version 1.0.2)

--- name: email-send description: 基于 Nodemailer 的邮件发送技能。使用 nodemailer + SMTP 向任意邮箱发送邮件。支持 163、QQ、Gmail 等主流 SMTP 服务。当用户请求发送邮件时触发。 metadata: { "openclaw": { "emoji": "📧", "user-invocable": true, "requires": { "env": ["SMTP_HOST", "SMTP_USER", "SMTP_PASS"], "bins": ["node"] }, "primaryEnv": "SMTP_PASS" } } --- # Email Send Skill 使用 nodemailer 通过 SMTP 发送邮件。 ## 安装依赖 使用前请先安装依赖： ```bash npm install ``` 这将安装包括 nodemailer 在内的所有必要依赖。 ## 安全注意事项 - **请勿在命令行中直接使用真实的 SMTP 凭据**，特别是在生产环境中 - **避免将 SMTP 密码提交到版本控制系统** - **使用环境变量或安全的配置管理系统**来存储敏感信息 - **定期更新 SMTP 授权码**以增强安全性 ## 环境配置 SMTP 凭据通过环境变量配置： | 变量 | 说明 | 示例 | |------|------|------| | SMTP_HOST | SMTP 服务器地址 | smtp.example.com | | SMTP_PORT | 端口 (25/465/587) | 465 | | SMTP_SECURE | 是否 SSL (true/false) | true | | SMTP_USER | 用户名/邮箱 | [REDACTED_SECRET] | | SMTP_PASS | 授权码/密码 | your-smtp-password | | SMTP_FROM | 发件人显示名 | Your Name <[REDACTED_SECRET]> | ## 使用方式 ### 直接调用脚本 ```bash SMTP_HOST=smtp.163.com \ SMTP_PORT=465 \ SMTP_SECURE=true \ SMTP_USER=[REDACTED_SECRET] \ SMTP_PASS=your-smtp-password \ SMTP_FROM="Your Name <[REDACTED_SECRET]>" \ node {baseDir}/scripts/send.js \ --to "[REDACTED_SECRET]" \ --subject "Email Subject" \ --body "Email Body" ``` ### AI 调用方式 当用户请求发送邮件时，使用 exec 工具执行上述命令。 ## 脚本参数 | 参数 | 必填 | 说明 | |------|------|------| | --to | 是 | 收件人邮箱 | | --cc | 否 | 抄送 (逗号分隔多个) | | --subject | 是 | 邮件主题 | | --body | 是 | 邮件正文 | | --html | 否 | body 是否为 HTML (设为 "true") | ## 常用 SMTP 配置 - **163 邮箱**: smtp.163.com, 端口 465 (SSL) 或 587 (TLS) - **QQ 邮箱**: smtp.qq.com, 端口 465 (SSL) 或 587 (TLS) - **Gmail**: smtp.gmail.com, 端口 587 (TLS) 授权码需要在邮箱设置中获取。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gowanginc/common-core-ela-checker (version 1.0.0)

--- name: common-core-ela-checker description: Common Core State Standards ELA alignment checker. Analyzes lesson plans, unit plans, and assessments against CCSS ELA standards for K-12. Use when reviewing lesson plans for standards alignment, creating curriculum maps, designing assessments, or ensuring coverage of reading, writing, speaking/listening, and language standards. --- # Common Core ELA Standards Checker Align lesson and unit plans with Common Core State Standards for English Language Arts (K-12). ## Key Features ### Standards Alignment - Check lessons against CCSS ELA standards - Identify which standards are addressed - Suggest missing standards coverage - Verify grade-level appropriateness ### Standards Categories - **Reading**: Literature, Informational Text, Foundational Skills (K-5) - **Writing**: Text Types, Production, Research - **Speaking & Listening**: Comprehension, Presentation - **Language**: Conventions, Knowledge, Vocabulary ### Analysis Types - Single lesson alignment check - Unit plan coverage analysis - Assessment standards mapping - Curriculum gap identification ## Quick Commands **Check lesson**: "Analyze this lesson plan for CCSS alignment" **Review unit**: "Check this unit plan against grade 8 ELA standards" **Suggest standards**: "What CCSS standards should I include for a poetry unit?" **Gap analysis**: "What standards are missing from this curriculum?" ## References - [Common Core ELA Standards](references/common-core-ela-standards.pdf) - Complete K-12 standards document - [Reading Standards](references/reading-standards.md) - Literature and informational text - [Writing Standards](references/writing-standards.md) - Text types and purposes - [Speaking & Listening](references/speaking-listening.md) - Comprehension and collaboration - [Language Standards](references/language-standards.md) - Conventions and vocabulary

path: references/language-standards.md
content: # Language Standards

## Conventions (L.1-2)
- L.K-12.1: Demonstrate command of grammar and usage
- L.K-12.2: Demonstrate command of capitalization, punctuation, spelling

## Knowledge of Language (L.3)
- L.K-12.3: Apply knowledge of language in different contexts

## Vocabulary Acquisition (L.4-6)
- L.K-12.4: Determine word meanings using context clues
- L.K-12.5: Demonstrate understanding of figurative language
- L.K-12.6: Acquire and use academic vocabulary


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnnywang2001/lorem-gen (version 1.0.0)

--- name: lorem-gen description: Generate placeholder text for design mockups, development, and testing. Supports classic Lorem Ipsum, hipster-style, and tech jargon. Output as plain text, HTML, Markdown, or JSON. Use when creating dummy content, filler text, placeholder paragraphs, or sample text for layouts and prototypes. --- # Lorem Gen Generate placeholder text via CLI. Supports multiple styles and output formats. ## Quick Start ```bash # 3 paragraphs of classic Lorem Ipsum python3 scripts/lorem_gen.py # 5 sentences python3 scripts/lorem_gen.py -n 5 -u sentences # 50 words of hipster-style text python3 scripts/lorem_gen.py -n 50 -u words -s hipster # Tech jargon as HTML python3 scripts/lorem_gen.py -s tech -f html # JSON output with reproducible seed python3 scripts/lorem_gen.py -n 2 -f json --seed 42 ``` ## Options | Flag | Description | Default | |------|-------------|---------| | `-n, --count` | Number of units | 3 | | `-u, --unit` | `paragraphs`, `sentences`, or `words` | paragraphs | | `-s, --style` | `lorem`, `hipster`, or `tech` | lorem | | `-f, --format` | `plain`, `html`, `markdown`, `json` | plain | | `--no-classic` | Skip "Lorem ipsum dolor sit amet..." opening | off | | `--seed` | Random seed for reproducible output | none | ## Styles - **lorem** — Classic Latin placeholder text - **hipster** — Artisan / wellness / lifestyle vocabulary - **tech** — DevOps, cloud, and infrastructure jargon ## Notes - No external dependencies (Python 3 stdlib only) - First paragraph starts with the classic opening by default (use `--no-classic` to disable) - Use `--seed` for deterministic output across runs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ianderrington/clawx (version 0.1.0)

--- name: clawx description: Agent verification via ClawX OAuth system. Use when checking agent verification status, embedding verification widgets, or working with agent identity/trust tiers. --- # ClawX - Agent Verification ## Verification API ### Check Agent Verification ```bash curl https://clawx.ai/api/v1/agents/{handle}/verify ``` Response: ```json { "verified": true, "tier": "quality", "verifiedAt": "2026-01-15T10:30:00Z" } ``` ### Verification Tiers | Tier | Meaning | |------|---------| | `null` | Unverified agent | | `human` | Human-backed agent (verified operator) | | `quality` | Premium verified agent (quality assured) | ## Embeddable Widget Display verification badge on any page: ```html <script src="https://clawx.ai/widget.js"></script> <div id="clawx-verification"></div> <script> ClawXWidget.init({ handle: 'agent_username', target: '#clawx-verification', theme: 'light' // or 'dark' }); </script> ``` ## Use Cases ### Verify before interaction ```javascript async function checkAgent(handle) { const res = await fetch(`https://clawx.ai/api/v1/agents/${handle}/verify`); const data = await res.json(); if (!data.verified) { console.warn('Interacting with unverified agent'); } return data.tier; } ``` ### Display trust indicator ```javascript const tier = await checkAgent('my-agent'); const badge = tier === 'quality' ? '✓✓' : tier === 'human' ? '✓' : '?'; ``` ## Integration Notes - Verification is OAuth-based - agents authenticate through ClawX - Tiers indicate trust level, not capability - Widget auto-updates when verification status changes - API rate limited to 100 req/min per IP


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zuoyuting214/zyt-ai-creation (version 0.1.0)

--- name: chanjing-ai-creation description: Use Chanjing AI creation APIs to submit image or video generation tasks across multiple models, inspect task status, poll async results, and explicitly download generated assets when requested. --- # Chanjing AI Creation ## When to Use This Skill 当用户要做这些事时使用本 Skill： * 用蝉镜 AI 创作模型生成图片 * 用蝉镜 AI 创作模型生成视频 * 查询 AI 创作任务列表或单个任务详情 * 轮询 AI 创作异步结果 * 在用户明确要求时下载图片或视频到本地 如果需求更接近“文生数字人”，优先使用 `chanjing-text-to-digital-person`。 如果需求更接近“已有数字人视频合成”，优先使用 `chanjing-video-compose`。 ## Preconditions 本 Skill 自己包含本地配置和鉴权流程，不依赖其他 skill 的运行时脚本。 本 Skill 使用： * 配置文件：`~/.chanjing/credentials.json` * 若设置环境变量 `CHANJING_CONFIG_DIR`：使用 `$CHANJING_CONFIG_DIR/credentials.json` * API 基础地址：`https://open-api.chanjing.cc`（可用 `CHANJING_API_BASE` 覆盖） 当本地缺少 AK/SK 或 AK/SK 无效时，脚本可能在默认浏览器打开蝉镜官网登录页： `https://www.chanjing.cc/openapi/login` ## Standard Workflow AI 创作的主接口是统一提交器： 1. 调用 `submit_task` 提交图片或视频生成任务，得到 `unique_id` 2. 调用 `poll_task` 轮询直到成功，得到 `output_url` 3. 如需回看任务参数或错误原因，调用 `get_task` 4. 如需看历史记录，调用 `list_tasks` 5. 只有在用户明确要求保存到本地时，才调用 `download_result` 这个 skill 默认做成“通用任务提交器”： * 对常见图片/视频模型，优先使用脚本提供的通用参数 * 对特殊模型参数，使用 `--body-file` 或 `--body-json` 透传完整请求体 ## Covered APIs 本 Skill 当前覆盖： * `POST /open/v1/ai_creation/task/submit` * `POST /open/v1/ai_creation/task/page` * `GET /open/v1/ai_creation/task` ## Scripts 脚本目录： * `scripts/` | 脚本 | 说明 | |------|------| | `chanjing-config` | 写入/查看本地 `app_id` 与 `secret_key`，并清理旧 token 缓存 | | `chanjing-get-token` | 从本地凭证获取有效 `access_token`（必要时自动刷新） | | `_auth.py` | 读取本地凭证、获取或刷新 `access_token` | | `submit_task` | 提交 AI 创作任务，输出 `unique_id` | | `get_task` | 获取单个任务详情 | | `list_tasks` | 列出图片或视频任务 | | `poll_task` | 轮询任务直到完成，默认输出第一个结果地址 | | `download_result` | 下载图片或视频到 `outputs/ai-creation/` | ## Usage Examples 示例 1：Seedream 3.0 文生图 ```bash TASK_ID=$(python3 scripts/submit_task \ --creation-type 3 \ --model-code "doubao-seedream-3.0-t2i" \ --prompt "赛博朋克城市夜景，霓虹灯，雨夜，电影镜头" \ --aspect-ratio "16:9" \ --clarity 2048 \ --number-of-images 1) python3 scripts/poll_task --unique-id "$TASK_ID" ``` 示例 2：腾讯 Kling v2.1 Master 图生视频 ```bash TASK_ID=$(python3 scripts/submit_task \ --creation-type 4 \ --model-code "tx_kling-v2-1-master" \ --ref-img-url "https://res.chanjing.cc/chanjing/res/aigc_creation/photo/start.jpg" \ --ref-img-url "https://res.chanjing.cc/chanjing/res/aigc_creation/photo/end.jpg" \ --prompt "角色从静止到转身，镜头平滑移动，叙事感强" \ --aspect-ratio "9:16" \ --clarity 1080 \ --quality-mode pro \ --video-duration 5) python3 scripts/poll_task --unique-id "$TASK_ID" ``` 示例 3：直接透传完整 JSON ```bash python3 scripts/submit_task \ --body-file ./payload.json ``` ## Download Rule 下载是显式动作，不是默认动作： * `poll_task` 成功后应先返回远端 `output_url` * 不要自动下载结果文件 * 只有当用户明确表达“下载到本地”“保存到 outputs”“帮我落盘”时，才执行 `download_result` ## Output Convention 默认本地输出目录： * `outputs/ai-creation/` ## Additional Resources 更多接口细节见： * `skills/chanjing-ai-creation/reference.md` * `skills/chanjing-ai-creation/examples.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: xwings/py-googlecalendar-cli (version 1.0.0)

--- name: google-calendar description: Tiny and short Python CLI tool to manage Google Calendar events from the command line -- list, add, update, delete events via the Calendar API. license: MIT homepage: https://github.com/xwings/py-googlecalendar-cli compatibility: Requires Python 3.6+. Network access to Google Calender metadata: {"author": "xwings", "openclaw": {env: ["GOOGLE_CLIENT_ID", "GOOGLE_CLIENT_SECRET", "GOOGLE_REFRESH_TOKEN", "GOOGLE_CALENDAR_ID"], "bins": ["python3 {baseDir}/scripts/google-calendar-cli.py"]}} --- # Google Calendar CLI A single-file CLI for Google Calendar using only Python 3 standard library. ## Setup 1. Create a Google Cloud project and enable the **Google Calendar API**. 2. Create **OAuth 2.0 credentials** (Desktop app). Note the client ID and secret. 3. Obtain a refresh token via the OAuth consent flow. 4. Export credentials: ```bash export GOOGLE_CLIENT_ID=... export GOOGLE_CLIENT_SECRET=... export GOOGLE_REFRESH_TOKEN=... export GOOGLE_CALENDAR_ID=primary # optional, defaults to "primary" ``` ## Usage ``` google-calendar-cli.py <command> [options] ``` | Command | Description | |----------|--------------------------| | `list` | List upcoming events | | `today` | Show today's events | | `add` | Create a new event | | `update` | Update an existing event | | `delete` | Delete an event | Credentials can also be passed as flags (`--client-id`, `--client-secret`, `--refresh-token`, `--calendar-id`). Use `--json` for raw JSON output. Run with `-h` for full help. ## Examples ```bash # List next 10 events python3 {baseDir}/scripts/google-calendar-cli.py list # Today's events python3 {baseDir}/scripts/google-calendar-cli.py today # Events in a date range python3 {baseDir}/scripts/google-calendar-cli.py list --from 2025-06-01T00:00:00Z --to 2025-06-30T23:59:59Z # Add an event python3 {baseDir}/scripts/google-calendar-cli.py add --title "Meeting" \ --start 2025-06-01T09:00:00-07:00 --end 2025-06-01T10:00:00-07:00 # Update an event python3 {baseDir}/scripts/google-calendar-cli.py update --event-id EVENT_ID --title "New Title" # Delete an event python3 {baseDir}/scripts/google-calendar-cli.py delete --event-id EVENT_ID ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/skylv-self-thinking-agent (version 1.0.0)

--- name: skylv-metacognition-engine description: Enables AI agents to reflect on their own reasoning, detect cognitive biases, and improve decision quality through structured self-examination loops. keywords: metacognition, self-reflection, bias-detection, reasoning, self-improvement, agent-ai triggers: metacognition, self-reflection, agent thinking, bias detection, reasoning quality --- # Metacognition Engine **Give your AI agent the ability to think about its own thinking.** ## What is Metacognition? Metacognition = "thinking about thinking." This skill enables AI agents to: - Detect when they're uncertain or confused - Identify reasoning gaps before they cause errors - Recognize cognitive biases in their own output - Self-correct before delivering answers ## Core Framework ### 1. Pre-Output Check Before responding, run through these questions: ``` 1. Am I confident in this answer? (Yes / Partial / No) 2. What are the 3 most likely ways this could be wrong? 3. What information would I need to be 100% certain? ``` ### 2. Cognitive Bias Detection Check for common biases: - **Anthropomorphism** — projecting human traits onto AI - **Authority bias** — deferring to stated credentials without verification - **Hindsight bias** — acting like something was obvious after the fact - **Confirmation bias** — seeking only confirming evidence ### 3. Uncertainty Quantification Express confidence explicitly: | Confidence | Meaning | Action | |------------|---------|--------| | 90%+ | Highly confident | Answer directly | | 70-89% | Likely correct | Answer + add caveat | | 50-69% | Uncertain | Ask clarifying questions | | <50% | Likely wrong | Decline or escalate | ## Example **Without metacognition:** > "The capital of France is Paris." **With metacognition:** > "Based on my training data, the capital of France is Paris (confidence: 95%). > Note: My knowledge has a cutoff date. For real-time data, verify current information." ## Use Cases - **Critical decisions**: Add metacognition checkpoint before any consequential answer - **User corrections**: When a user corrects you, analyze WHY you were wrong - **Complex problems**: Run bias detection before solving multi-step problems - **Knowledge boundaries**: Automatically flag when you're approaching your knowledge limit ## MIT License © SKY-lv


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: litiao1224/obsidian-litiao (version 1.0.0)

--- name: obsidian-litiao description: Work with Obsidian vaults (plain Markdown notes) and automate via obsidian-cli. homepage: https://help.obsidian.md metadata: {"clawdbot":{"emoji":"💎","requires":{"bins":["obsidian-cli"]},"install":[{"id":"brew","kind":"brew","formula":"yakitrak/yakitrak/obsidian-cli","bins":["obsidian-cli"],"label":"Install obsidian-cli (brew)"}]}} --- # Obsidian Obsidian vault = a normal folder on disk. Vault structure (typical) - Notes: `*.md` (plain text Markdown; edit with any editor) - Config: `.obsidian/` (workspace + plugin settings; usually don’t touch from scripts) - Canvases: `*.canvas` (JSON) - Attachments: whatever folder you chose in Obsidian settings (images/PDFs/etc.) ## Find the active vault(s) Obsidian desktop tracks vaults here (source of truth): - `~/Library/Application Support/obsidian/obsidian.json` `obsidian-cli` resolves vaults from that file; vault name is typically the **folder name** (path suffix). Fast “what vault is active / where are the notes?” - If you’ve already set a default: `obsidian-cli print-default --path-only` - Otherwise, read `~/Library/Application Support/obsidian/obsidian.json` and use the vault entry with `"open": true`. Notes - Multiple vaults common (iCloud vs `~/Documents`, work/personal, etc.). Don’t guess; read config. - Avoid writing hardcoded vault paths into scripts; prefer reading the config or using `print-default`. ## obsidian-cli quick start Pick a default vault (once): - `obsidian-cli set-default "<vault-folder-name>"` - `obsidian-cli print-default` / `obsidian-cli print-default --path-only` Search - `obsidian-cli search "query"` (note names) - `obsidian-cli search-content "query"` (inside notes; shows snippets + lines) Create - `obsidian-cli create "Folder/New note" --content "..." --open` - Requires Obsidian URI handler (`obsidian://…`) working (Obsidian installed). - Avoid creating notes under “hidden” dot-folders (e.g. `.something/...`) via URI; Obsidian may refuse. Move/rename (safe refactor) - `obsidian-cli move "old/path/note" "new/path/note"` - Updates `[[wikilinks]]` and common Markdown links across the vault (this is the main win vs `mv`). Delete - `obsidian-cli delete "path/note"` Prefer direct edits when appropriate: open the `.md` file and change it; Obsidian will pick it up.

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: obsidian
version: 1.0.0
publishedAt: 1767545362143

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: akkualle/akkualle-gsc-analytics (version 1.0.0)

--- name: gsc-search description: "Ruft Google Search Console Daten für akku-alle.de ab - Klicks, Impressionen, CTR, Rankings und Top-Keywords. Nutze das shell Tool um /root/.openclaw/skills/gsc-search/gsc-search auszuführen." metadata: {"requires":{"env":["GOOGLE_APPLICATION_CREDENTIALS"]},"emoji":"🔍","always":true} user-invocable: true --- # Google Search Console Skill Ruft Search Console Daten für akku-alle.de ab. ## Aufruf via Shell Tool **WICHTIG**: Rufe dieses Skill mit dem `shell` Tool auf: ```bash /root/.openclaw/skills/gsc-search/gsc-search ``` Für nur Keywords/Suchanfragen: ```bash /root/.openclaw/skills/gsc-search/gsc-search "queries" ``` ## Beispiel-Ausgabe ``` ## 🔍 Search Console Übersicht (02.02 - 09.02.2026) - **Klicks:** 106 - **Impressionen:** 10,661 - **CTR:** 0.99% - **Ø Position:** 6.9 ## 🔎 Top Suchanfragen | Query | Klicks | Impressionen | CTR | Pos | |-------|--------|--------------|-----|-----| | e scooter versicherung 2026 | 2 | 138 | 1.4% | 2.8 | ``` ## Wann verwenden Nutze dieses Skill wenn der User fragt: - "Zeige mir die Search Console Daten" - "Für welche Keywords rankt akku-alle.de?" - "Wie sind die Google Rankings?" - "SEO Statistiken"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: unisound-llm/unisound-overall-report (version 1.0.0)

--- name: health-exam-overall-report description: 体检报告整体解读。输入完整体检报告文本，输出整体健康评级、主要发现（含严重程度）、优先行动清单和个性化生活方式建议（JSON + 通俗易懂的整体解读）。 metadata: { "openclaw": { "emoji": "📄" } } --- # 体检报告整体解读 概述 ---- 面向**体检中心/健康管理机构**，给定受检者完整体检报告，本技能会： - 综合所有检查结果给出整体健康评级（优秀/良好/需关注/需重视） - 提取关键异常发现，按重要程度排序并标注严重程度 - 列出最优先需要处理的行动（最多3条） - 给出个性化生活方式建议 - 建议下次体检时间及重点关注项目 - 生成面向受检者的通俗易懂整体解读文字 数据安全、隐私与伦理声明 ------------------------ - **最小必要原则**：仅处理体检报告解读所必需的检查结果数据；不要求包含姓名、证件号、手机号等直接身份标识。 - **严格脱敏**：在发送至任何模型/接口前，会对可识别个人身份的信息进行脱敏/去标识化处理。 - **不做本地持久化**：不将用户输入与中间结果写入本地持久化存储。仅在内存中短暂处理；**本次调用结束即销毁**。 - **医疗边界**：本技能输出为健康管理辅助解读，不构成医疗诊断；如有异常项目，请务必就诊由执业医生进一步评估。 输入格式 -------- 纯文本（UTF-8），包含体检报告内容（可粘贴报告原文），例如： ```text 受检者：男，45岁 体检日期：2026年4月 一般检查：身高175cm，体重82kg，BMI 26.8，血压142/90mmHg 血常规：WBC 7.2×10⁹/L，RBC 5.1×10¹²/L，HGB 155g/L（均正常） 生化：ALT 52U/L（↑），AST 38U/L，TG 2.8mmol/L（↑），TC 5.9mmol/L（↑），FBG 6.2mmol/L（↑） 尿常规：尿蛋白(-)，尿糖(-) 心电图：窦性心律，ST段轻度改变 胸部CT：双肺未见明显实质性病变 腹部超声：轻度脂肪肝 ``` 也支持 JSON 格式（包含 `text`/`content`/`report` 字段的对象）。 快速开始 -------- ```bash # 从 skills 目录运行 python3 health-exam/report-interpret/overall-report/scripts/run.py \ --input data/health-exam-overall/case-001.txt \ --appkey <your-appkey> # 保存输出到文件 python3 health-exam/report-interpret/overall-report/scripts/run.py \ --input data/health-exam-overall/case-001.txt \ --appkey <your-appkey> \ --output runs/health-exam-overall/case-001.json ``` 参数说明 -------- - `--input PATH`：**必填**。体检报告文件路径（txt 或 json，UTF-8）。 - `--appkey STRING`：**必填**。调用内部医疗大模型的鉴权 key，由平台分配。 - `--output PATH`：输出文件路径（默认：打印到 stdout）。 - `--base URL`：内部大模型 base URL（默认：`https://maas-api.hivoice.cn/v1`）。 - `--model STRING`：模型名称（默认：`u1-insuremed`）。 - `--timeout SECONDS`：HTTP 超时秒数；`0` 表示一直等待（默认：0）。 - `--encoding STRING`：输入文件编码（默认：`utf-8`）。 输出约定 -------- 输出分两部分： **JSON 结构**： ```json { "overall_grade": "需关注", "summary": "本次体检发现代谢相关多项异常，包括血压偏高、血脂升高、血糖偏高及轻度脂肪肝，需积极改善生活方式并尽快就医评估。", "key_findings": [ { "category": "心血管/代谢", "finding": "血压142/90mmHg，达高血压1级标准；TG、TC均升高；空腹血糖6.2mmol/L（糖耐量减低范围）", "severity": "中度异常", "action_needed": "就医复诊，评估是否需要药物干预" }, { "category": "消化/代谢", "finding": "ALT轻度升高，腹部超声提示轻度脂肪肝", "severity": "轻度异常", "action_needed": "定期监测，控制体重和饮食" } ], "normal_systems": ["血常规正常", "尿常规正常", "胸部CT未见异常"], "priority_actions": [ {"rank": 1, "action": "前往内科/心内科就诊，评估高血压及代谢综合征", "reason": "血压达高血压标准，合并血脂、血糖异常"}, {"rank": 2, "action": "3个月内复查肝功能、血脂、血糖", "reason": "监测代谢指标变化"} ], "lifestyle_advice": ["减少高脂高糖饮食", "每周至少150分钟中等强度有氧运动", "减重至正常BMI范围"], "next_exam_suggestion": "6个月后复查，重点关注：血压、血糖、血脂、肝功能" } ``` **整体解读**：以"【整体解读】"开头，用通俗语言面向受检者说明健康状况和建议。 依赖 ---- ### 运行环境 - Python 3.7+（仅使用标准库，无需额外安装） ### 外部 API - 内部医疗大模型：`https://maas-api.hivoice.cn/v1/chat/completions` 备注 ---- - overall_grade 评定标准：全部正常→优秀；有轻微异常但无需就医→良好；有需就诊异常→需关注；有紧急处理项→需重视 - **发布约束**：示例输入、运行输出均放在 skill 包外（`data/`、`runs/`），skill 目录内仅保留可发布的核心文件


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: freedompixels/cn-random-picker (version 1.2.5)

slug: cn-random-picker name: 随机选择器 version: "1.0.0" author: 千策 # 随机选择器 随机选择器。抽奖抽签神器。 ## 功能 - **随机抽取**：从多个选项中随机选一个 - **多人抽取**：一次抽取多个不重复结果 - **权重模式**：每个选项可设置不同权重 - **去重保证**：已抽中的不会重复抽到 - **公平模式**：使用secrets模块确保密码学安全随机 ## 安装要求 - Python 3.6+ - 无外部依赖 ## 使用方法 ```bash # 基本随机选择 python3 scripts/random_picker.py "张三,李四,王五,赵六" # 抽取多个 python3 scripts/random_picker.py "A,B,C,D,E" --count 2 # 带权重 python3 scripts/random_picker.py "一等奖:1,二等奖:3,三等奖:6" ``` ## 示例 输入：`张三,李四,王五` 输出：`🎉 抽中: 李四` ## 分类 生活工具 ## 关键词 随机, 抽奖, 抽签, lottery, random, picker

path: scripts/random_picker.py
content: #!/usr/bin/env python3
import sys, json, random

def pick(items, count=1):
    items = items.split('\n') if '\n' in items else items.split(',')
    items = [i.strip() for i in items if i.strip()]
    if count >= len(items):
        random.shuffle(items)
        return {'picked': items, 'all': True}
    return {'picked': random.sample(items, count), 'all': False}

if __name__ == '__main__':
    items = sys.argv[1] if len(sys.argv)>1 else 'a,b,c,d,e'
    count = int(sys.argv[2]) if len(sys.argv)>2 else 1
    print(json.dumps(pick(items, count), ensure_ascii=False))


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