OpenClaw/clawhub-security-signals huggingface dataset:
Skill: izsook/magyar-formatter (version 1.0.0)

--- name: magyar-formatter description: Magyar dátum, szám, pénz formázás: helyi formátumok alkalmazása (YYYY. MM. DD., tizedesvessző, Ft). Use when: (1) magyar dátum formátum kell, (2) számok magyar formázása (tizedesvessző), (3) pénz összegek Ft-ban, (4) magyar telefonszám, cím formátum. --- # Magyar Formázás ## Áttekintés Magyar helyi formátumok alkalmazása: dátum, számok, pénzek, telefonszámok, és címek. ## Mikor használd - **Dátum formázás**: magyar dátum (YYYY. MM. DD.) - **Szám formázás**: tizedesvessző, ezres elválasztó - **Pénz formázás**: Ft szimbólum, helyi valuta - **Telefonszám**: +36 XX XXX XXXX formátum - **Cím formátum**: magyar cím szerkezet ## Formátum szabályok ### 1. Dátum ``` Magyar: 2026. március 18. Rövid: 2026. 03. 18. ISO: 2026-03-18 (konvertáld magyarra) ``` ### 2. Számok ``` Angol: 1,234.56 Magyar: 1 234,56 (ezres szóköz, tizedesvessző) ``` ### 3. Pénz ``` Angol: $1,234.56 Magyar: 1 234,56 Ft Nagy összeg: 1,2 millió Ft ``` ### 4. Telefonszám ``` Nemzetközi: +36 30 123 4567 Helyi: (30) 123-4567 ``` ### 5. Cím ``` 1234 Budapest, Petőfi utca 12. fsz. 3. irányítószám város, utca házszám emelet ajtó ``` ## Scriptek - **scripts/format_date.py** – dátum magyar formázás - **scripts/format_number.py** – számok formázása - **scripts/format_currency.py** – pénz Ft-ban - **scripts/format_phone.py** – telefonszám formázás ## Példakód ```python def format_hungarian_date(date): return f"{date.year}. {date.month:02d}. {date.day:02d}." def format_hungarian_number(num): return f"{int(num):,}".replace(',', ' ') + f",{abs(num - int(num)):.2f}"[2:] def format_forint(amount): return f"{format_hungarian_number(amount)} Ft" ``` ## Referenciák - **references/date-formats.md** – dátum formátumok részletesen - **references/currency.md** – Ft konverziók, váltások - **references/address-format.md** – magyar cím szerkezet ## Jegyzetek - Tizedesvessző kritikus (nem pont!) - Ezres elválasztó: szóköz vagy keskeny szóköz - Ft mindig szám után, szóközzel - Dátum pontok kötelezőek


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: robinyves/ai-tip-from-hwchase17 (version 1.0.0)

# AI Tip from @hwchase17 ## Description Automatically generated AI learning skill from curated web and social media sources. ## Steps 1. New in LangChain: You can now easily create custom agents with structured outputs! 2. from langchain_core.pydantic_v1 import BaseModel, Field 3. class Joke(BaseModel): 4. setup: str = Field(description="question to set up a joke") 5. punchline: str = Field(description="answer to resolve the joke") 6. llm.with_structured_output(Joke) ## Code Examples ```python from langchain_core.pydantic_v1 import BaseModel, Field class Joke(BaseModel): setup: str = Field(description="question to set up a joke") punchline: str = Field(description="answer to resolve the joke") llm.with_structured_output(Joke) ``` ## Dependencies - Python 3.8+ - Relevant libraries (see code examples)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bylikai/job-status (version 1.0.1)

--- name: job-status description: | HPC/AI job status query skill. Fetches job status information by jobId from a specific API endpoint and parses the jobState field from the JSON response. **Use this skill when**: (1) Need to query HPC/AI computing job status (2) User provides a jobId parameter (3) Need to get job status (e.g., RUNNING, COMPLETED, FAILED) (4) Need to monitor computing job progress alwaysActive: false metadata: openclaw: emoji: "⚡" category: "monitoring" author: "OpenClaw Assistant" version: "1.0.0" --- # Job Status Query Skill ## 🎯 Function Overview This skill queries HPC/AI computing job status information. It accesses the API with the provided jobId, retrieves detailed job information, and extracts the key status field. ## 🔧 Technical Implementation - **API Endpoint**: `http://www.aicnic.cn/jobs/api/jobs/infos/hpcai28/{jobId}` - **Response Format**: JSON - **Key Field**: `data.jobState` - **Error Handling**: Network timeout, API errors, JSON parsing errors ## 📋 Quick Start ### Method 1: Natural Language Call ``` User: Query job status, jobId is 15000 Assistant: Querying job 15000 status... ``` ### Method 2: Parameterized Call ```json { "jobId": "15000" } ``` ## 🚀 Usage Scenarios ### Scenario 1: Query Single Job Status ```json { "jobId": "15000" } ``` ### Scenario 2: Batch Job Status Query User: Query status for jobs 15000, 15001, 15002 ### Scenario 3: Monitor Job Completion User: Check if job 15000 is completed ## 🔍 Return Status Description Common `jobState` values: - **PENDING**: Job is queued - **RUNNING**: Job is running - **COMPLETED**: Job completed successfully - **FAILED**: Job execution failed - **CANCELLED**: Job was cancelled - **TIMEOUT**: Job timed out ## ⚠️ Notes 1. **jobId Format**: Must be a valid job ID 2. **Network Connectivity**: Must be able to access `www.aicnic.cn` 3. **API Limits**: Be aware of API rate limits 4. **Error Handling**: Network errors or invalid jobId will return appropriate error messages ## 📊 Example Responses Successful response: ```json { "success": true, "jobId": "15000", "status": "COMPLETED", "details": { "jobState": "COMPLETED", "endTime": "2024-12-09T09:30:10", "id": 20452 }, "message": "Job query successful", "timestamp": "2026-03-19T06:30:00Z" } ``` Error response: ```json { "success": false, "jobId": "99999", "error": "Job not found or access failed", "message": "API returned error or network connection failed", "timestamp": "2026-03-19T06:30:30Z" } ``` ## 🔗 Related Skills - **System Monitoring**: Can be combined with system monitoring skills - **Notification Alerts**: Can trigger notifications on job status changes - **Log Recording**: Record job status history


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: merjua14/cold-email-outreach (version 1.0.0)

--- name: cold-email-outreach description: Automated cold email outreach pipeline using Resend API. Builds prospect lists from government databases or web scraping, enriches with contact info, sends personalized cold emails with drip follow-ups, and tracks responses. Use when setting up cold email campaigns, building lead lists, automating email outreach, prospecting businesses, sending drip sequences, or managing email deliverability. --- # Cold Email Outreach — Automated Prospecting & Drip Pipeline Build lists, enrich contacts, send personalized cold emails, follow up automatically. ## Pipeline Overview 1. **Source** — Pull leads from government databases, web scraping, or CSV import 2. **Enrich** — Find emails via website scraping, Google search, or enrichment APIs 3. **Personalize** — Generate custom email copy per lead using templates + variables 4. **Send** — Deliver via Resend API with rate limiting and domain rotation 5. **Follow Up** — Automated drip sequences (Day 3, Day 7) 6. **Track** — Log opens, replies, bounces to Google Sheets or CSV ## Requirements - **Resend API key** — `RESEND_API_KEY` (free tier: 100 emails/day) - **Verified sending domain** — SPF + DKIM + DMARC configured - **Google Sheets OAuth** (optional) — for lead tracking ## Quick Start ```bash export RESEND_API_KEY=your_key # Send from CSV list node scripts/send-campaign.js --list leads.csv --template templates/intro.txt --from "[REDACTED_SECRET]" # Dry run (no emails sent) node scripts/send-campaign.js --list leads.csv --template templates/intro.txt --dry-run # Send follow-ups node scripts/send-followups.js --campaign campaign-2026-03-12 ``` ## Email Templates Templates use `{variable}` placeholders: ``` Subject: {subject} Hi {first_name}, {body} Best, {sender_name} ``` Available variables: `{first_name}`, `{business_name}`, `{city}`, `{industry}`, `{specific_issue}`, `{sender_name}`, `{subject}` ## Deliverability Rules - Max 25 emails per domain per day (cold outreach) - Warm up new domains: start at 5/day, increase by 5 every 3 days - Always include physical address (CAN-SPAM) - Include unsubscribe mechanism - Rotate sending domains if scaling past 50/day - Remove bounced emails immediately - Honor unsubscribes within 24 hours ## Scripts - `scripts/send-campaign.js` — Main campaign sender with rate limiting - `scripts/send-followups.js` — Drip follow-up sender - `scripts/enrich-list.js` — Email enrichment from websites ## References - `references/templates.md` — Proven email templates with open rates - `references/deliverability.md` — Domain setup and spam prevention guide


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nimblemarketingapex-netizen/metrika-analysis (version 1.0.1)

--- name: metrika-analysis description: Используется для анализа данных из API Яндекс.Метрики, когда пользователь просит проанализировать трафик, конверсии, поведение посетителей или отчет по сайту. metadata: openclaw: emoji: "📈" --- # Metrika Analysis Бот **не публикует данные**, **не изменяет счетчики без разрешения** и экономит токены. ## Когда использовать Используй этот скилл, когда пользователь: - просит проанализировать Яндекс.Метрику - хочет получить отчет по трафику или конверсиям - спрашивает про поведение посетителей сайта - хочет найти страницы с высоким процентом отказов ## Примеры запросов - Проанализируй данные Яндекс Метрики - Где у меня высокий bounce rate? - Какие каналы трафика дают больше конверсий? ## Шаги ## Шаги 1. Проверить наличие METRIKA_TOKEN и COUNTER_ID 2. Получение данных: - Использовать METRIKA_TOKEN и COUNTER_ID - Получить метрики: посещения, глубина просмотра, отказы, конверсии 3. Анализ данных: - Определить топ источники трафика и их конверсии - Найти страницы с высоким bounce rate - Выявить слабые места в воронке конверсии 4. Формирование рекомендаций: - Улучшение целевых страниц и контента - Оптимизация страниц с высоким bounce rate - Краткие гипотезы по увеличению конверсий


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

--- name: "tailwind" version: "1.0.0" description: "Tailwind reference tool. Use when working with tailwind in frontend contexts." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [tailwind, frontend, dev, reference, cli] category: "frontend" --- # Tailwind Tailwind reference tool. Use when working with tailwind in frontend contexts. ## When to Use - Working with tailwind and need quick reference - Looking up frontend standards or best practices for tailwind - Troubleshooting tailwind issues - Need a checklist or guide for tailwind tasks ## Commands ### `intro` ```bash scripts/script.sh intro ``` Overview and core concepts ### `quickstart` ```bash scripts/script.sh quickstart ``` Getting started guide ### `patterns` ```bash scripts/script.sh patterns ``` Common patterns and best practices ### `debugging` ```bash scripts/script.sh debugging ``` Debugging and troubleshooting ### `performance` ```bash scripts/script.sh performance ``` Performance optimization tips ### `security` ```bash scripts/script.sh security ``` Security considerations ### `migration` ```bash scripts/script.sh migration ``` Migration and upgrade guide ### `cheatsheet` ```bash scripts/script.sh cheatsheet ``` Quick reference cheat sheet ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nufeng1999/chinese-voice-skill (version 1.0.4)

--- name: Chinese Voice Skill description: 使用微软 Edge TTS 生成高质量中文语音，默认使用 XiaoxiaoNeural 语音。当用户需要语音回复时自动触发。 read_when: - 生成中文语音 - 创建语音回复 - 使用 TTS 合成语音 - 需要语音回复 - 中文语音对话 - edge-tts 语音生成 - 语音对话 - TTS 转换 - 发送语音 - 语音合成 - 开启语音 - 开始语音 - 用语音回复 metadata: {"clawdbot":{"emoji":"🎤","priority":10,"requires":{"bins":["python","pip"]}}} allowed-tools: Bash(edge-tts:*) --- # Edge TTS 中文语音合成技能 ## 概述 使用微软 Edge TTS 生成高质量中文语音，默认使用 XiaoxiaoNeural 语音。当用户需要语音回复时自动触发。 ## 能力 - **语音合成**: 将文本转换为高质量的中文语音 - **默认语音**: zh-CN-XiaoxiaoNeural（甜美自然的中文语音） - **备用方案**: 当 edge-tts 不可用时，自动降级到系统自带的 System.Speech - **平台支持**: - ✅ QQ 发送（<qqmedia> wav 格式） ## 使用方式 ### 基本用法 当用户表达需要语音回复时（如"生成语音"、"用语音告诉我"、"用语音说"），系统会自动： 1. 检测 edge-tts 是否可用 2. 使用 Edge TTS + XiaoxiaoNeural 生成语音 3. 发送 `<qqmedia>输出文件.wav</qqmedia>` ### 前置条件 1. **Python 3.7+** 已安装（用于运行 edge-tts） 2. **pip** 可用 3. **QQ 通道** 已配置（用于发送 wav 格式语音） ## 配置选项 ### 语音选择 - 默认: `zh-CN-XiaoxiaoNeural` - 其他可选微软语音: - `zh-CN-YunxiNeural`（沉稳） - `zh-CN-XiaoyiNeural`（温柔） - `zh-CN-YunyangNeural`（磁性） ### 语音参数 - `Rate`: 语速（默认 0，可调整 -5 到 5） - `Volume`: 音量（默认 1.0，范围 0 到 1） ## 技术实现 ### 命令行调用 使用 Python 的 edge_tts 模块： ```bash python -m edge_tts --voice "zh-CN-XiaoxiaoNeural" --text "要转换的文本" --write-media "输出文件.wav" ``` ### 安装 edge-tts 如果未安装，使用 pip 安装： ```bash pip install --user edge-tts -i https://mirrors.aliyun.com/pypi/simple/ ``` ### 备用语音方案 如果 edge-tts 不可用，系统会自动使用系统自带的中文 TTS。 ## 输出格式 - **文件格式**: WAV（微软 TTS 标准格式） - **采样率**: 24000 Hz - **声道**: 单声道 - **位深度**: 16-bit ## 注意事项 1. **网络要求**: edge-tts 需要访问微软服务器 2. **文件大小**: 生成的语音文件通常在 50-200 KB 3. **自动清理**: 临时文件可能由系统自动清理 4. **备用方案**: 当 edge-tts 不可用时自动降级，不影响基本功能 ## 示例 ``` 用户: 生成语音，"你好！这是一段测试文本。" AI: [调用 edge-tts 生成语音] [发送 <qqmedia>C:\Users\ADMINI~1\AppData\Local\Temp\xxx.wav</qqmedia>] [提示语音生成完成] ``` ## 故障排除 ### edge-tts 未安装 使用 pip 安装 edge-tts： ```bash pip install --user edge-tts -i https://mirrors.aliyun.com/pypi/simple/ ``` ### edge-tts 路径问题 检查 edge-tts 是否在 PATH 中： ```powershell Get-Command edge-tts.exe -ErrorAction SilentlyContinue ``` ### 语音未生成 1. 检查网络连接 2. 查看错误信息 3. 确认 QQ 通道已配置 4. 确认 ffmpeg 已安装（如需转换格式）

path: _meta.json
content: ownerId: kn78tpnrwtfvbsqdzjzfsd866982xe4q
slug: chinese-voice-skill
version: 1.0.2
publishedAt: 1773638015012

path: .clawhub/origin.json
content: version: 1
registry: https://clawhub.ai
slug: chinese-voice-skill
installedVersion: 1.0.2
installedAt: 1773638401669

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iamhankai/beijing-house-pro (version 1.0.8)

--- name: beijing_house description: 查询北京住宅小区的基础信息（房价/区域/交通），并通过官方验证渠道核实学区划片信息。 --- # 北京住宅全信息助手 (房价/学区/交通) ## 🛠 环境要求与安装 本技能涉及网页抓取与浏览器自动化。使用前请确保安装以下依赖： ### 1. Python 依赖 ```bash pip install requests beautifulsoup4 selenium ``` ### 2. 系统环境 (关键) - **Chrome 浏览器**：需安装 Chrome 或 Chromium。 - **ChromeDriver**：需下载与浏览器版本匹配的 ChromeDriver，并确保其在系统的 PATH 路径中。 - **网络权限**：需能够访问 `bj.ke.com` (房价) 以及北京各区教委官网。 --- ## 📖 核心指令与策略 ### 1. 基础信息查询 (价格、年份、交通) - **执行指令**：`python scripts/query_info.py --name "<小区名>"`。 - **兜底策略**：若脚本因反爬限制失败，**允许**使用 `web_search` 搜索类似 `"北京 [小区名] 均价 建成年份"`。 ### 2. 官方学区划片 (严格多步策略) **严禁直接引用搜索引擎中的非官方学区汇总信息。必须通过以下闭环逻辑验证：** #### A. 海淀区 (17学区制) 海淀区划片复杂，本技能采用“搜索定位 + 官方名录核实”的双保险模式： 1. **第一步**：运行 `python scripts/query_school_browser.py --district "海淀" --name "<小区名>"`。 2. **第二步**：由于不再硬编码小区对应关系，脚本会返回 17 个官方学区名单并提示 `info_required`。 3. **第三步**：Agent 必须使用 `web_search` 搜索：`"海淀区 [小区名] 属于哪个学区"`（例如：上地学区、中关村学区）。 4. **第四步**：确定学区名后，再次运行脚本，`--name` 参数传入确定的**学区名称**（如 `上地学区`），即可获得该学区官方管辖的全部公办中小学名单。 #### B. 朝阳区 - **执行指令**：`python scripts/query_school_browser.py --district "朝阳" --name "<小区名>"`。 - **逻辑**：后端会自动运行无头浏览器访问朝阳教委官网实时抓取。 #### C. 东城/西城/其他区 - 脚本会返回官方查询入口 URL。Agent 需调用 `browser` 系统工具引导用户手动完成登录/扫码验证。 --- ## ⚠️ 安全与数据时效性 1. **拒绝猜测**：严禁在未得到官方系统或学区名录确认的情况下向用户回复学区信息。 2. **人工介入**：对于需要个人登录的区域，Agent 必须提示用户手动操作，不得尝试自动化登录。 3. **政策变动**：所有返回结果必须附带声明：“信息来源于 2025 年官方公开资料，具体划片以当年度教委发布的最新《招生简章》为准。” --- ## 注意事项 - 仅支持北京地区。 - 严禁删除 `haidian_districts.json` 文件。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: deadblue22/feishu-msg-reader (version 1.0.0)

--- name: feishu-message description: | Fetch Feishu message content by message_id, with optional thread context. Activate when: needing to read the content of a specific Feishu message by its message_id, needing thread context around a message, or encountering "[Interactive Card]" placeholder text. Handles all msg_types: text, post, interactive (fallback only), image, merge_forward. --- # Feishu Message Fetcher Fetch any Feishu message content by `message_id` using the IM API, with optional thread context. ## Known Limitation **Interactive cards** (`msg_type: interactive`): The Feishu GET message API (`/im/v1/messages/{id}`) only returns **fallback/degraded text** in `body.content`, not the full card JSON. This is a Feishu platform limitation — there is no API to retrieve the rendered card structure after sending. **Workaround**: Use `--thread` to fetch the entire thread context. The interactive card is usually a reply to a text/post message that contains the actual content. Reading the thread gives you the full picture. ## Usage ```bash # Fetch a single message python3 scripts/fetch_message.py <message_id> # Fetch with thread context (root message + all replies in thread) python3 scripts/fetch_message.py <message_id> --thread # Raw API response python3 scripts/fetch_message.py <message_id> --raw ``` ## Auth Automatic — reads `appId`/`appSecret` from `~/.openclaw/openclaw.json`. Alternatively set `FEISHU_APP_ID` + `FEISHU_APP_SECRET` env vars, or pass `--token <tenant_access_token>`. ## Output JSON with: `message_id`, `msg_type`, `sender_id`, `sender_type`, `chat_id`, `create_time`, `root_id`, `parent_id`, `content` (parsed). With `--thread`: adds `thread` array (all messages in the same thread, sorted chronologically) and `thread_count`. ## Typical Workflow When you encounter `[Interactive Card]` in a replied-to message: 1. Get the message_id from inbound metadata (`has_reply_context`, parent message info) 2. Run `fetch_message.py <parent_message_id> --thread` 3. The thread context will contain the text/post messages with actual content 4. Use that content to fulfill the user's request


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yusaku-0426/health-summary (version 1.0.0)

--- name: health-summary description: Generate daily/weekly/monthly health summaries with nutrition totals, target comparisons, and trends. metadata: { "openclaw": { "emoji": "📊", "requires": { "scripts": ["scripts/health_summary.js"] }, }, } --- # health-summary 日次・週次・月次の健康サマリーを生成する。 ## Script ```bash # 今日のサマリー node scripts/health_summary.js today # 特定日のサマリー node scripts/health_summary.js today --date=2026-02-24 # 週間サマリー（直近7日） node scripts/health_summary.js week # 月次サマリー（当月） node scripts/health_summary.js month ``` ## 出力形式 JSON で以下を返す: - `totals`: カロリー、P/C/F、食物繊維、糖質、ナトリウム、飽和脂肪、水分、運動時間 - `targets`: 目標値（config/health_targets.json から） - `deltas`: 目標との差分 - `latest_weight`: 直近の体重 - `latest_sleep`: 直近の睡眠時間 ## ユーザーへの返信フォーマット サマリーを返す際は以下の形式: ``` 📊 今日の健康サマリー (2026-02-24) 🍽 栄養: 1,500kcal / P 120g / C 180g / F 40g 目標比: kcal -700 / P -45g / C -95g / F -15g 🥦 詳細栄養: Fiber 15g / Sugar 30g / Na 1,800mg / SatFat 10g 💧 水分: 1,500ml（目標: 2,000ml） 🏃 運動: 30分 ⚖️ 体重: 70kg / 😴 睡眠: 7h ``` ## 目標設定 目標値は `config/health_targets.json` から読み込む。デフォルト（lean_mass_gain モード）: - kcal: 2,200 / P: 165g / C: 275g / F: 55g - 水分: 2,000ml / 食物繊維: 25g / 糖質上限: 50g - ナトリウム上限: 2,300mg / 飽和脂肪上限: 16g


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ahsbnb/file-to-link (version 1.0.0)

--- name: file-to-link description: Uploads a local file to Qiniu Cloud Storage and returns a shareable URL. --- # File to Link Skill This skill provides a professional and reliable way to take a local file and get a publicly accessible URL for it. It uses **Qiniu Kodo Cloud Storage** as the backend. This skill was upgraded from a previous version that used a less reliable, temporary hosting service. It now uses a robust cloud storage provider and correctly handles file paths with special characters (e.g., Chinese, spaces). ## SECURITY WARNING **Do not use this skill for sensitive, private, or confidential files.** Files uploaded using this skill may become **publicly accessible** depending on the bucket's configuration. Use this tool only for non-sensitive data that you are comfortable sharing. ## Core Functionality The skill takes a local file path as input, uploads the file to a pre-configured Qiniu Kodo bucket, and prints the resulting public URL to standard output. ## Prerequisites This skill requires the `qiniu` Python package to be installed. If it's not present, the skill will fail with an import error. ```sh pip install qiniu ``` ## Usage Example To upload a file and get its link, execute the core Python script with the `--file` parameter. ```powershell # Example usage & "F:\python 3.10\python.exe" "C:\Users\EDY\.openclaw\skills\file-to-link\scripts\create_link.py" --file "F:\周明慧\资料\clawhub_skill\违规·素材\违规视频1.mp4" ``` ### Parameters * `--file` (required): The full, absolute local path to the file you want to upload. ### Output If the upload is successful, the script will print **only the URL** to its standard output. For example: ``` http://tbbc24uef.hn-bkt.clouddn.com/%E8%BF%9D%E8%A7%84%E8%A7%86%E9%A2%911.mp4_1772527755 ``` If there is an error (e.g., file not found, Qiniu credentials incorrect), an error message will be printed to standard error, and the standard output will be empty.


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