OpenClaw/clawhub-security-signals huggingface dataset:
Skill: c0ldsmi1e/email-flows (version 1.0.0)

--- description: "Designs onboarding, activation, and retention email sequences for SaaS. Use when someone wants help with email marketing, drip campaigns, or email automation." user_invocable: true command: email-flows --- You are an email marketing specialist for SaaS products. Help founders design email sequences that onboard, activate, and retain users. **Start by asking these discovery questions (ask all at once, then wait for answers):** 1. What is your product and what does the user journey look like? (signup > onboarding > activation > paid) 2. What's your business model? (freemium, free trial, reverse trial, paid only) 3. What are the key activation milestones? (actions that correlate with retention) 4. Do you currently send any automated emails? If so, which ones? 5. What email tool are you using or considering? 6. What's your biggest email challenge? (low open rates, poor trial conversion, churn, etc.) **After receiving answers, provide tailored email sequences with these sections:** ### Email Strategy Overview - Which email flows to prioritize based on their stage and biggest gaps - Behavioral vs. time-based triggers - Key metrics to track per flow ### Onboarding Email Sequence Design a 5-7 email onboarding sequence: - Email 1: Welcome + single most important next step - Email 2: Guide to key activation action - Email 3: Social proof / success story - Email 4: Feature highlight (based on what they haven't tried) - Email 5: Check-in / offer help - Include subject lines, timing, and behavioral triggers for each ### Trial-to-Paid Conversion Sequence If applicable, design a conversion sequence: - Reminder of trial expiration - Value recap (what they've accomplished) - Urgency/scarcity (if appropriate) - Comparison of free vs. paid ### Retention & Re-engagement - Win-back sequence for inactive users - Feature announcement template - Feedback request email ### Email Best Practices - Subject line formulas that work for SaaS - Optimal send times and frequency - Plain text vs. designed emails - Personalization and segmentation tips ### Action Plan Numbered checklist to implement their first email flow. ### Further Reading - The SaaS Email Marketing Playbook: https://saasplaybook.co/ - Email Marketing for SaaS 101: https://blog.beehiiv.com/p/email-marketing-for-saas - Lifecycle email planning worksheets: https://userlist.com/worksheets/ - How to Build High-Converting SaaS Email Onboarding Sequences: https://www.reddit.com/r/SaaS/comments/1b7l04k/how_to_build_highconverting_saas_email_onboarding/ - Onboarding Email concepts (Figma): https://www.figma.com/community/file/1207279330099591674/onboarding-emails-25-concepts - Behavior-based Email Marketing for B2B SaaS: https://encharge.notion.site/Course-Behavior-based-Email-Marketing-for-B2B-SaaS-18373064882b80ee84dffbe869883eab - How PostHog built their onboarding email flow: https://posthog.com/blog/how-we-built-email-onboarding


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linuszz/kpc-comb (version 1.0.0)

--- name: kpc-comb description: "Visualize Key Purchase Criteria importance and performance. Use for competitive positioning and product strategy." --- # Key Purchase Criteria (KPC) Comb ## Metadata - **Name**: kpc-comb - **Description**: Key purchase criteria importance vs performance chart - **Triggers**: KPC, key purchase criteria, buying factors, decision criteria ## Instructions Analyze key purchase criteria for $ARGUMENTS to identify competitive gaps. ## Framework ### The Comb Chart ``` Importance │ 10 ┤ ╭───╮ ╭───╮ │ ╭───╮│ │╭───╮│ │ 8 ┤ │ ││ ││ ││ │ ╭───╮ │ │ ││ ││ ││ │╭───╮│ │ 6 ┤╭───╮│ ││ ││ ││ ││ ││ │ ││ ││ ││ ││ ││ ││ ││ │ 4 ┤│ ││ ││ ││ ││ ││ ││ │ ││ ││ ││ ││ ││ ││ ││ │ 2 ┤│ ││ ││ ││ ││ ││ ││ │ │└───┘└───┘└───┘└───┘└───┘└───┘└───┘ └────────────────────────────────── P Q R S T U V Performance ``` ### KPC Categories | Category | Examples | |----------|----------| | **Product** | Features, quality, reliability | | **Price** | Total cost, value, ROI | | **Service** | Support, delivery, warranty | | **Brand** | Reputation, trust, social proof | | **Convenience** | Availability, ease of use, speed | ## Output ``` ## Key Purchase Criteria: [Product/Category] ### Criteria Analysis | Criterion | Importance (1-10) | Our Performance | Competitor A | Gap | |-----------|-------------------|-----------------|--------------|-----| | Price | 9 | 6 | 8 | -2 | | Quality | 8 | 9 | 7 | +2 | | Features | 7 | 7 | 8 | -1 | | Support | 6 | 5 | 6 | -1 | | Brand | 5 | 8 | 9 | -1 | | Speed | 4 | 9 | 7 | +2 | ### Comb Visualization ``` High Importance, Low Performance (Fix Priority): ┌─────────────────┐ │ PRICE (9 vs 6) │ ← Gap: -2 └─────────────────┘ High Importance, High Performance (Maintain): ┌─────────────────┐ │ QUALITY (8 vs 9)│ ← Advantage: +2 └─────────────────┘ ``` ### Strategic Implications **Strengths (Maintain):** 1. Quality - Strong competitive advantage 2. Speed - Differentiator in market **Weaknesses (Improve):** 1. Price - Most important criterion, underperforming 2. Support - Gap vs. competitors **Opportunities:** 1. Price repositioning 2. Support enhancement ### Action Plan | Priority | Criterion | Action | Impact | |----------|-----------|--------|--------| | 1 | Price | Cost reduction / value messaging | High | | 2 | Support | Investment in service | Medium | ``` ## Tips - Focus on high importance criteria first - Weight by customer segment if needed - Update regularly as market evolves - Validate with customer research


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: araa47/local-stt (version 1.0.0)

--- name: local-stt description: Local STT with selectable backends - Parakeet (best accuracy) or Whisper (fastest, multilingual). metadata: {"openclaw":{"emoji":"🎙️","requires":{"bins":["ffmpeg"]}}} --- # Local STT (Parakeet / Whisper) Unified local speech-to-text using ONNX Runtime with int8 quantization. Choose your backend: - **Parakeet** (default): Best accuracy for English, correctly captures names and filler words - **Whisper**: Fastest inference, supports 99 languages ## Usage ```bash # Default: Parakeet v2 (best English accuracy) ~/.openclaw/skills/local-stt/scripts/local-stt.py audio.ogg # Explicit backend selection ~/.openclaw/skills/local-stt/scripts/local-stt.py audio.ogg -b whisper ~/.openclaw/skills/local-stt/scripts/local-stt.py audio.ogg -b parakeet -m v3 # Quiet mode (suppress progress) ~/.openclaw/skills/local-stt/scripts/local-stt.py audio.ogg --quiet ``` ## Options - `-b/--backend`: `parakeet` (default), `whisper` - `-m/--model`: Model variant (see below) - `--no-int8`: Disable int8 quantization - `-q/--quiet`: Suppress progress - `--room-id`: Matrix room ID for direct message ## Models ### Parakeet (default backend) | Model | Description | |-------|-------------| | **v2** (default) | English only, best accuracy | | v3 | Multilingual | ### Whisper | Model | Description | |-------|-------------| | tiny | Fastest, lower accuracy | | **base** (default) | Good balance | | small | Better accuracy | | large-v3-turbo | Best quality, slower | ## Benchmark (24s audio) | Backend/Model | Time | RTF | Notes | |---------------|------|-----|-------| | Whisper Base int8 | 0.43s | 0.018x | Fastest | | **Parakeet v2 int8** | 0.60s | 0.025x | Best accuracy | | Parakeet v3 int8 | 0.63s | 0.026x | Multilingual | ## openclaw.json ```json { "tools": { "media": { "audio": { "enabled": true, "models": [ { "type": "cli", "command": "~/.openclaw/skills/local-stt/scripts/local-stt.py", "args": ["--quiet", "{{MediaPath}}"], "timeoutSeconds": 30 } ] } } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rogue-agent1/httpstat (version 1.0.0)

--- name: httpstat description: Pretty HTTP response statistics with timing waterfall. Use when asked to check a URL's response time, debug slow requests, measure TTFB, or get HTTP timing breakdown. Like curl -v but readable. Zero dependencies. --- # httpstat ⚡ Pretty HTTP timing statistics. Shows DNS, TCP, TLS, TTFB, and transfer times. ## Usage ```bash # Basic timing python3 scripts/httpstat.py https://example.com # With headers python3 scripts/httpstat.py https://api.github.com -H "Accept: application/json" # POST request python3 scripts/httpstat.py https://httpbin.org/post -X POST -d '{"key":"value"}' ``` ## Output Shows a visual waterfall of: DNS Lookup → TCP Connection → TLS Handshake → Server Processing → Content Transfer


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: alexmayanjun-collab/db-table-compare (version 1.0.0)

--- name: db-table-compare description: 对比两个数据库（MySQL vs ODPS）的表字段差异，生成 HiveSQL ALTER TABLE 和 DataX JSON 格式 tags: [database, compare, mysql, odps, schema, diff, datax] --- # 数据库表字段对比技能 ## 功能 对比两个数据库的表结构差异，生成： 1. HiveSQL ALTER TABLE 语句 2. DataX JSON 格式（get_json_object 提取字段） ## 触发词 - "对比 [表名] 字段" - "表结构差异" - "MySQL vs ODPS [表名]" - "生成 alter table" - "生成 datax json" - "字段差异" ## 支持的数据库 | 数据库 | 类型 | 连接方式 | |--------|------|----------| | TH DataWorks | ODPS/MaxCompute | SSH + odpscmd | | PH DataWorks | ODPS/MaxCompute | SSH + odpscmd | | TH FIN | MySQL | Python pymysql | | TH FLE | MySQL | Python pymysql | | TH SAP | MySQL | Python pymysql | ## 执行流程 1. **确定数据源** - 解析表名和环境 2. **查询表结构** - DESC / SHOW CREATE TABLE 3. **对比字段** - 找出差异 4. **生成输出** - ALTER TABLE + DataX JSON ## 输出格式 ### 1. ALTER TABLE（新增字段） ```sql ALTER TABLE dwd_xxx_di ADD COLUMNS ( field_name BIGINT COMMENT '注释' ); ``` ### 2. DataX JSON（字段提取） ```json get_json_object(values, '$.field_name') as field_name, ``` ## 使用示例 ### 示例：对比 TH FIN 表 **用户：** "对比 th-fin 的 ka_accounts_receivable_detail_c3 和 dwd_fin_ka_accounts_receivable_detail_c3_di" **执行：** ```bash # 1. 查 MySQL 字段 python3 -c " import pymysql conn = pymysql.connect(host='core-dev-db.fex.pub', port=3306, user='th_dev_fin_rw', password='xxx', database='th_dev_fin') cursor = conn.cursor() cursor.execute('DESC ka_accounts_receivable_detail_c3') for r in cursor.fetchall(): print(r[0], r[1]) conn.close() " # 2. 查 ODPS 字段 ssh datax "cd /mnt/www/addr/th_odpscmd/bin && echo 'DESC dwd_fin_ka_accounts_receivable_detail_c3_di;' | ./odpscmd" ``` **返回：** - 字段对比表 - 差异说明 - ALTER TABLE 语句 - DataX JSON 格式 --- ## 智能处理 1. **自动识别数据源** - `dwd_`, `ads_`, `dim_` 前缀 → ODPS - 其他 → MySQL 2. **自动选择环境** - TH 相关 → TH DataWorks / TH MySQL - PH 相关 → PH DataWorks / PH MySQL - fin → th_dev_fin 3. **字段匹配** - 不区分大小写 ## DataX JSON 格式说明 DataX 数据摄取时，ODPS 字段从 JSON 提取： | 用途 | 格式 | |------|------| | 读取 JSON | `get_json_object(values, '$.字段名')` | | 别名 | `as 字段名` | | 完整行 | `get_json_object(values, '$.字段名') as 字段名,` | ## 注意事项 1. **只读查询** - 只执行 DESC/SHOW CREATE TABLE 2. **不执行 DDL** - 只生成语句，用户确认后再执行 3. **密码保护** - 密码不输出 ## 更新日志 ### 2026-04-21 - ✅ 创建技能 - ✅ 支持 TH FIN vs TH DataWorks 对比 - ✅ 支持生成 ALTER TABLE - ✅ 支持生成 DataX JSON 格式（get_json_object）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leahliang/ai-news-infographics (version 1.0.0)

--- AIGC: ContentProducer: Minimax Agent AI ContentPropagator: Minimax Agent AI Label: AIGC ProduceID: 64389f5483ccf2c5e859bb3c2008f5c8 PropagateID: 64389f5483ccf2c5e859bb3c2008f5c8 ReservedCode1: 30450220751771b398feb11d141c22a5f8d21a49f98cc2859de61cde9012a1e226caa5e0022100f9dacb20758ac8edf6a04b3aa56736260422f5f0f936656b7e6f6256f50d8390 ReservedCode2: 3044022048c1054bd2bb4014e8f25b79f78a360beae6f1df3f8b343c6a17f015594d63680220565e641fc16b1fdb28b7711576d3287854036481cb986939e162b49265baf440 description: 生成AI新闻资讯的Infographics图片。使用场景：(1) 用户要求生成AI热门产品或新闻的图片 (2) 需要近期AI动态的可视化内容 (3) 制作OpenClaw相关的AI资讯图片 (4) 创建社交媒体分享的AI新闻卡片。功能：搜索近期热门AI产品和新闻，筛选与OpenClaw相关的内容，生成新闻杂志风格的Infographics图片。 name: ai-news-infographics --- # AI新闻资讯 Infographics 生成 ## 触发条件 当用户要求以下内容时使用此skill： - 生成AI新闻/产品的图片 - 制作AI资讯infographics - 创建AI动态分享卡片 - 制作OpenClaw相关的资讯图片 ## 工作流程 ### 1. 搜索热门AI新闻 使用 `batch_web_search` 搜索近期AI热门新闻： ```json [ {"cursor": 1, "data_range": "m", "num_results": 10, "query": "AI news 2025 2026 popular products"}, {"cursor": 1, "data_range": "m", "num_results": 10, "query": "OpenClaw AI agent latest news"}, {"cursor": 1, "data_range": "m", "num_results": 10, "query": "AI agent automation tools 2025"} ] ``` ### 2. 筛选与OpenClaw相关的内容 优先保留以下类型的新闻： - OpenClaw/MaxClaw相关动态 - AI Agent/自动化工具 - MCP (Model Context Protocol) 相关 - 生产力AI工具 - 开源AI项目 ### 3. 生成Infographics图片 使用 `image_synthesize` 生成新闻杂志风格的图片。 **Prompt模板（英文）:** ``` Modern AI news infographic magazine style, professional journalism layout. Top section: [新闻标题] Middle section: [关键特性/要点用图标和短文本展示] Bottom section: [相关信息/链接] Visual style: - Clean, editorial magazine design - Bold typography headlines - Minimalist data visualization icons - White/light gray background with accent colors - Professional, news-worthy aesthetic - High contrast text for readability Include these elements: - Magazine-style headline layout - Feature bullet points with icons - Clean section dividers - Subtle gradient accents - Modern sans-serif typography feel ``` **图片规格：** - 分辨率: 2K (2560x1440) 或 4K (3840x2160) - 宽高比: 16:9 (横版) 或 4:3 - 风格: 新闻杂志、简洁专业 ### 4. 输出 将生成的图片保存到 workspace，并通过 CDN 上传获取可分享的链接。 ## 注意事项 - 搜索关键词加入当前年月确保时效性 - 优先选择有具体产品名、功能描述的新闻 - 图片需要清晰可读，避免文字过多 - 如果搜索结果少于5条，扩大搜索范围


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-repo-cleaner (version 1.0.0)

--- name: skylv-git-pruner slug: skylv-git-pruner version: 1.0.0 description: "Safely removes merged and stale Git branches. Identifies branches already on main/master. Triggers: clean branches, remove old branches, delete merged branches, git cleanup." author: SKY-LV license: MIT tags: [git, cleanup, branch-management, devtools] keywords: [git, branch, cleanup, prune, delete] triggers: git pruner, clean branches, delete merged branches --- # Git Branch Pruner ## Overview Safely identifies and removes merged, stale, and orphaned Git branches. ## When to Use - User asks to "clean up branches" or "delete merged branches" - Branch list is too long to manage ## How It Works ### Step 1: Identify main branch git branch --show-current git remote show origin | findstr "HEAD branch" ### Step 2: List all branches git branch -a --format "%(refname:short) %(upstream:short) %(committerdate:short)" ### Step 3: Categorize Merged: already in main/master Stale: no commit in 90+ days Orphaned: no upstream tracking Active: recent commits under 30 days ### Step 4: Safe deletion 1. Always show user what will be deleted first 2. Exclude protected branches: main, master, develop, release/* 3. Exclude branches with unpushed commits 4. Ask for confirmation before deletion ## Output Format Merged branches (safe to delete): feature-old-login -> DELETE bugfix-typo-2024 -> DELETE Protected (do not delete): main <- current develop Commands: git branch -d (safe) or git branch -D (force) Remote: git push origin --delete branch-name


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theshimaw-svg/ai-employee (version 1.0.0)

# AI Employee — Elite Execution Operator Turn your AI into a high-performance employee that executes, not just talks. --- ## 🔥 Identity You are an **elite AI employee**. Your only goal: > Produce real, measurable results as efficiently as possible. You are not an assistant. You are an operator. --- ## ⚡ Core Rules 1. Results > explanations 2. Execution > ideas 3. High ROI actions only 4. Speed over perfection 5. No fluff, no generic advice --- ## 🧠 Thinking System For every task, follow: 1. Objective 2. Breakdown 3. Top 3 actions 4. Execution plan 5. Immediate next step --- ## ⚙️ Behavior - You take initiative - You prioritize impact - You eliminate useless steps - You think in systems --- ## 🚫 Never Do - Over-explain - Give vague advice - Suggest low-value actions --- ## ✅ Output Format ### 🎯 Objective ... ### 🧩 Breakdown ... ### 🔝 Top 3 Actions 1. 2. 3. ### ⚡ Execution Plan ... ### 🚀 Do This Now ... --- ## 🔁 Improvement Loop After every task: - What worked? - What didn’t? - Improve next step --- ## 🚀 Activation "Act as my AI employee: [task]"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: crossservicesolutions/password-protect-pdf (version 1.0.0)

--- name: password-protect-pdf description: Add password protection to a PDF by uploading it to the Solutions API, polling until completion, then returning a download URL for the protected PDF. license: MIT compatibility: agentskills: ">=0.1.0" metadata: category: document-security tags: - pdf - password - encrypt - security - cross-service-solutions provider: Cross-Service-Solutions (Solutions API) allowed-tools: - http - files --- # password-protect-pdf ## Purpose This skill password-protects a PDF by: 1) accepting a PDF file from the user, 2) accepting a password from the user, 3) uploading both to the Solutions API, 4) polling the job status until it is finished, 5) returning the download URL for the password-protected PDF. ## Credentials The API requires an API key used as a Bearer token: - `Authorization: Bearer <API_KEY>` How the user gets an API key: - https://login.cross-service-solutions.com/register - Or the user can provide an API key directly. **Rule:** never echo or log the API key. ## API endpoints Base URL: - `https://api.xss-cross-service-solutions.com/solutions/solutions` Create password-protect job: - `POST /api/32` - `multipart/form-data` parameters: - `file` (PDF-Datei) — required — PDF file - `userPass` (Passwort) — required — string password Get result by ID: - `GET /api/<ID>` When done, the response contains: - `output.files[]` with `{ name, path }` where `path` is a downloadable URL. ## Inputs ### Required - PDF file (binary) - Password (`userPass`, string) - API key (string) ### Optional - None ## Output Return a structured result: - `job_id` (number) - `status` (string) - `download_url` (string, when done) - `file_name` (string, when available) Example output: ```json { "job_id": 321, "status": "done", "download_url": "https://.../protected.pdf", "file_name": "protected.pdf" }


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: qiansonguio/tjweather (version 1.0.3)

--- name: tjweather description: "全球地点天气预报及地理编码分析（TJWeather API）。" homepage: https://api.tjweather.com user-invocable: true metadata: {"openclaw": {"emoji": "🌤️", "requires": {"bins": ["python3"], "env": ["TJWEATHER_API_KEY"]}, "primaryEnv": "TJWEATHER_API_KEY"}} --- # TJWeather Skill 🌤️ 通过 **TJWeather API** 查询全球任意地点的天气预报。本工具不仅提供精准的气象数据，还内置了高可靠性的三级地理编码回退逻辑，确保无论用户输入多么模糊，都能得到妥善的处理。 --- ## ⚠️ 核心配置与准则 > [!IMPORTANT] > **API KEY 获取**：本 Skill 依赖环境变量 `TJWEATHER_API_KEY`。该 Key 已由用户通过 `~/.openclaw/openclaw.json` 完成注入。 > **Agent 禁止行为**：严禁向用户索要或询问 API Key。 > [!TIP] > **预报限制**：测试版本最长支持 **10 天**。若用户查询请求超过此限制，请在回复中礼貌说明，并展示前 10 天的数据。 --- ## 🛠️ 执行流程 (强制逻辑) 作为 Agent，你必须严格按照以下阶段序列执行任务，不得跳步。 ### 阶段一：地理编码 (Geocoding - CRITICAL) 你 **必须且仅能** 使用该内部脚本。**禁止** 使用内置 Web 搜索获取坐标。 ```bash # 执行地理编码查询 python3 {baseDir}/scripts/geocoder.py "{location_query}" ``` #### 🔍 响应处理深度指引 - **状态解析**：解析 JSON 输出中的 `lon`, `lat`, `name`, `source`, `tz`。 - **回退感知 (FALLBACK PROTOCOL)**： - 若 `source` 为 `Internal (Enhanced Fuzzy)`，意味着精确位置无法匹配，已回退到城市级坐标。**你必须在回复的首行明确告知用户**。 - **逻辑示例**：用户查“龙旗广场”，若 Source 为 Internal，回复需包含：“未能找到龙旗广场的精确位置，已自动为您查询所属城市（北京市）的天气。” ### 阶段二：气象分析 (Weather Analysis) 结合获取的坐标与你对该地点**夏令时 (DST)** 的最终裁定，调用分析脚本。 ```bash # 执行天气预测、统计及大势分析 python3 {baseDir}/scripts/tjweather.py {lon} {lat} "{matched_name}" "{original_query}" {fcst_days} {tz} ``` > [!CAUTION] > **时区 (tz) 裁定规则**： > `geocoder.py` 返回的 `tz` 是物理经度转换的参考值。你必须运用你的通用知识，确认该地点当前是否处于夏令时（例如：伦敦在 3-10 月间需在 tz=0 基础上 +1），以确保回复时间线完全符合当地习惯。 --- ## 📝 输出规范 (MANDATORY) > [!IMPORTANT] > **回复必须包含以下三个部分，漏掉任何一项均视为任务失败 (FAILED)：** > 1. **首行自适应提示**：包含查询词、匹配结果及（如果有）回退说明。 > 2. **数据正文**：展示 `tjweather.py` 返回的所有每日统计（气温、风力、降水等）。 > 3. **温润体贴的总结**：在底部追加一段 `📝 总结`。语气需柔和、关怀备至，概括整体趋势并提供生活建议。 --- ## 完整示例 (Success with Precision) **用户：** "北京海淀区上地街道未来3天天气" **你的逻辑：** 1. 调用 `geocoder.py "北京海淀区上地街道"` -> 返回 `source: Nominatim`, `name: 上地街道, 海淀区, 北京市, 100193, 中国`。 2. 确认由于是首选服务成功，无需回退提示。 3. 调用 `tjweather.py` 获取数据。 **期望输出：** > 您查询的 北京海淀区上地街道 匹配到 上地街道, 海淀区, 北京市, 100193, 中国，坐标：116.29, 40.04 > > 📅 2026年4月10日 星期五 > 🌡️ 气温: 7.5°C ~ 26.4°C > ... (统计数据) > > 📝 总结: 上地街道这两天天气晴朗舒适，非常适合户外活动。早晚温差较大，建议您早出晚归时增加一件外套。祝您生活愉快！😊 --- > [!NOTE] > 遵循本协议将使你的查询表现出卓越的专业度与拟人化的同理心。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tobewin/latex-studio (version 1.0.5)

--- name: latex-studio description: LaTeX document generator for academic papers and technical reports. Use when user needs to create professional academic papers, theses, or technical documents with math formulas, tables, and citations. Generates publication-ready .tex files compatible with Overleaf and all LaTeX compilers. version: 1.0.5 license: MIT-0 metadata: {"openclaw": {"emoji": "📐", "requires": {"bins": ["python3"], "env": []}}} --- # LaTeX Studio Professional LaTeX document generator for academic papers and technical reports. ## Features - 📄 **Academic Papers**: Standard paper formatting - 📐 **Math Formulas**: Full LaTeX math support - 📑 **Tables**: Professional table layouts - 📝 **Citations**: Reference management - 📊 **Charts**: Integration with data - ✅ **Compatible**: Works with Overleaf, TeX Live, MiKTeX ## Main Use Case LaTeX is primarily used for academic paper writing. This skill focuses on English language support for maximum compatibility and professional quality. ## Supported Document Types | Type | Use Case | |------|----------| | Research Paper | Academic publication | | Technical Report | Documentation | | Thesis | Graduate work | | White Paper | Business/technical | | Conference Paper | IEEE/ACM format | ## Trigger Conditions - "Write a paper" / "Create a LaTeX document" - "Generate academic paper" - "LaTeX report" / "Research paper" - "latex-studio" --- ## LaTeX Template ```latex \documentclass[12pt, a4paper]{article} % Packages \usepackage[margin=2.5cm, headheight=14pt]{geometry} \usepackage{amsmath, amssymb} \usepackage{graphicx} \usepackage{hyperref} \usepackage{booktabs} \usepackage{xcolor} \usepackage{colortbl} \usepackage{enumitem} \usepackage{fancyhdr} % Colors \definecolor{primary}{RGB}{30, 60, 114} \definecolor{secondary}{RGB}{100, 100, 100} % Header/Footer \pagestyle{fancy} \fancyhf{} \fancyhead[L]{\small\color{secondary}Document Title} \fancyfoot[C]{\thepage} % Title \title{\color{primary}\textbf{Paper Title}} \author{Author Name} \date{\today} \begin{document} \maketitle \begin{abstract} Abstract text goes here. \end{abstract} \tableofcontents \newpage \section{Introduction} Introduction content... \section{Methodology} Method content... \begin{table}[h] \centering \renewcommand{\arraystretch}{1.3} \begin{tabular}{|l|c|c|c|} \hline \rowcolor{primary} \textcolor{white}{\textbf{Header1}} & \textcolor{white}{\textbf{Header2}} & \textcolor{white}{\textbf{Header3}} \\ \hline Row1 & Data1 & Data2 \\ \hline \end{tabular} \caption{Table Title} \label{tab:example} \end{table} \section{Results} Results content... \section{Conclusion} Conclusion content... \end{document} ``` --- ## Usage Examples ``` User: "Write a research paper about AI" Agent: Generate LaTeX document with proper formatting User: "Create a technical report" Agent: Generate professional report template ``` --- ## Notes - Generates .tex files - Compatible with Overleaf and all LaTeX compilers - Requires LaTeX environment to compile to PDF - Focuses on English language support


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