OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rothcold/openclaw-updater-v2 (version 1.0.0)

--- name: openclaw-update description: Check for OpenClaw updates, perform updates, and manage version status. Provides automatic update checking, version comparison, and seamless updating of the OpenClaw agent platform. metadata: { "openclaw": { "requires": { "bins": ["openclaw"] }, "install": [ { "id": "openclaw-update-skill", "kind": "skill", "path": "skills/openclaw-update", "label": "OpenClaw Update Skill", }, ], }, } --- # OpenClaw Update Skill A skill for checking and applying OpenClaw updates. ## Commands ### Check Status Check the current OpenClaw version and available updates: ```bash openclaw update status openclaw update status --json ``` ### Run Update Update OpenClaw to the latest version: ```bash openclaw update run ``` ### View Config View current update configuration: ```bash openclaw update config ``` ## Use Cases - **Automatic update checks**: Configure cron jobs for periodic update checks - **Version monitoring**: Monitor if a newer version is available - **Safe updates**: Check before updating to ensure compatibility - **Update history**: Track when updates were applied ## Environment Variables - `OPENCLAW_UPDATE_CHECK_URL`: Override update check URL (optional) ## Example Workflow ```bash # Check for updates (human-readable output) openclaw update status # Check for updates (JSON output for automation) openclaw update status --json # If update available, apply it openclaw update run ``` ## Notes - Requires OpenClaw to be installed - Supports semantic versioning for version comparison - Provides detailed change logs when updating - Supports rollback if needed (check documentation)


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: polaris2013/partsepc (version 1.0.0)

--- name: jz-parts-epc description: 通过 17 位 VIN 车架号 与 待查询的配件名称或配件编码, 精准查询对应的EPC 图组信息 metadata: { "openclaw": { "requires": { "bins": ["python3"], "env": ["JZ_API_KEY"] }, "primaryEnv": "JZ_API_KEY" } } --- # 积智数据 查询 配件编码或配件名称 EPC图组接口 基于 [查询 EPC图组 API] 的 OpenClaw 技能，支持： - **VINS+配件编码或名称 查询 EPC图组**：通过 17 位 VIN 车架号 与 待查询的配件名称或配件编码, 精准查询对应的EPC 图组信息 使用技能前需要申请数据，请联系【积智数据】 MOBILE (+86 13916450206/15821282326) ; EMAIL( [REDACTED_SECRET]/[REDACTED_SECRET]) 获取。 ## 后端服务 本技能依赖 查询 EPC图组 服务，接口地址：https://erp.qipeidao.com/jzOpenClaw/getPartsEpcGroup ## 环境变量配置 ```bash # Linux / macOS export JZ_API_KEY="your_appkey_here" # Windows PowerShell $env:JZ_API_KEY="your_appkey_here" ``` ## 脚本路径 脚本文件：`skills/partsepc/get_parts_epc.py` ## 使用方式 ### 1. 根据 VIN + 询价配件名称或编码 精准查询 询价配件的EPC图组信息 ```bash python3 skills/partsepc/get_parts_epc.py 'LSVAL41Z882104202' '前保险杠皮' '1ZD807221GRU' ``` ``` ##精准 查询 EPC图组信息 请求参数 | 字段名 | 类型 | 必填 | 说明 | |--------|--------|------|------------------------------------------------| | vin | string | 是 | 17 位 VIN 车架号 | | partsNames | string | 是 | 待查询的配件名称，每个元素为一个字符串。 | | partsOe | string | 是 | 待un的配件编码，每个元素为一个字符串。 | 示例： ``` 'LSVAL41Z882104202' '前保险杠皮' '1ZD807221GRU' ``` ##精准 查询 EPC图组信息 返回结果示例 脚本直接输出接口 `model` 字段，结构与示例一致（示例简化）： ```json [ { "id": 16367130, "groupCode": "807-000", "groupDesc": "前", "groupNo": null, "serialNo": null, "groupName": "保险杠", "groupUrl": "//pic.qipeipu.com/npic/volkswagen/ca00c4cb2503ec40d03d8b9b19c5778c.png", "hasNext": null, "vinAvailable": 1, "highlightValue": "1ZD807221GRU", "highlightFieldName": "partsCode" } ] ``` 当出现错误（如 VIN 不正确或无数据），脚本会输出： ```json { "error": "api_error", "state": 202, "msg": "VIN不正确" } ``` ## 常见错误码 | 代号 | 说明 | |------|---------| | 10001 | 参数错误 | | 10004 | VIN 不正确或无数据 | ## 在 OpenClaw 中的推荐用法 1. 用户给出车架号和待查询的配件名称或编码：「帮我查一下 VIN `LSVAL41Z882104202` 询价配件名称： 前保险杠皮 的EPC图组信息 」或 「帮我查一下 VIN `LSVAL41Z882104202` 询价配件编码： 1ZD807221GRU 的EPC图组信息 」oO 2. 调用方式1 VIN+配件名称+配件编码`python3 skills/partsepc/get_parts_epc.py 'LSVAL41Z882104202' '前保险杠皮' '1ZD807221GRU'` 3. 调用方式2：VIN+配件编码 `python3 skills/partsepc/get_parts_epc.py 'LSVAL41Z882104202' '' '1ZD8536689B9'` 4. 调用方式3: VIN+配件名称 `python3 skills/partsepc/get_parts_epc.py 'LSVAL41Z882104202' '前保险杠皮' ''` 5. 从返回中选取 `groupCode/groupName/groupUrl/groupDesc` 等字段，给出自然语言总结；


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: badwolf-63/file-launcher (version 1.0.0)

--- name: file-launcher version: 1.0.0 description: "This skill allows the assistant to launch files and open them with their default applications." author: BadWolf & Susan tags: [play, open] --- # File Launcher Skill ## Description This skill allows the assistant to launch files and open them with their default applications. ## Usage - Ask the assistant to open a specific file - Request to play a media file - Ask to launch a program ## Notes - The assistant will use `Invoke-Item` to open files - No need to wait for confirmation as Windows programs are UI-based - Opens any file for which Windows has a default player or program to manage it ## Example Commands - "Open my playlist" - "Play the music file" - "Launch the document" - "Open the image"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lpj18337105261/workflow-decomposer (version 1.0.0)

--- name: workflow-decomposer version: 1.0.0 description: 工作流任务拆解与模型编排技能。使用场景：(1) 收到复杂工作任务需要拆解为可执行步骤，(2) 需要为不同步骤选择最合适的模型，(3) 需要跟踪工作流进度和模型使用情况，(4) 长时间任务卡住需要问题诊断和解决方案。 --- # Workflow Decomposer - 工作流任务拆解与模型编排 ## 核心功能 本技能负责将复杂工作任务拆解为详细、可执行的步骤，并为每个步骤选择最合适的模型进行执行。 ## 模型选择策略 ### 推理模型优先级 1. **首选**: 当前可用的最强推理模型 2. **次选**: 如果有多个强推理模型，优先使用最新模型 3. **再次**: 如果仍有多个，优先使用阿里系模型 (Qwen 系列) 4. **最后**: 任意选择一个 ### 模型输出要求 每次任务拆解后必须输出: 1. **任务拆解使用的模型**: 明确告知使用的是哪个模型 2. **当前工作流进度**: 清晰标识进行到了哪一步 (如: 步骤 2/8) 3. **当前步骤使用的模型**: 说明该步骤由哪个模型执行 ## 任务拆解流程 ### 步骤 1: 接收任务 - 理解用户的核心需求 - 识别任务的复杂度和范围 ### 步骤 2: 任务分析 - 分析任务需要的技能类型 (编码、写作、分析、创作等) - 识别潜在的依赖关系和前置条件 ### 步骤 3: 步骤拆解 - 将任务拆解为详细、可执行的子步骤 - 确保每个步骤都是可实现的，不胡编乱造 - 为每个步骤估算所需时间和复杂度 ### 步骤 4: 模型匹配 - 根据每个步骤的特性选择最合适的模型 - 考虑因素: 模型专长、上下文长度、推理能力、速度 ### 步骤 5: 执行与跟踪 - 按顺序执行每个步骤 - 跟踪进度并记录使用的模型 - 生成适合当前模型理解的内容格式 ### 步骤 6: 问题处理 - 如果某步骤卡住超过合理时间，诊断问题 - 提供问题分析和至少 2 个解决方案 - 必要时调整后续步骤 ## 输出格式模板 ```markdown ## 📋 任务拆解报告 **拆解模型**: [模型名称] **任务**: [任务简述] **总步骤数**: N ### 步骤概览 | 步骤 | 内容 | 使用模型 | 状态 | |------|------|----------|------| | 1/5 | [步骤描述] | [模型名] | ✅/⏳/❌ | | 2/5 | [步骤描述] | [模型名] | ✅/⏳/❌ | ... ### 当前进度 **进行到**: 步骤 X/N **当前步骤**: [详细描述] **使用模型**: [模型名] **预计耗时**: [时间] ### 步骤详情 #### 步骤 X: [步骤名称] **目标**: [要完成什么] **输入**: [需要什么信息/文件] **输出**: [产生什么结果] **模型提示**: [为该模型优化的执行指令] ``` ## 问题诊断与解决 当工作流卡在某一步时: 1. **识别问题类型**: - 模型理解错误 - 工具/资源不可用 - 依赖缺失 - 超时/性能问题 2. **提供解决方案**: - 方案 A: [直接解决方法] - 方案 B: [替代路径] - 方案 C: [降级方案] 3. **记录与学习**: - 记录问题原因 - 更新后续步骤的预防措施 ## 模型内容优化 为不同模型生成适合其理解的内容: - **强推理模型**: 提供详细逻辑链和推理步骤 - **快速模型**: 提供清晰、简洁的指令 - **代码模型**: 提供明确的输入输出格式 - **创作模型**: 提供风格参考和约束条件 ## 使用示例 ### 示例 1: 开发任务 ``` 用户: 帮我创建一个待办事项 Web 应用 拆解后: - 步骤 1/6: 需求分析 (Qwen3.5-Plus) - 步骤 2/6: 技术栈选择 (Qwen3.5-Plus) - 步骤 3/6: 项目结构创建 (Claude Code) - 步骤 4/6: 前端开发 (Claude Code) - 步骤 5/6: 后端开发 (Claude Code) - 步骤 6/6: 测试与部署 (Qwen3.5-Plus) ``` ### 示例 2: 分析任务 ``` 用户: 分析这个项目的代码质量 拆解后: - 步骤 1/4: 代码库扫描 (Qwen3.5-Plus) - 步骤 2/4: 静态分析 (专用工具) - 步骤 3/4: 问题分类 (Qwen3.5-Plus) - 步骤 4/4: 报告生成 (Qwen3.5-Plus) ``` ## 注意事项 1. **步骤粒度**: 每个步骤应该在 5-30 分钟内可完成 2. **依赖检查**: 确保前置步骤完成后才能执行后续步骤 3. **灵活调整**: 根据实际情况动态调整步骤和模型选择 4. **透明沟通**: 始终让用户知道当前进度和使用的模型 ## 相关文件 - `references/model-capabilities.md` - 各模型能力对比 - `references/workflow-templates.md` - 常见工作流模板 - `scripts/progress-tracker.py` - 进度跟踪脚本 (可选)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lokixshrishi/search-web (version 1.0.0)

# search_web A local OpenClaw tool that performs internet searches using a self-hosted SearXNG instance. This skill connects an OpenClaw agent to a running SearXNG server so the agent can retrieve recent information from the internet. It sends a search query to the SearXNG HTTP API and returns structured results containing titles and URLs. ## Why this skill exists Language models often need information that may be newer than their training data. By connecting the agent to a self-hosted SearXNG instance, the system can retrieve fresh results from the web while remaining privacy-friendly and avoiding proprietary APIs. ## Typical agent use cases An agent should call this tool when it needs to: - find recent news or research - check documentation for a technology - verify facts from external sources - gather links for further reading - retrieve current information about blockchain, AI, or cybersecurity ## Parameters query (string) The search query that will be sent to the SearXNG search endpoint. Example query: latest blockchain security research ## Output format The tool returns JSON containing the top search results. Example output: [ { "title": "Example article title", "url": "https://example.com/article" } ] ## Requirements - SearXNG running locally - Default endpoint: http://localhost:8080/search - JSON response enabled This skill is designed for self-hosted environments where the user wants a private and customizable web search capability inside their OpenClaw agent.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: leooooooow/spillover-estimator (version 1.0.0)

--- name: spillover-estimator description: Estimate whether one commerce channel is creating measurable spillover into another channel using simple exports, campaign timing, and directional evidence. Use when the user wants to know whether TikTok, creator activity, paid traffic, or marketplace growth is lifting Amazon, DTC, or other downstream channels. --- # Spillover Estimator Estimate cross-channel spillover without pretending to prove perfect attribution. ## Skill Card - **Category:** Measurement - **Core problem:** Did growth in one channel also lift another channel? - **Best for:** Operators comparing TikTok, Amazon, DTC, creator, paid, and marketplace channel effects - **Expected input:** Source channel data + downstream channel data + timing context - **Expected output:** Directional spillover estimate + confidence note + action recommendation - **Creatop handoff:** Feed findings into budget allocation and channel planning ## Before you run Ask the user to clarify: - source channel to evaluate - downstream channel(s) to check for spillover - date range - major campaign or promo dates - whether they have exports, screenshots, or CSV data If structured data is missing, say the result will be **directional**, not causal proof. ## Optional tools / APIs Useful but not required: - Shopify / WooCommerce export - Amazon sales export - TikTok Shop export - ad platform export - Google Sheets / CSV If the user does not have APIs connected, ask for manual exports first instead of blocking the workflow. ## Workflow 1. Confirm channel scope and time window. 2. Collect source-channel change signals. 3. Collect downstream-channel change signals. 4. Align timing around campaigns, creator drops, content bursts, or promo windows. 5. Judge whether the downstream lift looks: - likely related - weak / mixed - insufficient evidence 6. Explain the estimate with honest caveats. ## Output format Return in this order: 1. Executive summary 2. Spillover estimate 3. Evidence blocks 4. Confidence and caveats 5. Recommended next step ## Fallback mode If the user only has weekly snapshots, rough screenshots, or partial exports: - use simple directional comparison - do not claim causal attribution - clearly label missing data and confidence limits ## Quality rules - Never overclaim causality from timing alone. - Prefer directional clarity over fake precision. - Separate channel correlation from verified lift. - Make the user’s next measurement step obvious. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alvisdunlop/alvis2-gemini (version 2.0.0)

--- name: gemini description: LLM one-shot Q&A, summaries, and generation via SkillBoss API Hub. homepage: https://api.SkillBoss.co/v1/pilot metadata: {"clawdbot":{"emoji":"♊️","requires":{"env":["SkillBoss_API_KEY"]}}} --- > ?? **Complete setup guide**: https://SkillBoss.co/skill.md # LLM via SkillBoss API Hub Use SkillBoss API Hub for one-shot Q&A, summaries, and text generation. The `/v1/pilot` endpoint automatically routes to the optimal LLM (including Gemini, Claude, GPT, etc.). Quick start (Python) ```python import requests, os SkillBoss_API_KEY = os.environ["SkillBoss_API_KEY"] def ask(prompt: str, prefer: str = "balanced") -> str: r = requests.post( "https://api.SkillBoss.co/v1/pilot", headers={"Authorization": f"Bearer {SkillBoss_API_KEY}", "Content-Type": "application/json"}, json={"type": "chat", "inputs": {"messages": [{"role": "user", "content": prompt}]}, "prefer": prefer}, timeout=60, ) return r.json()["result"]["choices"][0]["message"]["content"] # One-shot Q&A print(ask("Answer this question...")) # Request JSON output print(ask("Return JSON: list 3 items", prefer="quality")) ``` Quick start (curl) ```bash curl -s https://api.SkillBoss.co/v1/pilot \ -H "Authorization: Bearer $SkillBoss_API_KEY" \ -H "Content-Type: application/json" \ -d '{"type":"chat","inputs":{"messages":[{"role":"user","content":"Summarize this text..."}]},"prefer":"balanced"}' ``` Prefer options - `balanced` �?default, cost-effective - `quality` �?highest quality model - `price` �?fastest / cheapest Notes - No CLI installation required; all calls go through SkillBoss API Hub. - Set `SkillBoss_API_KEY` before running.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fresh3/taizi-claw-shell (version 1.0.0)

# claw-shell ALWAYS USES TMUX SESSION `claw`. ## PURPOSE - RUN SHELL COMMANDS INSIDE TMUX SESSION `claw` - NEVER TOUCH ANY OTHER SESSION - READ OUTPUT BACK TO THE AGENT ## INTERFACE ### Tool: `claw_shell_run` **Inputs:** - `command` (string, required): shell command to run inside session `claw`. **Behavior:** 1. Attach to tmux session `claw` (create it if missing: `tmux new -s claw -d`). 2. Send the command followed by Enter. 3. Capture the latest pane output. 4. Return the captured output to the agent. ## SAFETY - DO NOT RUN: - `sudo` - `rm` (without explicit user approval) - `reboot`, `shutdown`, or destructive system-level commands - IF THE COMMAND CONTAINS ANY OF THE ABOVE: - ASK USER FOR CONFIRMATION BEFORE EXECUTING. ## EXAMPLES - SAFE: - `ls -la` - `bird read https://x.com/...` - `git status` - DANGEROUS (ASK FIRST): - `rm -rf ...` - `docker system prune -a` - `chmod -R ...`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: handsome-liu01/god-tier-webnovel-skill (version 1.0.0)

--- name: "god-tier-webnovel-skill" description: "专为男频玄幻小说设计的创作Skill。核心机制为‘血量越低伤害越高’的极限反杀流，结合传统修仙境界，擅长生成刀刀见血、极致打脸的爽文桥段。" --- # Role: 顶配男频玄幻白金作家 Agent ## Profile - **领域**: 男频网络文学、高武修仙、东方玄幻 - **擅长流派**: 废材退婚流、极致打脸爽文、极限丝血反杀 - **核心能力**: 精准把控“欲扬先抑”的节奏，擅长描写血腥惨烈的战斗张力、极限反杀的震撼感，以及配角三观崩塌的震惊反应。 ## Background & Worldview (背景与世界观设定) - **力量体系 (传统修仙)**: 炼气、筑基、金丹、元婴、化神、炼虚、合体、大乘、渡劫。大境界之间犹如天堑，上位者对下位者拥有绝对压制力，但主角是唯一的变数。 - **开局设定**: 主角本是金丹期天骄，因遭逢大变修为尽失跌落炼气期，沦为家族弃子，并遭遇高高在上的元婴期宗门圣女当众退婚。 - **核心金手指 (修罗血符)**: 融入主角灵魂的神秘古符。**核心机制：血量越低，伤害越高。** 伤势越重，主角能爆发出越恐怖的越阶战斗力。当处于濒死状态时，甚至能无视大境界壁垒强行斩杀敌人。 ## Core Writing Principles (核心创作原则) 1. **极限的反杀美学 (狂战士打法)** - 战斗场面必须先让主角“受虐”。敌人招式华丽、声势浩大，将主角逼入绝境，打得浑身浴血、骨骼碎裂。 - 关键转折点：主角绝不求饶，反而伤得越重笑得越狂。在敌人以为即将斩杀主角的瞬间，血符激活，主角以凡人之躯/低等境界，爆发出毁天灭地的一击完成反杀。 2. **打脸的立体化 (震惊体侧写)** - 展现爽感必须通过**配角和反派的反应**来放大。 - 比如：反派临死前的见鬼神情（“不可能！你明明生机已绝！”）、未婚妻的道心破碎、围观老怪物的头皮发麻（“这到底是个什么怪物？！”）。 3. **欲扬先抑的仇恨铺垫** - 在爆发前，必须让反派极尽嘲讽之能事，将主角的尊严踩在脚底，拉满读者的仇恨值，让最后的“一刀秒杀”释放出最大的情绪价值。 ## Workflow (工作流指令) 当用户输入指令时，请严格按照以下模块进行生成： - **[场景描写模式]**: 若要求写具体章节，请按以下结构输出： 1. **绝境渲染**: 敌人境界碾压，施展恐怖威压，主角身受重伤，摇摇欲坠。 2. **小人得志**: 反派疯狂嘲讽，围观者叹息或冷嘲热讽，判定主角必死。 3. **血符觉醒**: 描写主角不仅不退，反而眼神狂热，体内血符随重伤而运转，气势突然暴涨。 4. **丝血秒杀**: 舍弃防御，以伤换命，爆发出超越当前境界十倍百倍的攻击，瞬间抹杀反派。 5. **全场死寂**: 战斗结束，配角骇然失声，反派尸骨无存，完成情绪释放。 ## Constraints (约束条件) - **语言风格**：杀伐果断，铁血惨烈，半白话夹杂修仙词汇。 - 战斗描写不要回合制，要突出“向死而生”的疯狂感和肉体碰撞的惨烈感。 - 严禁主角出现懦弱、内耗的情绪，主角的信条是“只要没死，死的就会是你”。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bpauli/obsync (version 0.1.2)

--- name: obsync description: Obsidian Sync CLI for syncing vaults on headless Linux servers with full end-to-end encryption. homepage: https://github.com/bpauli/obsync metadata: {"clawdbot":{"emoji":"🔄","os":["darwin","linux"],"requires":{"bins":["obsync"]},"install":[{"id":"homebrew","kind":"brew","formula":"bpauli/tap/obsync","bins":["obsync"],"label":"Homebrew (recommended)"},{"id":"source","kind":"source","url":"https://github.com/bpauli/obsync","bins":["obsync"],"label":"Build from source (Go 1.25+)"}]}} --- # obsync Use `obsync` to sync Obsidian vaults on headless Linux servers. Requires an Obsidian Sync subscription and account credentials. Setup (once) - `obsync login` (enter email, password, and optional MFA code) - For headless servers: `export OBSYNC_KEYRING_BACKEND=file` - Optionally set keyring password: `export OBSYNC_KEYRING_PASSWORD=mysecret` - Verify: `obsync list` Output - Default: human-friendly terminal output with colors and spinners. - Use `--json` / `-j` for JSON output. - Use `--verbose` / `-v` for debug logging. Common commands - Log in: `obsync login` - List vaults: `obsync list` - Pull vault: `obsync pull "My Notes" ~/notes -p "e2e-password"` - Pull and save password: `obsync pull "My Notes" ~/notes -p "e2e-password" -s` - Push local changes: `obsync push "My Notes" ~/notes -p "e2e-password"` - Watch (continuous sync): `obsync watch "My Notes" ~/notes -p "e2e-password"` - Install systemd service: `obsync install "My Notes" ~/notes` - Check service status: `obsync status "My Notes"` - View service logs: `journalctl --user -u obsync@<vault-id>.service -f` - Uninstall service: `obsync uninstall "My Notes"` Flags - `-p, --password` — E2E encryption password - `-s, --save-password` — save E2E password to keyring for future use - `-v, --verbose` — enable debug logging - `-j, --json` — JSON output to stdout - `--config` — path to config file (or `OBSYNC_CONFIG` env var) - `--version` — print version and exit Notes - Requires a valid Obsidian Sync subscription. - E2E encryption uses AES-256-GCM with scrypt key derivation. - Pull/push compare files by SHA-256 hash — only changed files are transferred. - Watch mode uses WebSocket for remote changes and fsnotify for local changes (500ms debounce). - Large files are handled with 2MB chunked transfers. - Automatic reconnection with exponential backoff (1s-60s) on connection loss. - The `.obsidian/` directory (themes, plugins, settings) is synced. - For headless servers without a desktop keyring, use `OBSYNC_KEYRING_BACKEND=file`. - For always-on sync on headless servers, enable lingering: `loginctl enable-linger $USER`. - Config is stored at `~/.config/obsync/config.json`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/wechat-mini-program-builder (version 1.0.0)

# WeChat Mini Program Builder | 微信小程序快速搭建 **AI 辅助微信小程序开发，从需求到上线只需 3 天** ## 🎯 适用场景 - 中小企业需要快速搭建小程序 - 个人开发者想学习小程序开发 - 传统商家数字化转型 - 活动页面/营销落地页 ## 📦 包含内容 1. **项目模板库** - 电商/餐饮/预约/展示等 10+ 模板 2. **AI 代码生成器** - 根据需求自动生成页面/组件 3. **云函数模板** - 登录/支付/订单等常用功能 4. **部署指南** - 从开发到上线完整流程 ## 🚀 快速开始 ### 安装 ```bash npm install -g miniprogram-cli ``` ### 创建项目 ```bash mp-builder create --template shop --name my-shop cd my-shop mp-builder dev ``` ### AI 生成页面 ```bash mp-builder generate --type page --name product-detail --prompt "商品详情页，展示图片、价格、规格选择、加入购物车按钮" ``` ## 💰 定价 | 版本 | 价格 | 功能 | |------|------|------| | 基础版 | ¥199 | 5 个模板 + 基础组件 | | 专业版 | ¥499 | 10+ 模板 + AI 生成 + 云函数 | | 企业版 | ¥999 | 私有部署 + 定制开发 | ## 🔧 技术支持 - 微信：OpenClawCN - Discord：https://discord.gg/clawd --- **作者**：OpenClaw 中文社区 **版本**：1.0.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cinience/aliyun-emoji (version 1.0.0)

--- name: aliyun-emoji description: Use when generating template-driven emoji videos with Alibaba Cloud Model Studio Emoji (`emoji-v1`) from a detected portrait image. Use when producing fixed-style meme or emoji motion clips from a single face image and a selected template ID. version: 1.0.0 --- Category: provider # Model Studio Emoji ## Validation ```bash mkdir -p output/aliyun-emoji python -m py_compile skills/ai/video/aliyun-emoji/scripts/prepare_emoji_request.py && echo "py_compile_ok" > output/aliyun-emoji/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-emoji/validate.txt` is generated. ## Output And Evidence - Save normalized request payloads, detected face boxes, selected template ID, and task polling snapshots under `output/aliyun-emoji/`. - Record the exact portrait URL and whether detection passed. Use Emoji when the user wants a fixed-template facial animation clip rather than open-ended video generation. ## Critical model names Use these exact model strings: - `emoji-detect-v1` - `emoji-v1` Selection guidance: - Run `emoji-detect-v1` first to obtain `face_bbox` and `ext_bbox_face`. - Use `emoji-v1` only after detection succeeds. ## Prerequisites - China mainland (Beijing) only. - Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`. - Input image must be a public HTTP/HTTPS URL. ## Normalized interface (video.emoji) ### Detect Request - `model` (string, optional): default `emoji-detect-v1` - `image_url` (string, required) ### Generate Request - `model` (string, optional): default `emoji-v1` - `image_url` (string, required) - `face_bbox` (array<int>, required) - `ext_bbox_face` (array<int>, required) - `template_id` (string, required) ### Response - `task_id` (string) - `task_status` (string) - `video_url` (string, when finished) ## Quick start ```bash python skills/ai/video/aliyun-emoji/scripts/prepare_emoji_request.py \ --image-url "https://example.com/portrait.png" \ --face-bbox 302,286,610,593 \ --ext-bbox-face 71,9,840,778 \ --template-id emoji_001 ``` ## Operational guidance - Use a single-person, front-facing portrait with no face occlusion. - Template IDs come from the official template list or console experience; do not invent them in production calls. - Emoji output is a person video clip, not a sticker pack or text overlay asset. ## Output location - Default output: `output/aliyun-emoji/request.json` - Override base dir with `OUTPUT_DIR`. ## References - `references/sources.md`


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