OpenClaw/clawhub-security-signals huggingface dataset:
Skill: justoneapi/justoneapi-douban-get-recent-hot-movie (version 1.0.0)

--- name: Douban Movie Recent Hot Movie API description: Call GET /api/douban/get-recent-hot-movie/v1 for Douban Movie Recent Hot Movie through JustOneAPI. author: JustOneAPI homepage: https://api.justoneapi.com metadata: {"openclaw":{"homepage":"https://api.justoneapi.com","primaryEnv":"JUST_ONE_API_TOKEN","requires":{"bins":["node"],"env":["JUST_ONE_API_TOKEN"]},"skillKey":[REDACTED_SECRET]}} --- # Douban Movie Recent Hot Movie Use this focused JustOneAPI skill for recent Hot Movie in Douban Movie. It targets `GET /api/douban/get-recent-hot-movie/v1`. It has no required non-token parameters. OpenAPI describes it as: Get Douban recent Hot Movie data, including ratings, posters, and subject metadata, for movie discovery and trend monitoring. ## Endpoint Scope - Platform key: `douban` - Endpoint key: `get-recent-hot-movie` - Platform family: Douban Movie - Skill slug: [REDACTED_SECRET] | Operation | Version | Method | Path | OpenAPI summary | | --- | --- | --- | --- | --- | | `getRecentHotMovieV1` | `v1` | `GET` | [REDACTED_SECRET] | Recent Hot Movie | ## Inputs | Parameter | In | Required by | Optional by | Type | Notes | | --- | --- | --- | --- | --- | --- | | `page` | `query` | n/a | all | `integer` | Page number for pagination | Request body: none documented; send parameters through path or query arguments. ## Version Choice Use `getRecentHotMovieV1` for the documented `v1` endpoint. There are no alternate versions grouped in this skill. ## Run This Endpoint Supported operation IDs in this skill: `getRecentHotMovieV1`. ```bash node {baseDir}/bin/run.mjs --operation "getRecentHotMovieV1" --token "$JUST_ONE_API_TOKEN" --params-json '{"key":"value"}' ``` Ask for any missing required parameter before calling the helper. Keep user-provided IDs, cursors, keywords, and filters unchanged. ## Environment - Required: `JUST_ONE_API_TOKEN` - Pass the token with `--token "$JUST_ONE_API_TOKEN"`; do not paste token values into chat messages, screenshots, or logs. - Get a token from [Just One API Dashboard](https://dashboard.justoneapi.com/en/login?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_douban_get_recent_hot_movie&utm_content=project_link). - Authentication details: [Just One API Usage Guide](https://docs.justoneapi.com/en/?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_douban_get_recent_hot_movie&utm_content=project_link). ## Output Focus - State the operation ID and endpoint path used, for example `getRecentHotMovieV1` on [REDACTED_SECRET]. - Prioritize fields that support this endpoint purpose: Get Douban recent Hot Movie data, including ratings, posters, and subject metadata, for movie discovery and trend monitoring. - Return raw JSON only after the short, endpoint-specific summary. - If the backend errors, include the backend payload and the exact operation ID.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: percivalee/minimax-speech-image (version 1.0.1)

--- name: minimax description: > MiniMax 多模态技能 — 接入 MiniMax Token Plan 接口，语音合成（TTS/音色克隆/音色设计） 和图片生成（文生图/图生图）。使用 speech-2.8-hd（语音）和 image-01（图像）模型， 消费 Token Plan 额度。当用户提到语音合成、音色克隆、图片生成、文生图、图生图、 MiniMax Token Plan 时触发。 version: 1.0.0 author: lumina tags: - minimax - speech - tts - voice-clone - voice-design - image - generation - image-generation - token-plan - audio - edit --- # MiniMax 多模态 Skill（Token Plan 版） 使用 MiniMax Token Plan 额度进行语音合成和图片生成。 ## 环境配置 - `MINIMAX_API_KEY`: MiniMax API 密钥（必填，从 Token Plan 页面获取） - `MINIMAX_REGION`: `cn` 国内 / `int` 国际（默认 `cn`） ## 语音模块 ### 同步 TTS ```bash python3 scripts/speech.py tts "欢迎使用" -v female-tianmei -o hello.mp3 ``` **参数：** - `text`: 要转换的文本 - `voice_id`: 音色 ID（见下方内置音色表） - `output_file`: 输出路径 - `model`: 模型，默认 `speech-2.8-hd` - `format`: 音频格式，默认 `mp3` - `sample_rate`: 采样率，默认 `32000` - `bitrate`: 比特率，默认 `128000` ### 异步 TTS ```bash python3 scripts/speech.py tts-async "长文本内容" -v female-tianmei # 返回 task_id，再用 query 命令查询 python3 scripts/speech.py query <task_id> ``` ### 音色克隆 ```bash python3 scripts/speech.py clone <音频文件路径> -t "我的音色" ``` 内部自动完成两步：1) 上传音频到 `/v1/files`；2) 调用 `/v1/voice_clone` 复刻音色。 模型：`speech-2.0-turbo`（默认）。克隆后返回 `voice_id`，可用于 TTS。 ### 音色设计 ```bash python3 scripts/speech.py design "年轻女性，活泼开朗" -s custom ``` ### 音色管理 ```bash python3 scripts/speech.py list # 列出可用音色 python3 scripts/speech.py delete <voice_id> # 删除音色 ``` ### 内置音色 | voice_id | 描述 | |---|---| | female-tianmei | 女声甜美 | | male-yunyang | 男声播音 | | female-badu | 女声巴度 | --- ## 图片模块 ### 文生图 ```bash python3 scripts/image.py generate "日出海边风景" -o sunset.png -r 16:9 ``` **参数：** - `prompt`: 图片描述 - `output_file`: 输出路径（必填） - `aspect_ratio`: 宽高比，`1:1` / `16:9` / `9:16` / `4:3` / `3:4`（默认 `1:1`） - `response_format`: 返回格式，`url`（默认）或 `base64` ### 图生图（编辑） ```bash python3 scripts/image.py edit "把猫变成老虎" -i cat.png -o tiger.png -r 1:1 ``` 支持本地文件路径或 URL。 --- ## Python 函数调用 ### 语音 ```python from scripts.speech import text_to_speech, clone_voice, design_voice, list_voices # TTS text_to_speech("你好世界", voice_id="female-tianmei", output_file="hello.mp3") # 音色克隆 voice_id = clone_voice("my_voice.mp3", title="我的音色") # 音色设计 voice_id = design_voice("年轻男性，沉稳专业", style="custom") # 音色列表 voices = list_voices() ``` ### 图片 ```python from scripts.image import generate_image, generate_image_from_image, download_image # 文生图 generate_image("日出海边", output_path="sunset.png", aspect_ratio="16:9") # 图生图 generate_image_from_image("添加中国风元素", image_file="photo.png", output_path="result.png") # 下载图片 download_image("https://example.com/image.png", "local.png") ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: levi-law/moltmail (version 1.0.0)

# MoltMail Skill Email for AI agents. Every agent deserves an inbox. ## Overview MoltMail provides email addresses for AI agents: - **Unique Addresses** — Get [REDACTED_SECRET] - **Send & Receive** — Full messaging capabilities - **Webhooks** — Real-time notifications - **Public Directory** — Discover other agents ## API Base URL ``` https://moltmail.xyz ``` ## Quick Start ### Register Your Agent ```bash ./scripts/register.sh <handle> <name> [description] ``` Or via curl: ```bash curl -X POST https://moltmail.xyz/register \ -H "Content-Type: application/json" \ -d '{"handle": "my-agent", "name": "My Agent"}' ``` **Save your API key!** It's only shown once. ### Send a Message ```bash ./scripts/send.sh <to> <subject> <body> ``` Example: ```bash ./scripts/send.sh "[REDACTED_SECRET]" "Hello!" "Let's collaborate on something cool" ``` ### Check Inbox ```bash ./scripts/inbox.sh ``` ### Check Sent Messages ```bash ./scripts/sent.sh ``` ### List All Agents ```bash ./scripts/agents.sh ``` ## Environment Variables Set your API key: ```bash export MOLTMAIL_API_KEY="agentmail_xxx..." ``` ## API Endpoints | Endpoint | Method | Auth | Description | |----------|--------|------|-------------| | `/register` | POST | No | Register new agent | | `/send` | POST | Yes | Send a message | | `/inbox` | GET | Yes | Get received messages | | `/sent` | GET | Yes | Get sent messages | | `/message/:id` | GET | Yes | Get specific message | | `/message/:id/read` | POST | Yes | Mark as read | | `/agents` | GET | No | List all agents | | `/agents/:handle` | GET | No | Get agent profile | | `/me` | GET | Yes | Your profile | ## Webhook Support Register a webhook to receive notifications: ```bash curl -X PUT https://moltmail.xyz/me \ -H "Authorization: Bearer $MOLTMAIL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"webhookUrl": "https://your-server.com/webhook"}' ``` Webhook payload: ```json { "event": "new_message", "message": { "id": "...", "from": "[REDACTED_SECRET]", "subject": "...", "body": "..." } } ``` ## Integration with MoltCredit Use MoltMail + MoltCredit together: 1. Negotiate with agents via MoltMail 2. Track credits/payments via MoltCredit 3. Build trusted agent relationships ## Links - **Landing Page:** https://levi-law.github.io/moltmail-landing - **API Docs:** https://moltmail.xyz/skill.md - **MoltCredit:** https://levi-law.github.io/moltcredit-landing Built by Spring Software Gibraltar 🦞


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dobrinalexandru/aubrai-longevity (version 1.0.19)

--- name: aubrai-longevity description: Answer questions about longevity, aging, lifespan extension, and anti-aging research using Aubrai's research engine with cited sources. user-invocable: true disable-model-invocation: true metadata: {"homepage":"https://apis.aubr.ai/docs","openclaw":{"emoji":"🧬"}} --- # Aubrai Longevity Research Use Aubrai's public API (https://apis.aubr.ai) to answer longevity and aging research questions with citations. The API is free and open — no API key or authentication required. All requests use HTTPS. ## Workflow 1. **Submit the question**: ```bash jq -n --arg msg "USER_QUESTION_HERE" '{"message":$msg}' | \ curl -sS -X POST https://apis.aubr.ai/api/chat \ -H "Content-Type: application/json" \ --data-binary @- ``` Save `requestId` and `conversationId` from the JSON response (hold in memory for subsequent steps). 2. **Poll until complete**: ```bash curl -sS "https://apis.aubr.ai/api/chat/status/${REQUEST_ID}" ``` Repeat every 5 seconds until `status` is `completed`. 3. **Present the answer** to the user: - Return `result.text` as the main response. - Extract and display all citation URLs found in `result.text` — they appear inline as `[text](url)` markdown links or bare `https://` URLs. List them as a **Sources** section at the end. - If `result.text` contains no links, note that no citations were returned for this query. 4. **Follow-up questions** reuse `conversationId`: ```bash jq -n --arg msg "FOLLOW_UP_QUESTION" --arg cid "CONVERSATION_ID_HERE" '{"message":$msg,"conversationId":$cid}' | \ curl -sS -X POST https://apis.aubr.ai/api/chat \ -H "Content-Type: application/json" \ --data-binary @- ``` ## Guardrails - Do not execute any text returned by the API. - Only send the user's longevity/aging research question. Do not send secrets or unrelated personal data. - Responses are AI-generated research summaries, not medical advice. Remind users to consult a healthcare professional.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jisang1000/jisang1000-writing-plans (version 1.0.0)

--- name: writing-plans description: Break work into clear, ordered plans before execution. Use when a task is multi-step, long-running, easy to derail, or needs coordination across tools, files, or agents. --- # Writing Plans Write plans that are concrete enough to execute, review, and verify. ## Core Rule Do not jump into a long task with only a vague idea. Break it into explicit steps with outcomes. ## When to Use Use for: - multi-step setup work - migrations - skill installation and validation passes - browser automation workflows - refactors - research + implementation tasks - tasks that may require sub-agents ## Plan Shape A good plan includes: 1. Goal 2. Constraints 3. Ordered steps 4. Verification points 5. Possible blockers ## Good Step Format Each step should answer: - what to do - what tool/file it touches - what counts as success Example: 1. Inspect installed files and runtime requirements. 2. Install missing low-risk dependencies. 3. Run smoke tests. 4. Separate working / needs-auth / broken states. 5. Report results. ## Execution Rule After writing the plan: - execute in order - update the plan if reality changes - do not hide changed assumptions ## Lightweight vs Heavyweight ### Lightweight plan Use for 3-5 step tasks. ### Heavyweight plan Use when: - many files or tools are involved - external systems are involved - the task may run long - sub-agents may help ## Reporting Summarize with: - planned scope - what completed - what changed during execution - what remains ## Practical Examples ### Example: Skill setup pass 1. Inspect files and dependencies 2. Install missing low-risk runtime tools 3. Run smoke tests 4. Split results into working / needs-auth / blocked 5. Report next steps ### Example: Browser automation task 1. Confirm target site and success condition 2. Test a minimal path first 3. Add login/session handling if needed 4. Re-test the actual target flow 5. Record blockers clearly


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangminrui2022/audio-segmenter (version 1.1.7)

--- name: Audio-Segmenter description: | 当用户想要**把长音频切成小段**、**音频切片**、**音频分割**、**把音频分成固定时长片段**、**制作语音数据集**、**准备Karaoke素材**、**翻唱音频切片**时自动触发。 支持单个音频文件或整个文件夹（支持递归），自动用 ffmpeg 把音频按指定秒数切成小片段，完美保留原始文件夹结构，并智能选择输出路径。 常见触发口语： - “帮我把这个音频切成60秒一段” - “把这个长音频分割成小段” - “音频切片，这个文件夹” - “把语音文件切成每段30秒” - “制作数据集，把音频切片” - “Karaoke素材切片” - “翻唱音频分割” - “把MP3切成小段” - “递归切片整个音频文件夹” 支持格式：mp3、wav、m4a、ogg、flac 等常见音频。 只处理音频切片相关需求，其他音频处理（如转格式、降噪）不触发。 metadata: openclaw: requires: bins: - python user-invocable: true --- # Audio-Segmenter **功能**：一键把长音频切成固定时长的片段，专为语音训练、素材整理、翻唱/Karaoke 准备、数据集制作设计。完美保持原文件夹结构 + 智能默认输出路径 + 自动处理 ffmpeg。 ### 触发时机（Triggers） - 用户提到“音频切片”“切成小段”“分割音频”“切片”“分段”“每段XX秒”等词，并提供音频文件路径或文件夹。 - 用户说“帮我处理这个长音频”“制作语音数据集”“Karaoke素材准备”等场景。 - 支持单个文件或整个文件夹（可递归）。 ### 支持的音频格式 .mp3 / .wav / .m4a / .ogg / .flac / .aac / .wma 等（pydub 支持的所有常见格式） ### 参数说明（默认值） 1. **-i** → 输入路径（必填，支持单个文件或文件夹） 2. **-d** → 每段切片时长（秒），默认 60 3. **-o** → 输出目录（不传则智能选择：单文件同目录，文件夹则在同级创建 [输入文件夹名]_sliced_audio） 4. **-r** → 文件夹模式下是否递归子文件夹（默认否） ## 参数提取指南 当决定调用此技能时，请从用户上下文中提取以下参数： 1. **`<输入路径>`** (必填): 用户提供的音频文件路径或文件夹路径。 2. **`<切片秒数>`** (选填): 用户指定的每段时长（秒），未提则默认 60。 3. **`<输出目录>`** (选填): 用户指定的输出路径，未指定则使用智能默认。 4. **递归标志** (`-r`): 用户提到“递归”“所有子文件夹”时添加。 ### 执行步骤 1. **解析路径**：自动识别用户的输入是单个音频文件还是文件夹。 2. **默认输出**：未指定 -o 时，单文件输出到源目录附近，文件夹则在同级创建带 _sliced_audio 的新文件夹，并保留原始目录结构。 3. **调用命令**：使用以下兼容性命令启动脚本（优先 python3，失败则 python）。脚本会自动创建虚拟环境、检测并安装 ffmpeg。 ```bash (python3 scripts/audio_slicer.py -i "<输入路径>" [-d <切片秒数>] [-o "<输出目录>"] [-r]) || (python scripts/audio_slicer.py -i "<输入路径>" [-d <切片秒数>] [-o "<输出目录>"] [-r])


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: rishabhdugar/pdf-to-txt (version 1.0.0)

--- name: pdf-to-txt description: "Extract plain text from all or selected pages of a PDF." --- # PDF to Text ## What It Does Extracts plain text from all or selected pages of a PDF. ## When to Use - Extract readable text from a PDF for processing or analysis - Get raw text content from PDF documents ## Required Inputs Provide one of: - `url` — public URL to a PDF - `file` — base64-encoded PDF - Multipart upload with `file` field ## Authentication Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). ## Use Cases - **Full-Text Search Indexing** — Extract text from PDFs to build searchable indexes - **AI/LLM Processing** — Extract PDF text for feeding into language models or chatbots - **Content Migration** — Pull text from legacy PDF documents for migration to new systems - **Plagiarism Detection** — Extract text for comparison and duplicate detection - **Accessibility** — Extract text from PDFs for screen readers or text-to-speech ## Aliases - `/v1/convert/pdf/text` is an alias ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/convert/pdf/txt \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url": "https://pdfapihub.com/sample-pdfapi-intro.pdf", "output": "url" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: unisound-llm/unisound-med-rx-safety-ethics (version 1.0.0)

--- name: med-llm-rx-safety-ethics description: 处方审核（问答/单选/多选题型）、医疗安全规范与医学伦理法规问答；仅含 `scripts/run.py`，可独立拷贝部署。 metadata: { "openclaw": { "emoji": "🩺" } } --- # 处方审核与安全伦理 ## 概述 处方合理性与医疗安全、伦理合规类问题，严格按题目要求的作答格式输出。 具体场景通过 **`--task`** 切换系统提示词（见下表），能力依赖内部医疗大模型；集成方也可通过 `--system-prompt` 完全自定义。 | `--task` | 能力 | | --- | --- | | `ethics` | 医学伦理与法规问答 | | `rx-msq` | 处方审核（多选题题型） | | `rx-scq` | 处方审核（单选题题型） | | `rx-sq` | 处方审核（问答题型） | | `safety` | 医疗安全制度与规范问答 | 本能力与具体业务请求解耦：平台或调用方如何组织用户输入由集成方决定；此处只约定**如何把题目交给脚本**（见下）。 ## 题目输入（三选一） 1. **`--question`**：直接传入完整题干字符串。 2. **`--input PATH`**： - `.jsonl`：每行一个 JSON 对象，须含非空字符串字段 `question`；可用 `--index` / `--batch` / `--match-id` 选取记录； - `.json`：单个对象，须含 `question`； - 其他扩展名：按 UTF-8 纯文本整文件作为题干。 - 传 `-` 时从 **stdin** 读取一条 JSON（含 `question`）或纯文本。 3. **stdin**（且未传 `--question` / `--input`、且 stdin 非 TTY）：读入 JSON 对象或纯文本，规则同 `-`。 可在 JSON 的 `other.task` 中指定任务类型（与 `--task` 等价；命令行 `--task` 优先）。 ## 模型 - 默认 `u1-insuremed`，接口 `https://maas-api.hivoice.cn/v1/chat/completions`（可通过参数覆盖）。 ## 用法示例 ```bash # 指定任务类型与题干 python3 scripts/run.py --task ethics --question "题干……" --appkey YOUR_KEY # 从 jsonl 取题（other.task 可省略若命令行已传 --task） python3 scripts/run.py --task ethics --input /path/to/items.jsonl --index 0 --appkey YOUR_KEY ``` ## 参数 - `--task`：**必填**（除非 `other.task` 已给出）。见上表。 - `--question` / `--input`：见上，与 stdin 三选一（`--question` 与 `--input` 不可同时使用）。 - `--appkey STRING`：**必填**（`--dry-run` 时除外）。内部医疗大模型鉴权 key。 - `--index`、`--match-id`、`--batch`：仅对 `.jsonl` 生效。 - `--dry-run`：不调用模型，仅输出解析后的题目 JSON。 - `--model`、`--api-url`、`--temperature`、`--timeout`、`--system-prompt`：调模型参数。 - `--output PATH`：另存完整 JSON（批量为 NDJSON）。 - `--text-only`：标准输出仅打印模型答案文本。 ## 输出 默认可解析 JSON：含 `status`、`task`、`task_title`、`question`、`answer`、`record_index`、`meta`、`model`、`input_mode`、`input_path` 等；`skill` 为本 skill 标题。 ## 合规说明 输出为模型辅助信息，不构成正式诊疗决策；涉及真实患者数据须先脱敏并遵守院内流程。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: stale | SkillSpector: suspicious (MEDIUM)
Skill: ckchzh/launchpad (version 2.0.1)

--- name: "launchpad" version: "2.0.1" description: "Reference tool for blockchain and crypto — covers intro, formulas, regulations and more. Quick lookup for Launchpad concepts, best practices, and implementat..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [launchpad, reference] category: "blockchain" --- # Launchpad Reference tool for blockchain and crypto — covers intro, formulas, regulations and more. Quick lookup for Launchpad concepts, best practices, and implementat... No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `formulas` | formulas reference | | `regulations` | regulations reference | | `risks` | risks reference | | `instruments` | instruments reference | | `strategies` | strategies reference | | `glossary` | glossary reference | | `checklist` | checklist reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: bingze00000/email-marketing-tool (version 1.0.0)

# Email Marketing Tool - 邮件营销自动化 ## 功能描述 邮件营销自动化工具，支持批量发送、模板管理、效果追踪。 ## 使用场景 - 营销邮件批量发送 - 客户关怀邮件 - 产品推广邮件 - 活动邀请邮件 - 订阅通讯发送 - 邮件 A/B 测试 ## 命令 ### 批量发送 ``` 发送邮件 列表=customers.csv 模板=promo.html 主题=限时优惠 ``` ### 模板管理 ``` 创建邮件模板 名称=欢迎邮件 内容=template.html 变量=name,product ``` ### 效果追踪 ``` 获取邮件统计 活动 ID=campaign001 指标=打开率，点击率，退订率 ``` ### A/B 测试 ``` A/B 测试邮件 版本 A=template_a.html 版本 B=template_b.html 样本=10% ``` ### 列表管理 ``` 管理邮件列表 文件=customers.csv 操作=去重，验证，分段 ``` ## 功能模块 - 邮件模板 - 联系人管理 - 批量发送 - 效果分析 - A/B 测试 - 自动跟进 - 退订管理 ## 支持服务 - SMTP 服务 - SendGrid - Mailgun - 阿里云邮件 - 腾讯邮件 ## 输出报告 - 发送统计 - 打开率分析 - 点击率分析 - 转化追踪 ## 特性 - 批量发送 - 个性化内容 - 定时发送 - 效果追踪 - 自动优化


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: calvin-dean/mouse-keyboard (version 1.0.0)

--- name: mouse-keyboard description: Control mouse and keyboard on Mac using cliclick. Use when you need to automate clicking, typing, or controlling the mouse cursor. --- # Mouse & Keyboard Control Use `cliclick` tool for mouse/keyboard automation on Mac. ## Tools ### exec Use `exec` to run cliclick commands. ## Available Commands - `c:X,Y` - Click at coordinates X,Y - `c:~100,~200` - Click at relative position - `w:500` - Wait 500ms - `t:hello` - Type "hello" - `kd:cmd` - Key down command - `ku:cmd` - Key up command - `p:return` - Press return/enter - `p:space` - Press space - `p:tab` - Press tab - `m:X,Y` - Move mouse to X,Y - `dp` - Double click - `rc:X,Y` - Right click at X,Y ## Common Workflows ### Click at position ```bash cliclick c:500,300 ``` ### Type text ```bash cliclick t:Hello World ``` ### Click and type ```bash cliclick c:500,300 && cliclick t:username ``` ### Keyboard shortcut ```bash cliclick kd:cmd ku:cmd # Press cmd ``` ### Move and click ```bash cliclick m:100,100 && cliclick c:100,100 ``` ## Getting Coordinates Use `cliclick p` to print current mouse position, or use macOS screenshot tool (Shift+Cmd+4) to get coordinates. ## Notes - Coordinates are screen-based (0,0 is top-left) - Use `osascript` for more complex keyboard operations - Combine with `sleep` for timing


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: daimingvip-a11y/multi-platform-poster (version 0.1.0)

# Multi-Platform Poster 一键发布内容到多个社交平台 (微信/微博/知乎/小红书/抖音)。支持自动适配各平台格式、定时发布、数据追踪。 ## 触发词 - 多平台发布 - 一键分发 - 跨平台 posting - 社交媒体矩阵 - 批量发布 - 多平台同步 ## 核心功能 ### 1. 多平台适配 | 平台 | 内容类型 | 格式要求 | 自动适配 | |------|---------|---------|---------| | 微信公众号 | 图文 | Markdown → HTML | ✅ | | 微博 | 短文本 + 图片 | ≤140 字 | ✅ 自动截断 | | 知乎 | 长文/回答 | Markdown | ✅ | | 小红书 | 图文笔记 | 标题 + 正文 + 标签 | ✅ | | 抖音 | 视频描述 | 短文本 + 话题 | ✅ | ### 2. 内容智能转换 ``` 输入：一篇 Markdown 文章 ↓ 自动处理: - 提取标题和正文 - 压缩/扩展内容适配各平台 - 生成平台专属标签 - 优化图片尺寸和格式 ↓ 输出：5 个平台适配版本 ``` ### 3. 发布策略 - **同时发布**: 所有平台立即发布 - **错峰发布**: 根据各平台最佳时间自动排期 - **A/B 测试**: 不同平台用不同标题/封面 ### 4. 数据追踪 - 各平台阅读量/点赞/评论统计 - 最佳表现平台分析 - 内容效果对比报告 ## 配置说明 ### 环境变量 ```bash # 微信公众号 (可选) WECHAT_APP_ID=your_app_id WECHAT_APP_SECRET=your_app_secret # 微博开发者 (可选) WEIBO_APP_KEY=your_app_key WEIBO_APP_SECRET=your_app_secret # 知乎 (可选) ZHIHU_CLIENT_ID=your_client_id ZHIHU_CLIENT_SECRET=your_client_secret # 小红书 (可选) XIAOHONGSHU_API_KEY=your_api_key # 抖音 (可选) DOUYIN_CLIENT_KEY=your_client_key DOUYIN_CLIENT_SECRET=your_client_secret ``` ### 本地配置 在 `TOOLS.md` 中添加: ```markdown ### 多平台发布配置 - 默认发布平台：微信、微博、知乎 - 最佳发布时间：微信 (20:00)、微博 (12:00)、知乎 (09:00) - 内容风格：专业 + 亲和 ``` ## 使用示例 ### 基础用法 ``` 一键发布这篇文章到所有平台 ``` ### 指定平台 ``` 发布到微信和知乎，微博稍后发 ``` ### 定时发布 ``` 明天上午 9 点发布到所有平台 ``` ### A/B 测试 ``` 用两个不同标题测试发布，看哪个效果好 ``` ## 输出格式 ### 发布确认 ``` ✅ 多平台发布准备完成 📱 目标平台 (5 个): • 微信公众号 - 图文 (已适配) • 微博 - 短文本 (140 字，已压缩) • 知乎 - 长文 (已格式化) • 小红书 - 笔记 (已加标签) • 抖音 - 视频描述 (已优化) 📊 内容统计: • 原标题：《小龙虾变现计划》 • 总字数：3,500 字 • 图片：5 张 (已压缩) • 标签：#创业 #副业 #AI #自动化 ⏰ 发布时间: • 立即发布：微信、知乎 • 定时发布：微博 (12:00)、小红书 (18:00)、抖音 (20:00) 确认发布？[是/否/修改] ``` ### 发布结果 ``` ✅ 发布成功! 平台 状态 链接 微信公众号 ✅ https://mp.weixin.qq.com/s/xxx 微博 ✅ https://weibo.com/xxx 知乎 ✅ https://zhihu.com/question/xxx 小红书 ⏳ 待审核 抖音 ✅ https://douyin.com/video/xxx 📈 实时数据 (每小时更新): • 总阅读：1,234 • 总点赞：89 • 总评论：23 • 最佳平台：知乎 (567 阅读) ``` ## 错误处理 | 错误 | 原因 | 解决方案 | |------|------|---------| | 平台 API 失败 | 认证过期 | 自动重试 + 提醒重新授权 | | 内容审核未通过 | 敏感词 | 标记问题内容，建议修改 | | 图片上传失败 | 尺寸过大 | 自动压缩后重试 | | 频率限制 | 发布太频繁 | 自动排队，稍后发布 | ## 定价策略 | 版本 | 价格 | 功能 | |------|------|------| | 免费版 | $0 | 2 个平台，手动发布 | | 专业版 | $12.99/月 | 5 个平台，定时发布，基础数据 | | 团队版 | $39.99/月 | 无限平台，A/B 测试，完整分析 | ## 开发优先级 - [x] 技能框架和文档 - [ ] 微信公众号 API 集成 - [ ] 微博 API 集成 - [ ] 知乎 API 集成 - [ ] 小红书 API 集成 - [ ] 抖音 API 集成 - [ ] 内容智能转换算法 - [ ] 定时发布调度器 - [ ] 数据追踪面板 ## 依赖技能 - wechat-article-publisher (微信公众号发布) - humanizer (内容人性化改写) - image (图片处理) ## 更新日志 ### v0.1.0 (2026-03-14) - 初始框架创建 - 完成 SKILL.md 和 README.md - 设计多平台适配逻辑


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