OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tobeyrebecca/toby-productivity-helper-3 (version 1.0.0)

--- name: productivity-helper-3 description: "Productivity helper tool #3 for task management, time tracking, and workflow optimization. Helps organize daily tasks and boost efficiency." allowed-tools: Bash, Read, Write metadata: openclaw: emoji: "⚡" --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # Productivity Helper #3 A productivity enhancement tool for OpenClaw users. ## Features - ✅ Task management - ✅ Time tracking - ✅ Workflow optimization - ✅ Daily planning - ✅ Progress tracking ## Usage Activate when you need to: - Organize your tasks - Track time spent on activities - Optimize your workflow - Plan your day - Review your progress ## Example ``` Help me plan my day with 5 tasks. ``` Assistant will help organize and prioritize your tasks.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lihai2582424632-droid/short (version 1.0.0)

--- name: short description: "Generate images with FLUX models (Black Forest Labs) via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA with custom style adaptation. Capabilities: text-to-image, image-to-image, LoRA fine-tuning, custom styles. Triggers: flux, flux.2, flux dev, flux schnell, flux pro, black forest labs, flux image, flux ai, flux model, flux lora" allowed-tools: Bash(infsh *) --- # FLUX Image Generation Generate images with FLUX models via [inference.sh](https://inference.sh) CLI. ![FLUX Image Generation](https://cloud.inference.sh/app/files/u/4mg21r6ta37mpaz6ktzwtt8krr/01kg0v0nz7wv0qwqjtq1cam52z.jpeg) ## Quick Start ```bash curl -fsSL https://cli.inference.sh | sh && infsh login infsh app run falai/flux-dev-lora --input '{"prompt": "a futuristic city at night"}' ``` > **Install note:** The [install script](https://cli.inference.sh) only detects your OS/architecture, downloads the matching binary from `dist.inference.sh`, and verifies its SHA-256 checksum. No elevated permissions or background processes. [Manual install & verification](https://dist.inference.sh/cli/checksums.txt) available. ## FLUX Models | Model | App ID | Speed | Quality | Use Case | |-------|--------|-------|---------|----------| | FLUX Dev LoRA | `falai/flux-dev-lora` | Medium | Highest | Production, custom styles | | FLUX.2 Klein LoRA | `falai/flux-2-klein-lora` | Fastest | Good | Fast iteration, 4B/9B sizes | ## Examples ### High-Quality Generation ```bash infsh app run falai/flux-dev-lora --input '{ "prompt": "professional product photo of headphones, studio lighting, white background" }' ``` ### Fast Generation (Klein) ```bash infsh app run falai/flux-2-klein-lora --input '{"prompt": "abstract art, colorful"}' ``` ### With LoRA Custom Styles ```bash infsh app sample falai/flux-dev-lora --save input.json # Edit to add lora_url for custom style infsh app run falai/flux-dev-lora --input input.json ``` ### Image-to-Image ```bash infsh app run falai/flux-dev-lora --input '{ "prompt": "transform to watercolor style", "image_url": "https://your-image.jpg" }' ``` ## For Other Image Tasks ```bash # Image editing with natural language infsh app run falai/reve --input '{"prompt": "change background to beach"}' # Upscaling infsh app run falai/topaz-image-upscaler --input '{"image_url": "https://..."}' ``` ## Related Skills ```bash # Full platform skill (all 150+ apps) npx skills add inference-sh/skills@inference-sh # All image generation models npx skills add inference-sh/skills@ai-image-generation # Upscaling npx skills add inference-sh/skills@image-upscaling ``` Browse all apps: `infsh app list` ## Documentation - [Running Apps](https://inference.sh/docs/apps/running) - How to run apps via CLI - [Image Generation Example](https://inference.sh/docs/examples/image-generation) - Complete image generation guide - [Streaming Results](https://inference.sh/docs/api/sdk/streaming) - Real-time progress updates


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cxzcy/feishu-voice-bot (version 1.0.0)

--- name: feishu-voice-bubble description: | Send native voice bubbles via Feishu using edge-tts + ffmpeg. Converts text to OGG/Opus audio and sends as a playable blue voice bubble. Use when: (1) User sends a voice message and expects voice reply. (2) User says "语音回复" or "用语音". (3) Any TTS/voice output to Feishu. Requires: edge-tts skill (scripts/tts-converter.js), ffmpeg, message tool with channel=feishu. --- # Feishu Voice Bubble Send native voice bubbles (蓝色可播放气泡) via Feishu using bot identity. ## Workflow ``` text → edge-tts (mp3) → ffmpeg (ogg/opus) → message tool (media=) → 原生气泡 ``` ## Quick Usage ```bash # One-liner via the helper script node scripts/voice-bubble.mjs "你好陛下" --voice zh-CN-XiaoxiaoNeural ``` Or use the message tool directly after generating the audio: ```bash # Step 1: Generate cd ~/.openclaw/workspace/skills/edge-tts/scripts node tts-converter.js "文本" --voice zh-CN-XiaoxiaoNeural --output /tmp/voice.mp3 # Step 2: Convert ffmpeg -i /tmp/voice.mp3 -c:a libopus -b:a 32k /tmp/voice.ogg -y # Step 3: Send via message tool message(action=send, channel=feishu, target=<open_id_or_chat_id>, media=/tmp/voice.ogg) ``` ## Supported Voices | 语言 | 语音 ID | 特点 | |------|---------|------| | 中文 | zh-CN-XiaoxiaoNeural | 女声，自然 | | 中文 | zh-CN-YunxiNeural | 男声，自然 | | 英文 | en-US-AriaNeural | 女声 | | 英文 | en-US-GuyNeural | 男声 | Full list: `node ~/.openclaw/workspace/skills/edge-tts/scripts/tts-converter.js --list-voices` ## Notes - **无需用户 OAuth** — 机器人身份 + `message` tool + `media=` 即可发送原生气泡 - **格式** — 飞书要求 OGG/Opus 格式，edge-tts 输出 MP3，需 ffmpeg 转换 - **临时文件** — 生成的音频存于 `/tmp/`，不会自动清理 - **中文默认** — 未指定 voice 时使用 `zh-CN-XiaoxiaoNeural`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: stale | SkillSpector: suspicious (MEDIUM)
Skill: embrron/ops-detection-incident-routing (version 1.0.2)

--- name: ops-detection-incident-routing description: Detect agent runtime anomalies and route incidents through approval-safe guardrails. Use when you need deterministic checks for cron failures, context pressure, dangling sessions, token spikes, and a controlled incident workflow (detect -> route -> investigate -> remediate). homepage: https://github.com/your-org/openclaw-public-skills metadata: {"clawdbot":{"emoji":"🛟","requires":{"bins":["bash","jq","python3"]}}} --- # ops-detection-incident-routing Run deterministic operations checks and route incidents with guardrails. This skill ships a small toolkit for: 1. detecting runtime anomalies from local state/log files 2. applying in-flight + cooldown guards 3. emitting structured incident actions for investigator/remediator flows ## Use This Skill Use this skill when you need a production-safe ops loop for agent systems and do not want ad-hoc prompt-only monitoring. ## Files - `scripts/ops-threshold-detector.sh` reads session/cron/snapshot state and appends detector JSONL events - `scripts/incident-guard-check.sh` checks in-flight/cooldown guard status for a check id - `scripts/incident-state-update.sh` updates guard state for start/complete/fail transitions - `scripts/ops-incident-router.sh` converts detector alerts into structured actions - `scripts/ops-detector-cycle.sh` detector + router cycle runner - `scripts/setup.sh` dependency checks + local example scaffold - `scripts/clean-generated.sh` removes generated `.jsonl` and lock artifacts before republishing from a used folder ## Setup ```bash bash scripts/setup.sh ``` ## Quick Start Run one full dry-run cycle: ```bash bash scripts/ops-detector-cycle.sh \ --workspace "$(pwd)/examples/workspace" \ --state-file "$(pwd)/examples/incident-state.json" \ --detector-out "$(pwd)/examples/ops-detector.jsonl" \ --router-out "$(pwd)/examples/router-actions.jsonl" ``` Run live mode (router also acquires in-flight locks): ```bash bash scripts/ops-detector-cycle.sh \ --workspace "$(pwd)/examples/workspace" \ --state-file "$(pwd)/examples/incident-state.json" \ --detector-out "$(pwd)/examples/ops-detector.jsonl" \ --router-out "$(pwd)/examples/router-actions.jsonl" \ --live ``` ## Output Contract Detector writes one JSON line per run: ```json { "ts": "2026-02-24T02:30:00Z", "status": "ALERT", "checks": 5, "alerts": [{"sev":"Sev-2","trigger":"cron_failure","value":2,"threshold":0}], "gaps": [] } ``` Router emits one JSON action per alert decision: ```json {"action":"spawn","check_id":"cron_failure","severity":"Sev-2","mode":"dry-run","task":"Investigate incident: cron_failure"} ``` ## Operational Pattern 1. schedule `ops-threshold-detector.sh` (every 5-15 min) 2. feed the latest detector line to `ops-incident-router.sh` 3. spawn investigator/remediator only from router output 4. keep remediation behind explicit owner approval For details, read `references/architecture.md`.

path: examples/incident-state.json

Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangjipeng977/ai-image-to-code-jipeng (version 1.0.0)

# AI Image To Code **Emoji:** 🖼️→💻 **Trigger:** User pastes a UI screenshot/image and wants code (HTML/CSS or React). ## What It Does Converts UI screenshots into working HTML/CSS or React + Tailwind components. Analyzes the layout structure, color palette, typography hierarchy, and spacing to produce faithful code reconstruction. ## Features - Vision-powered layout extraction (header, sidebar, main content, etc.) - Multi-format output: plain HTML/CSS (default) or React + Tailwind CSS - Mobile-first responsive (detects mobile screenshots → max-width: 375px) - Contextual placeholder content (e.g., "Price: $49.99" not lorem ipsum) ## Modes | Mode | Description | |------|-------------| | `/ai-image-to-code` | Convert UI image to HTML/CSS | | `/ai-image-to-code/react` | Output React functional component + Tailwind | | `/ai-image-to-code/describe` | Text description of layout, no code | ## How To Use ``` /ai-image-to-code ``` Paste a screenshot, ask to generate HTML/CSS. ``` /ai-image-to-code/react ``` Asks for React + Tailwind output instead. ``` /ai-image-to-code/describe ``` Just describe the layout, no code generation. ## Technical Notes - Uses MiniMax vision model to analyze screenshot - Detects dark mode and applies appropriate color schemes - Generates semantic HTML structure - Tailwind classes mapped from visual analysis --- *Last updated: 2026-05-28*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: emergencescience/emergence-pulse-moltcn (version 0.1.2)

--- name: emergence-pulse-moltcn version: 0.1.2 description: emergence.science - 涌现科学：Moltbook.cn（中文智能体社交网络）的每日脉搏摘要。 homepage: https://github.com/emergencescience/skills metadata: clawdbot: emoji: "🔥" requires: env: [] primaryCredential: "~/.config/moltcn/credentials.json" files: ["scripts/digest.py"] --- > **提供者**: 涌现科学 (Emergence Science) > **状态**: 已验证 (Surprisal Protocol) > **简介**: 由 涌现科学 驱动的原生 AI 智能服务。 # 涌现脉搏 (Moltbook.cn 版) 获取 Moltbook.cn 上的热门帖子摘要，提供中文简介和直接阅读链接。本工具旨在为智能体提供高信噪比的社区动态。 ## 使用方法 (Usage) ```bash # 获取热门帖子（默认 10 条） python3 scripts/digest.py --limit 10 # 自定义排序和数量 python3 scripts/digest.py --sort hot --limit 5 python3 scripts/digest.py --sort new --limit 10 python3 scripts/digest.py --sort top --limit 10 ``` ## 触发词 (Trigger Words) - "脉搏：MoltCN 今日热门" - "获取 Moltbook.cn 摘要" - "脉搏：MoltCN 热帖" - "Emergence 脉搏：MoltCN" ## 配置 (Configuration) 凭据文件：`~/.config/moltcn/credentials.json` ## 外部端点 (External Endpoints) | URL | 数据 | 目的 | |-----|------|------| | `https://www.moltbook.cn/api/v1/posts` | 无 | 获取热门帖子列表 | | `https://www.moltbook.cn/api/v1/posts/{id}` | 无 | 获取帖子详情生成摘要 | ## 安全与隐私 (Security & Privacy) - **只读 (Read-Only)**: 本技能仅从 Moltbook.cn 读取公开信息，**不具备**任何写入、发帖或修改数据的权限。 - **凭据**: 仅使用本地 `~/.config/moltcn/credentials.json`，不使用环境变量。 - **数据流**: 仅向 Moltbook.cn 发送请求，不包含用户私密数据。 - **信任声明**: 使用本技能即代表数据将发送至 Moltbook.cn。请仅在信任该平台时使用。 --- *由 惊异协议 (Surprisal Protocol) 提供支持。技术验证请访问：[Emergence Science](https://emergence.science/skills)* *Powered by the Surprisal Protocol. For technical verification, visit [Emergence Science](https://emergence.science/skills).*

path: _meta.json
content: ownerId: kn7c83ffhzqbhaqes1fdt9t5e5825jme
slug: emergence-pulse-moltcn
version: 0.1.2
primaryCredential: ~/.config/moltcn/credentials.json
publishedAt: 1773991736000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jerryxn/happy-img2-direct (version 1.0.0)

--- name: happy_img2_direct version: 1.0.0 description: Generate images with an OpenAI-compatible image provider such as happy/gpt-image-2, with retries and bounded batch concurrency. metadata: openclaw: emoji: "🖼️" requires: bins: - python3 - node config: - models.providers --- # Happy IMG2 Direct Skill Generate real images through an OpenAI-compatible `/images/generations` endpoint configured in OpenClaw. Default behavior: - provider: `happy` unless `OPENCLAW_IMAGE_PROVIDER` is set - model: `gpt-image-2` unless `OPENCLAW_IMAGE_MODEL` is set - size: `1024x1024` - timeout: `600000ms` per image - output: `~/.openclaw/generated-images/` - no local fake-image fallback - no built-in message delivery; send or attach files using your normal OpenClaw/channel tools ## Single image ```bash python3 skills/happy-img2-direct/scripts/run.py \ --prompt "A realistic photo of an orange cat sitting by a window, no text, no watermark" \ --task-name "cat-test" \ --no-send ``` Useful flags: - `--prompt` required - `--task-name` output filename prefix - `--provider` provider key in OpenClaw config, default `happy` - `--model` image model, default `gpt-image-2` - `--size` default `1024x1024` - `--timeout-ms` default `600000` - `--output-dir` default `~/.openclaw/generated-images` - `--max-attempts` default `3`, maximum `5` - `--retry-base-delay`, `--retry-max-delay`, `--retry-jitter` - `--raw` marker for callers that intentionally keep the user prompt unchanged - `--no-send` accepted for compatibility; this public skill always leaves delivery to the caller Successful output is JSON containing `ok:true`, `image_path` or `output`, bytes, model/provider, attempt count, and run directory. ## Batch images ```bash python3 skills/happy-img2-direct/scripts/batch_run.py @batch.json ``` Example: ```json { "batch_name": "article-covers", "max_workers": 4, "timeout_ms": 600000, "send_to_feishu": false, "tasks": [ {"task_name": "cover-1", "prompt": "Realistic shop counter photo, no readable text"}, {"task_name": "cover-2", "prompt": "Realistic office desk photo, no readable text"} ] } ``` Batch rules: - bounded concurrency, current hard maximum `4` - each item has its own task directory and logs - one failed image does not prevent other images from finishing - final `batch_result.json` records success/failure per task - delivery is disabled in the public version; use OpenClaw/channel tools to send files ## Retry behavior Retries are limited and only used for retryable failures: - timeout - upstream failures - rate limits - HTTP `408/429/500/502/503/504` - wrapper parse errors Non-retryable errors, such as invalid requests or auth failures, fail fast with redacted diagnostics. ## Safety and publishing notes This skill intentionally contains no private OpenClaw IDs, no hard-coded user paths, no API keys, and no channel recipient IDs. It reads provider configuration from the local OpenClaw config at runtime.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lunarmoon26/steam-icon-fixer (version 1.0.0)

--- name: steam-icon-fixer description: Fix missing or generic Steam game desktop icons on Linux by finding the Steam app ID from local launchers and installing replacement artwork into the user's icon theme. Use when Steam-created desktop launchers show blank or generic icons, when the app ID must be discovered from local launchers, or when a downloaded image needs to be installed in Steam's icon naming scheme. --- # Steam Icon Fixer Use this skill to repair Steam launcher icons on Linux desktops. ## Core workflow 1. Prefer the user's explicit inputs when available. - downloaded image path - game name - Steam app ID - specific `.desktop` file 2. Otherwise infer the common local locations. - launcher dir: `${XDG_DATA_HOME:-~/.local/share}/applications` - icon dir: `${XDG_DATA_HOME:-~/.local/share}/icons/hicolor` 3. Discover the app ID from the launcher metadata. - look for `Icon=steam_icon_<id>` - look for `Exec=...steam://rungameid/<id>` - if needed, search local launchers by game name 4. Run the bundled installer script. 5. Refresh the icon cache if the script does not do it automatically. ## Commands Scan local Steam launchers for missing icons: ```bash python3 scripts/steam_icon_fixer.py scan ``` Install an image with an explicit app ID: ```bash python3 scripts/steam_icon_fixer.py install \ --image /path/to/image.png \ --app-id 1086940 ``` Install an image by matching a game name: ```bash python3 scripts/steam_icon_fixer.py install \ --image /path/to/image.png \ --game "Baldur's Gate 3" ``` Install using a specific desktop file: ```bash python3 scripts/steam_icon_fixer.py install \ --image /path/to/image.png \ --desktop-file ~/.local/share/applications/Baldur's\ Gate\ 3.desktop ``` Use explicit directories when the machine is nonstandard: ```bash python3 scripts/steam_icon_fixer.py install \ --image /path/to/image.png \ --game "DOOM: The Dark Ages" \ --desktop-dir /custom/applications \ --icons-dir /custom/icons/hicolor ``` ## Bundled resources - `scripts/steam_icon_fixer.py`: deterministic installer/scanner for Steam launcher icons. - `references/`: add launcher or icon-theme notes here if the workflow needs machine-specific guidance. ## Notes - The script should generate standard icon sizes and keep Steam's `steam_icon_<appid>.png` naming. - For `.ico` inputs, Pillow may be required. - If GNOME or another dock keeps the old icon, unpin and re-pin the launcher after the cache refresh.


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

# link-all - 外部平台链接与管理 ## 概述 这是一个专门帮助用户链接、访问、调用、管理外部平台的 skill。 ## 职责 1. **寻找合适的工具和方案** - 完成用户指定平台的链接、授权、访问、管理 2. **平台调研** - 了解平台背景信息，查找相关技术文档（API/CLI） 3. **方案适配** - 优先查找 OpenClaw 插件，其次 CLI 工具，最后梳理 API 4. **用户确认** - 将找到的方案发给用户选择 5. **脚本开发** - 如需 API 则编写相关脚本 6. **技能转化** - 首次成功链接后询问用户是否创建为 skill ## 工作流程 ### 步骤 1：了解平台背景 当用户提供要访问的平台时： - 搜索该平台的基本信息 - 了解平台的定位、主要功能、认证方式 - 确定平台是否支持 API/CLI 访问 ### 步骤 2：查找技术文档 - 搜索平台官方 API 文档 - 搜索平台官方 CLI 工具 - 搜索开发者社区的相关资源 ### 步骤 3：适配方案优先级 ``` 优先级 1: OpenClaw Plugins → 优先查找是否已有现成插件 优先级 2: CLI 工具 → 查找官方或社区提供的命令行工具 优先级 3: API 文档 → 梳理 REST API 文档，编写调用脚本 ``` ### 步骤 4：用户确认 将找到的方案整理后发给用户： - 方案 1：使用 XXX 插件（如果存在） - 方案 2：使用 XXX CLI 工具 - 方案 3：通过 API 调用（需要开发脚本） 等待用户确认使用哪个方案。 ### 步骤 5：执行链接 - 如果是插件：帮助安装配置 - 如果是 CLI：帮助安装并配置认证 - 如果是 API：编写脚本并完成首次调用测试 ### 步骤 6：技能转化（重要！） 首次成功链接后，**必须**询问用户： ``` ✅ 平台链接成功！是否需要将此平台的链接方案创建为独立的 skill？ - "要" / "需要" → 帮用户梳理方案并创建 skill - "不要" / "不需要" → 结束 ``` 如果用户需要创建 skill： 1. 梳理完整的链接方案（认证方式、配置参数、常用操作） 2. 在 `~/.openclaw/skills/` 下创建新的 skill 目录 3. 编写 SKILL.md 文件 4. 告知用户 skill 已创建 ## 输出格式 ### 平台信息报告 ``` ## 平台：XXX ### 基本信息 - 官网：xxx - 类型：API/CLI/SDK - 认证方式：API Key/OAuth/Token ### 技术文档 - API 文档：xxx - CLI 工具：xxx - SDK：xxx ### 推荐方案 1. 方案一：xxx 2. 方案二：xxx 3. 方案三：xxx ``` ### 链接成功报告 ``` ## ✅ XXX 平台链接成功 ### 配置信息 - 认证方式：xxx - 配置参数：xxx - 测试结果：xxx ### 常用操作 - 操作 1：xxx - 操作 2：xxx ### 是否需要创建 skill？ 请回复 "要" 或 "不要" ``` ## 示例对话 **用户**：帮我连接 GitHub **我**： ``` ## 平台：GitHub ### 基本信息 - 官网：https://github.com - 类型：代码托管平台 - 认证方式：Personal Access Token (PAT) ### 技术文档 - API：https://docs.github.com/rest - CLI：gh (GitHub CLI) ### 推荐方案 1. **方案一（推荐）**：使用 `gh` CLI 工具 - 官方出品，易用 2. **方案二**：使用 GitHub REST API - 灵活，可开发脚本 请选择方案（1 或 2）： ``` **用户**：选 1 **我**：帮你安装配置 GitHub CLI... （链接成功后） ``` ## ✅ GitHub 链接成功 - 用户：xxx - 认证：SSH Key 已配置 - 测试：✓ 已验证 是否需要将此平台的链接方案创建为独立的 skill？ - 回复 "要" 或 "不要" ``` ## 注意事项 - 必须按照步骤执行，不能跳过用户确认环节 - 首次链接成功后**必须**询问用户是否需要创建 skill - 如果用户选择创建 skill，需要完整记录配置和操作步骤 - 保持文档清晰，方便后续复用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: allinherog-star/ai-humanizer-zh (version 1.0.0)

--- name: ai-humanizer-zh description: "去AI味助手适合市场营销、运营、内容创作者、产品在用户提出“论文AI写的，帮我去掉AI味”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成AI 写作痕迹诊断、自然化改写稿、问题片段说明。" requiredEnvVars: - name: AISKILLS_API_KEY description: "从 AI Skills 官网 https://ai-skills.ai 获取的 API Key，用于运行导出的技能调用。" --- # ai-humanizer-zh 去AI味助手 [快速开始](https://github.com/allinherog-star/ai-skills/tree/main#%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) [更多技能](https://ai-skills.ai) ### 概述 去AI味助手用于回答「论文AI写的，帮我去掉AI味」、中文改写、AI 写作痕迹、文案润色，适合市场营销、运营、内容创作者、产品在明确业务目标、内容材料或分析对象后调用。 它会结合待去 AI 味文本、粘贴需要改得更自然的中文内容，可以是 AI 生成文章、营销文案、社…等输入，整理关键上下文，并输出AI 写作痕迹诊断、自然化改写稿、问题片段说明，便于继续执行、复盘或交付。 能力定位补充：中文内容创作者、运营和营销团队常需要把 AI 生成草稿改成更自然、更像真实人类写作的发布稿，适合沉淀为平台上的文本诊断与改写 技能。 ### 什么时候使用 **适用场景** - 用户提出“论文AI写的，帮我去掉AI味”这类问题，需要快速拆解目标、判断重点并形成可执行结果 - 市场营销、运营、内容创作者、产品需要围绕去AI味助手生成AI 写作痕迹诊断、自然化改写稿、问题片段说明 - 用户已经准备了改写目标（例如更像真人、更口语、更专业、更有观点、更适合发布或降低营销腔。）、目标读者（说明读者是谁、他们熟悉什么、对内容最在意什么。）、公开文本链接（填写无需登录即可访问的文章、页面、笔记或文档链接。），希望整理成可执行的分析或优化结果 - 用户需要把去AI味助手相关材料转成清晰结论、优先级和下一步动作 ### 调用方式 通过导出的 Python runner 直接调用 AI Skills API： ### 命令示例 **基础调用** ```bash python3 scripts/run.py --params '{}' ``` **带常用参数调用** ```bash python3 scripts/run.py --params '{"goal":"改写目标"}' ``` ### 参数说明 | 参数 | 类型 | 必填 | 默认 | 说明 | | --- | --- | --- | --- | --- | | `goal` | string | 否 | - | 例如更像真人、更口语、更专业、更有观点、更适合发布或降低营销腔 | | `audience` | string | 否 | - | 说明读者是谁、他们熟悉什么、对内容最在意什么 | | `materialUrl` | string | 否 | - | 填写无需登录即可访问的文章、页面、笔记或文档链接；需要传可访问的完整 URL | | `reviewDepth` | string | 否 | `标准改写` | 选择只做快速去味、标准改写，还是深度重写并解释修改原因；可选值：`快速去味`、`标准改写`、`深度重写` | | `materialFile` | string | 否 | - | 支持 docx、pdf、md、txt 等文本稿件，适合提交文章草稿、审稿版或长文材料 | | `materialText` | string | 否 | - | 粘贴需要改得更自然的中文内容，可以是 AI 生成文章、营销文案、社媒长文、课程稿或说明文 | | `targetPlatform` | string | 否 | `通用中文内容` | 选择文本最终要发布或交付的场景；可选值：`通用中文内容`、`公众号文章`、`小红书笔记`、`官网博客`、`营销落地页`、`课程讲稿`、`知识库文档` | | `tonePreference` | string | 否 | - | 例如自然口语、专业克制、创作者第一人称、品牌官方但不生硬 | | `brandRequirements` | string | 否 | - | 补充必须保留的事实、品牌说法、禁用词、合规边界或不能改变的语气 | 完整机器可读参数结构见 `references/form-schema.json`。 ### 参数取值参考 当前技能没有需要额外查表的分类参数。 ### 支持的输入格式 当前技能直接接收 JSON 参数；如果参数里包含链接字段，请传完整、可访问的 URL。 ### 示例请求 下面的示例参数可直接传给 `scripts/run.py`，runner 会把它们发送给 AI Skills API。 ```bash python3 scripts/run.py --params '{"goal":"改写目标"}' ``` 等价的 `--params` JSON： ```json { "goal": "改写目标" } ``` ### 返回结果示例 ```json { "success": true, "data": { "message": "示例结果请以技能真实返回结构为准。" }, "meta": { "executionTime": 842, "cached": false } } ``` ### 交付内容 - AI 写作痕迹诊断、自然化改写稿、问题片段说明：围绕用户目标整理可直接阅读、复盘或交付的核心结果。 - 输入材料解读：结合改写目标（例如更像真人、更口语、更专业、更有观点、更适合发布或降低营销腔。）、目标读者（说明读者是谁、他们熟悉什么、对内容最在意什么。）、公开文本链接（填写无需登录即可访问的文章、页面、笔记或文档链接。）提炼关键上下文和判断依据。 - 下一步动作：给出优先级、执行建议或可继续加工的内容框架。 ### 结果使用建议 - 先判断输出是否回答了用户关于「去AI味助手」的核心问题。 - 再检查结果是否覆盖AI 写作痕迹诊断、自然化改写稿、问题片段说明，以及是否给出明确下一步动作。 - 如果输入材料较少，建议让用户补充目标、受众、限制条件或原始材料后再运行。 ### 运行前准备 - `AISKILLS_BASE_URL`：默认 `https://ai-skills.ai` - `AISKILLS_API_KEY`：必填，用于认证调用 - `AISKILLS_TENANT_ID`：默认 `default`


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