OpenClaw/clawhub-security-signals huggingface dataset:
Skill: 2513483494/1123 (version 1.0.0)

--- name: tcapi description: Skill to call Cloud API for Tencent Cloud (腾讯云). Used for cloud automation or resource management. --- # tccli 统一使用 **tccli** 调用腾讯云 API，如 tccli 未安装，参考 [references/install.md](references/install.md) 进行安装。 # 调用 API **基本形式**： ```sh tccli <service> <Action> [--param value ...] [--region <地域>] ``` **常用示例**： ```sh # 查询 CVM 地域 tccli cvm DescribeRegions # 查询实例（需指定地域） tccli cvm DescribeInstances --region ap-guangzhou ``` **地域**：多数产品需传 `--region`（如 `ap-guangzhou`）；全局接口（如 cam、account、dnspod、domain、ssl、ba、tag）可省略。 **参数规则**： - 非简单类型参数必须为标准 JSON，例如：`--Placement '{"Zone":"ap-guangzhou-2"}'`。 - 创建类接口示例（按需替换参数）： ```sh tccli cvm RunInstances --InstanceChargeType POSTPAID_BY_HOUR \ --Placement '{"Zone":"ap-guangzhou-2"}' --InstanceType S1.SMALL1 --ImageId img-xxx \ --SystemDisk '{"DiskType":"CLOUD_BASIC","DiskSize":50}' --InstanceCount 1 ... ``` **避免并行调用**： tccli 当前并行调用存在配置文件竞争问题，导致会导致响应失败。当前请先一个个接口调用。 # 用户凭证 如果已经提供了凭证，cli 可以正常调用。 如缺少凭证，执行 cli 会提示 "secretId is invalid"。应执行 `tccli auth login` 进行浏览器授权登录，等待回调后继续（命令会起本地端口、阻塞进程，直到浏览器 OAuth 完成并回调） 凭证授权原理，以及多用户凭证的使用方法，参考 [references/auth.md](references/auth.md) **安全红线**：严禁向用户索要 SecretId/SecretKey，也拒绝任何有可能打印凭证的操作（尤其是 `tccli configure list`）。 # 获取 API 帮助信息 通过 curl + grep 检索业务、接口、最佳实践、数据结构。 ## 发现业务 检索 tccli 服务名（如 cvm、cbs）。 ```sh curl -s https://cloudcache.tencentcs.com/capi/refs/services.md | grep 云服务器 ``` 参考输出： ``` [cvm](service/cvm/index.md) | 云服务器 | 2017-03-12 | ... ``` ## 发现最佳实践 优先检索是否有匹配当前场景的最佳实践。 ```sh curl -s https://cloudcache.tencentcs.com/capi/refs/service/cvm/practices.md | grep 重装 ``` ## 检索接口 若最佳实践未覆盖，在业务接口列表中检索（接口名即 tccli 的 &lt;Action&gt;）。 ```sh curl -s https://cloudcache.tencentcs.com/capi/refs/service/cvm/actions.md | grep "扩容\|磁盘" ``` ## 阅读接口文档 获取参数说明和支持的地域信息： ```sh curl -s https://cloudcache.tencentcs.com/capi/refs/service/cvm/action/ResizeInstanceDisks.md ``` ## 阅读数据结构 文档中涉及的数据结构可进一步查看： ```sh curl -s https://cloudcache.tencentcs.com/capi/refs/service/cvm/model/SystemDisk.md ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kasture-rohit/vcf-compliance-mcp (version 1.0.1)

--- name: vcf-compliance-mcp description: An MCP server that interfaces with VMware Aria Operations to run regulatory compliance checks (ISO 27001, PCI DSS, CIS, etc.) against the VCF environment. version: 1.0.1 homepage: https://github.com/kasture-rohit/vcf-openclaw-skills metadata: openclaw: requires: env: - ARIA_OPS_HOST - ARIA_OPS_API_TOKEN emoji: "" --- # VCF Regulatory Compliance Scanner This skill uses the Model Context Protocol (MCP) to provide the AI agent with a native `get_vcf_compliance_status` tool. It queries VMware Aria Operations to generate instant audit reports for standard security frameworks. ### Setup Instructions for OpenClaw To use this skill, the OpenClaw agent must run the attached Python MCP server. 1. Install dependencies: `pip install -r requirements.txt` 2. Configure your OpenClaw settings to launch this MCP server. Add the following to your agent's MCP configuration: ```json { "mcpServers": { "vcf-compliance": { "command": "python", "args": ["server.py"], "env": { "ARIA_OPS_HOST": "your-aria-ops-fqdn", "ARIA_OPS_API_TOKEN": "your-api-token" } } } }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mupengi-bot/doc-factory (version 1.0.0)

--- name: doc-factory description: 문서 생성 후 디스코드 채널 간 양방향 링크로 전달하는 프로세스. 펭귄공장(지휘 채널)에서 문서 생성 지시가 오면 대상 채널에 문서 업로드 + 펭귄공장 링크, 펭귄공장에 완료 알림 + 대상 채널 링크를 남긴다. "문서 만들어줘", "PDF 만들어줘", "견적서 만들어줘", "리스트 정리해줘" 등 문서 생성·전달 요청 시 사용. --- # Doc Factory — 문서 생성 프로세스 ## 프로세스 펭귄공장(`1486250338338996325`)에서 지시가 들어오면: 1. **문서 생성** — HTML → Chrome headless PDF (여백 좁게, `--no-margins`) 2. **대상 채널에 업로드** — 문서 파일 + 본문에 `<#1486250338338996325>` (펭귄공장) 링크 포함 3. **펭귄공장에 완료 알림** — `<#대상채널>` 링크 포함하여 바로 이동 가능하게 ## 핵심 규칙 - **양방향 링크 필수**: 대상 채널 → 펭귄공장, 펭귄공장 → 대상 채널 - 대상 채널 메시지 예: `📋 문서 제목\n💬 피드백은 <#1486250338338996325>에서!` - 펭귄공장 메시지 예: `<#대상채널>에 문서 올렸어! 확인하고 피드백 줘 🐧` ## PDF 생성 팁 ```bash /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome \ --headless --disable-gpu --print-to-pdf="output.pdf" \ --no-margins --print-to-pdf-no-header input.html ``` - 여백: 좁게 (`@page { margin: 15mm 10mm; }` HTML 내부 제어) - 헤더별 구분: 테이블 또는 섹션으로 시각적 분리 - 파일명: `MUFI-{문서종류}-{날짜}.pdf`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1kalin/afrexai-training-program (version 1.0.0)

# Employee Training Program Builder Build structured training programs for any role, department, or compliance requirement. Generates curricula, schedules, assessment criteria, and completion tracking. ## When to Use - Onboarding new hires with role-specific training - Rolling out compliance training (SOC 2, HIPAA, GDPR, OSHA) - Upskilling teams on new tools or processes - Building leadership development programs - Creating certification prep paths ## Instructions When asked to build a training program, gather: 1. **Role/audience** — Who's being trained? 2. **Objective** — What should they know/do after? 3. **Timeline** — How long is the program? 4. **Format** — Self-paced, instructor-led, blended? Then generate: ### Program Structure ``` PROGRAM: [Name] AUDIENCE: [Role/team] DURATION: [X weeks] FORMAT: [Self-paced / Instructor-led / Blended] MODULE 1: [Topic] - Learning objectives (3-5 measurable outcomes) - Content outline (topics, subtopics) - Activities (hands-on exercises, case studies) - Assessment (quiz, practical demo, project) - Time estimate: X hours - Resources needed: [tools, access, materials] MODULE 2: [Topic] ... ``` ### Assessment Framework For each module, define: - **Knowledge check**: Quiz (70% pass rate minimum) - **Practical assessment**: Demo or project deliverable - **Competency rubric**: 1-5 scale per skill area ### Completion Tracking ``` | Employee | Module 1 | Module 2 | Module 3 | Overall | Status | |----------|----------|----------|----------|---------|--------| | Name | ✅ 92% | ✅ 88% | 🔄 In Progress | 60% | On Track | ``` ### Compliance Training Requirements If compliance-related, include: - Regulatory requirement citation - Recertification frequency - Documentation/record retention period - Audit trail format ### ROI Metrics Track training effectiveness: - **Time to productivity**: Days from start to independent work - **Error rate reduction**: Before/after training comparison - **Retention impact**: 94% of employees stay longer at companies investing in training (LinkedIn 2024) - **Cost per employee**: Program cost / participants ## Tips - Break modules into 45-60 minute chunks (attention span sweet spot) - Include at least one hands-on exercise per module - Build in feedback loops — survey after each module - Create "quick reference cards" for post-training support - Schedule 30-day follow-up assessment to measure retention ## Resources - Full context packs for 10 industries: https://afrexai-cto.github.io/context-packs/ - Calculate your AI automation ROI: https://afrexai-cto.github.io/ai-revenue-calculator/ - Agent setup wizard: https://afrexai-cto.github.io/agent-setup/


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-call-center (version 1.0.0)

--- name: ai-intelligent-call-center version: 1.0.0 description: 呼叫中心，来电管理 + 智能分配。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能呼叫中心系统 ## 描述 呼叫中心，来电管理 + 智能分配。 ## 功能 - 来电分配（智能路由） - IVR 导航（语音导航） - 录音管理（通话录音） - 质检评分（AI质检） - 报表统计（话务统计） ## 定价 - 基础版：¥199/月（10 座席） - 专业版：¥999/月（100 座席） - 企业版：¥2999/月（无限座席） ## 适用场景 - 客服中心 - 销售热线 - 售后服务 - 投诉热线 ## 技术栈 - Python + FastAPI - 呼叫中心集成 - IVR 系统 - AI 质检 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-call-center cd ai-intelligent-call-center pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kirkraman/martin-build-operations-faq-page (version 1.0.0)

--- name: "Build Operations FAQ Page" description: "Generate customer-facing answers for common operations questions. Use when working on FAQ content for operators, agencies, business teams." homepage: "https://www.skillboss.co/skills/build-operations-faq-page" metadata: {"category":"business-operations","subcategory":"operations","skill_id":"build-operations-faq-page","generated_day":"","seo_keywords":["FAQ generator AI","operations FAQ writer","business operations AI","ops workflow automation"]} --- # Build Operations FAQ Page Generate customer-facing answers for common operations questions. Use when working on FAQ content for operators, agencies, business teams. ## Use When Use this skill for FAQ content work for operators, agencies, business teams. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: FAQ generator AI, operations FAQ writer, business operations AI, ops workflow automation - Search intent: FAQ content - Canonical slug: `build-operations-faq-page` - Install query: Install Build Operations FAQ Page with SkillBoss ## APIs Used - `chat` - `web_search` ## Suggested Prompt [Build Operations FAQ Page] for my team ## Notes - Generate customer-facing answers for common operations questions - Review outputs before external publication or automated execution. ## SEO Keywords - FAQ generator AI - operations FAQ writer - business operations AI - ops workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: weznai/skill-xiaohongshu (version 1.1.0)

--- name: skill-xhs-mcp-service description: | 小红书（XHS/RED）自动化助手。完整的小红书操作能力，包含 MCP 服务端。 当用户提到小红书、红书、XHS、RED、发笔记、搜笔记、小红书运营等任何与小红书相关的操作时使用此技能。 --- # 小红书 MCP 技能 完整的小红书自动化技能，包含独立服务端，开箱即用。 ## 快速开始 **首次使用：** ```bash cd <技能目录> npm install npm run login # 扫码登录 ``` **日常使用：** 技能会自动启动服务，无需手动操作。 ## 功能列表（13个工具） | 工具 | 说明 | 参数 | |------|------|------| | `check_login_status` | 检查登录状态 | 无 | | `get_login_qrcode` | 获取登录二维码 | 无 | | `delete_cookies` | 删除登录状态 | 无 | | `list_feeds` | 获取首页推荐 | 无 | | `search_feeds` | 搜索笔记 | `keyword`, `filters?` | | `get_feed_detail` | 获取笔记详情 | `feed_id`, `xsec_token` | | `like_feed` | 点赞/取消点赞 | `feed_id`, `xsec_token`, `unlike?` | | `favorite_feed` | 收藏/取消收藏 | `feed_id`, `xsec_token`, `unfavorite?` | | `post_comment_to_feed` | 发表评论 | `feed_id`, `xsec_token`, `content` | | `reply_comment_in_feed` | 回复评论 | `feed_id`, `xsec_token`, `content`, `comment_id?` | | `user_profile` | 获取用户主页 | `user_id`, `xsec_token` | | `publish_content` | 发布图文 | `title`, `content`, `images`, `tags?` | | `publish_with_video` | 发布视频 | `title`, `content`, `video`, `tags?` | ## 使用方式 ### 方式1：Node.js 直接调用（推荐） ```javascript import { searchFeeds, likeFeed, publishContent } from './scripts/xhs-tools.js'; // 搜索笔记 const results = await searchFeeds('咖啡'); // 点赞 if (results.feeds[0]) { await likeFeed(results.feeds[0].id, results.feeds[0].xsec_token); } // 发布图文 await publishContent({ title: '我的笔记', content: '正文内容', images: ['./photo1.jpg'], tags: ['美食'] }); ``` ### 方式2：MCP 服务调用 服务默认运行在 http://localhost:18060/mcp ```bash # 启动服务（通常自动启动） npm start # 通过 MCP Inspector 调试 npx @modelcontextprotocol/inspector http://localhost:18060/mcp ``` ### 方式3：命令行快速操作 ```bash # 检查登录 node -e "import('./scripts/xhs-tools.js').then(m => m.checkLoginStatus()).then(console.log)" # 搜索 node -e "import('./scripts/xhs-tools.js').then(m => m.searchFeeds('美食')).then(r => console.log(r.feeds.length))" ``` ## 服务管理 **检查服务状态：** ```bash node scripts/ensure-service.js status ``` **启动服务：** ```bash node scripts/ensure-service.js start ``` **停止服务：** ```bash node scripts/ensure-service.js stop ``` ## 详细文档 - [API 文档](references/api.md) - 完整的 API 参考和示例 ## 注意事项 1. **账号安全**：同一账号不能在多个网页端登录 2. **发布限制**：每天最多 50 篇笔记 3. **标题限制**：最多 20 字 4. **正文限制**：最多 1000 字 5. **Cookie 有效期**：建议每周重新登录一次 ## 环境变量 | 变量 | 说明 | 默认值 | |------|------|--------| | `XHS_PORT` | 服务端口 | `18060` | | `XHS_HOST` | 绑定地址 | `0.0.0.0` | | `XHS_PROXY` | 代理地址 | - | 使用代理： ```bash XHS_PROXY=http://proxy:port npm start ```


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nimblev2023/getmem (version 1.0.2)

--- name: getmem description: 'Persistent memory for AI agents via getmem.ai. Call mem.get() before each LLM call to inject context, and mem.ingest() after each turn to save the conversation.' metadata: openclaw: emoji: 🧠 install: - id: pip-getmem kind: pip package: getmem-ai label: Install getmem-ai (pip) --- # getmem.ai Memory Skill Persistent memory for your AI agent via [getmem.ai](https://getmem.ai). ## Setup Set your API key in the environment: ```bash export GETMEM_API_KEY=gm_live_YOUR_KEY_HERE ``` Get your key at https://platform.getmem.ai — **$20 free credit on signup**. ## Usage ```python import getmem_ai as getmem, os mem = getmem.init(os.environ["GETMEM_API_KEY"]) # Before each LLM call — get relevant memory context result = mem.get(user_id, query=user_message) context = result["context"] # inject into system prompt # After each turn — save both user + assistant messages mem.ingest(user_id, messages=[ {"role": "user", "content": user_message}, {"role": "assistant", "content": reply}, ]) ``` ## How it works 1. `mem.get()` fetches only the relevant memories for the current query (semantic search) 2. Context is injected into your system prompt — typically 200-800 tokens 3. `mem.ingest()` saves the full conversation exchange asynchronously 4. Memory persists indefinitely — no TTL, no purge ## Token savings Standard approach: full conversation history every turn = 10,000-40,000 tokens With getmem: only relevant context = 200-800 tokens. Save up to 95% on context tokens. ## Links - Website: https://getmem.ai - Platform: https://platform.getmem.ai - PyPI: https://pypi.org/project/getmem-ai/ - npm: https://npmjs.com/package/getmem - OpenClaw plugin: clawhub:getmem-openclaw


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xbos1314/video-generation-minimax (version 1.0.0)

--- name: video-generation-minimax description: 视频生成技能，使用 MiniMax 视频生成 API 创建视频 metadata: {"openclaw":{"emoji":"🎬","requires":{"bins":["python3"]}}} --- # Video Generation MiniMax 使用 MiniMax 视频生成 API 创建视频。支持4种生成模式： 1. **文生视频**：根据文本描述生成视频 2. **图生视频**：基于图片+文本描述生成视频 3. **首尾帧**：首图+尾图+文本生成视频 4. **主体参考**：人脸照片+文本，保持人物特征一致 ## 前置要求 - 安装 Python 依赖：`pip3 install requests` - 设置环境变量 `MINIMAX_API_KEY` ## 使用方法 ### 方式一：Python 脚本（推荐） ```bash python3 {baseDir}/scripts/video_gen.py --mode text --prompt "描述文字" python3 {baseDir}/scripts/video_gen.py --mode image --prompt "描述文字" --image "图片URL" python3 {baseDir}/scripts/video_gen.py --mode start_end --prompt "描述文字" --first "首图URL" --last "尾图URL" python3 {baseDir}/scripts/video_gen.py --mode subject --prompt "描述文字" --subject "人脸图片URL" ``` ### 参数说明 | 参数 | 说明 | 必填 | |------|------|------| | --mode | 模式：text/image/start_end/subject | 是 | | --prompt | 视频描述文本 | 是 | | --image | 图生视频的首帧图片URL | image模式必填 | | --first | 首尾帧模式的首帧图片URL | start_end模式必填 | | --last | 首尾帧模式的尾帧图片URL | start_end模式必填 | | --subject | 主体参考模式的人脸图片URL | subject模式必填 | | --duration | 视频时长：6/10 | 否，默认6 | | --resolution | 分辨率：720P/1080P | 否，默认1080P | | --output | 输出文件名 | 否，默认 output.mp4 | ### 示例 ```bash # 文生视频 python3 {baseDir}/scripts/video_gen.py --mode text --prompt "镜头拍摄一个女性坐在咖啡馆里，女人抬头看着窗外" # 图生视频 python3 {baseDir}/scripts/video_gen.py --mode image --prompt "Contemporary dance" --image "https://example.com/image.png" # 首尾帧生成 python3 {baseDir}/scripts/video_gen.py --mode start_end --prompt "A little girl grow up" --first "https://example.com/start.jpg" --last "https://example.com/end.jpg" # 主体参考 python3 {baseDir}/scripts/video_gen.py --mode subject --prompt "在街头走路的男士" --subject "https://example.com/face.jpg" ``` ## 数据来源 - API Key：从环境变量 `MINIMAX_API_KEY` 读取 - API 文档：https://platform.minimaxi.com/docs/llms.txt ## 注意事项 - 视频生成是异步过程，需要轮询任务状态 - 推荐轮询间隔为10秒 - **生成完成后自动下载视频到你的工作空间 `{WorkspaceDir}/video-generation/`目录下随后发送给用户** - 模型版本：文生视频用 MiniMax-Hailuo-2.3，主体参考用 S2V-01


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

--- name: birdweather description: Query BirdWeather station data — species detections, trends, and comparisons from BirdNET-Pi and PUC bird song detection stations. Use when asked about bird sightings, bird song detections, local bird activity, BirdWeather stations, or wildlife audio monitoring. No API key required. --- # BirdWeather CLI at `scripts/birdweather.py` for querying BirdWeather station data. No API key needed — all station data is public. Find station IDs at https://app.birdweather.com/ ## Commands ```bash # Station info (name, location, status, last detection) scripts/birdweather.py station <station_id> # Species detected (default: today) scripts/birdweather.py species <station_id> --period day|week|month --limit 50 # Species for date range scripts/birdweather.py species <station_id> --from 2026-02-01 --to 2026-02-14 # Top species (compact, default: week) scripts/birdweather.py top <station_id> --period week --limit 10 # Recent individual detections scripts/birdweather.py detections <station_id> --limit 20 # Compare periods (shows new arrivals, departures, trends) scripts/birdweather.py compare <station_id> --this week --last month # Any command with raw JSON output scripts/birdweather.py --json species <station_id> --period week ``` ## Output Species lists show detection count, common name, and scientific name: ``` 467 Eurasian Blue Tit (Cyanistes caeruleus) 290 Eurasian Wren (Troglodytes troglodytes) ``` Compare shows arrivals, departures, and significant changes between periods. ## Notes - Detection count ≠ bird count (one bird singing repeatedly = many detections) - BirdNET AI classification can misidentify — unusual species may be false positives - Stations go offline periodically — empty results may mean the station is down, not that there are no birds - Period options: `day` (today), `week` (last 7 days), `month` (last 30 days) - Stdlib only — no pip dependencies


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nmww/minimax-skill (version 1.0.0)

--- name: minimax-skill description: Unified MiniMax media generation skill for Token Plan workflows. Use when the user asks to generate audio, speech, TTS, narration, images, illustrations, posters, covers, thumbnails, videos, motion clips, text-to-video, image-to-video, or needs MiniMax API setup instructions across Linux, macOS, Windows, Docker, or CI. --- # MiniMax Media Generation Use this skill to generate MiniMax audio, images, and videos from one command surface. ## Requirements - Python 3 - `requests` package - `MINIMAX_API_KEY` environment variable For system-specific API key setup, read `references/setup.md`. ## Unified Command Run `scripts/minimax.py` with one mode: `audio`, `image`, or `video`. ```bash python scripts/minimax.py {audio|image|video} [options] ``` ## Audio Generate an MP3 file from text: ```bash python scripts/minimax.py audio \ --text "你好，今天我们聊一下 MiniMax。" \ --output /tmp/minimax-audio.mp3 ``` Useful options: - `--model speech-2.8-turbo` for lower latency - `--model speech-2.8-hd` for higher fidelity - `--voice-id <voice_id>` to select a voice when available ## Image Generate images into a directory: ```bash python scripts/minimax.py image \ --prompt "一只穿西装的橘猫，电影感，柔光" \ --output /tmp/minimax-images \ --aspect-ratio 1:1 ``` Useful options: - `--model image-01` - `--model image-01-live` for stronger style control when available - `--n 2` to request multiple images - `--image-file <url_or_base64>` for image-to-image workflows when supported Outputs are saved as `output-0.jpeg`, `output-1.jpeg`, etc. ## Video Generate a video file: ```bash python scripts/minimax.py video \ --prompt "镜头缓慢推进，一只橘猫走过雨夜街道，霓虹灯反光" \ --output /tmp/minimax-video.mp4 ``` Useful options: - `--model MiniMax-Hailuo-2.3` - `--first-frame-image <url_or_base64>` for image-to-video - `--last-frame-image <url_or_base64>` for start/end-frame video - `--subject-reference '<json>'` for subject reference workflows - `--timeout 1800` to control polling timeout in seconds ## Direct Scripts The unified command delegates to standalone scripts in the same skill: - `scripts/generate_audio.py` - `scripts/generate_image.py` - `scripts/generate_video.py` Use these directly only when a narrower, mode-specific command is preferred. ## Safe Sharing Notes - Do not include a real API key in the skill package. - Ask users to configure `MINIMAX_API_KEY` locally. - Keep generated media outside the skill folder, for example under `/tmp` or a project output directory.


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