OpenClaw/clawhub-security-signals huggingface dataset:
Skill: shawn92/ftshare-fund-data (version 1.0.0)

--- name: FTShare-fund-data description: 基金数据技能集（market.ft.tech）。覆盖基金基本信息、净值历史、累计收益率、基金概览列表、支持基金标的列表。用户询问基金详情、基金净值、基金收益、基金列表时使用。 --- # FT 基金数据 Skills 本 skill 是 `FTShare-fund-data` 的**统一路由入口**。 根据用户问题，从下方「能力总览」或「询问方式与子 skill 对应表」匹配对应子 skill，然后通过 `run.py` 执行并解析响应。 > 所有接口均以 `https://market.ft.tech` 为基础域名。 --- ## 调用方式（唯一规则） `run.py` 与本文件（`SKILL.md`）位于同一目录。执行时： 1. 取本文件的绝对路径，将末尾 `/SKILL.md` 替换为 `/run.py`，得到 `<RUN_PY>` 。 2. 调用：`python <RUN_PY> <子skill名> [参数...]` ```bash # 示例（<RUN_PY> 为实际绝对路径） python <RUN_PY> fund-basicinfo-single-fund --institution-code 000001 python <RUN_PY> fund-cal-return-single-fund-specific-period --institution-code 159619 --cal-type 1Y python <RUN_PY> fund-nav-single-fund-paginated --institution-code 000001 --page 1 --page-size 50 python <RUN_PY> fund-overview-all-funds-paginated --page 1 --page-size 20 python <RUN_PY> fund-support-symbols-all-funds-paginated --page 1 --page-size 20 ``` > `run.py` 内部通过 `__file__` 自定位，无论安装在何处都能正确找到各子 skill 的脚本。 --- ## 基金 — 询问方式与子 skill 对应表 | 询问方式（用户常说的词） | 子 skill | |------------------------|----------| | **基金基本信息**、某只**基金详情**、**基金管理人/基金经理**、**基金类型/投资目标** | `fund-basicinfo-single-fund` | | **基金累计收益率**、**近1年/近3个月收益**、**YTD 收益**、**基金收益曲线** | [REDACTED_SECRET] | | **基金净值**、**单位净值/累计净值**、**日增长率**、**基金净值历史** | `fund-nav-single-fund-paginated` | | **基金概览**、**所有基金信息**、**基金列表概况** | [REDACTED_SECRET] | | **支持的基金列表**、**基金代码清单**、**所有基金标的** | [REDACTED_SECRET] | --- ## 能力总览 - **`fund-basicinfo-single-fund`**：查询指定基金基础信息（名称、管理人、经理、类型、投资目标等）。必填：`--institution-code`（6 位基金代码）。若用户仅给基金名称，先通过 [REDACTED_SECRET] 或 [REDACTED_SECRET] 映射代码再查。 - **[REDACTED_SECRET]**：查询指定基金在指定区间的累计收益率时间序列。必填：`--institution-code`、`--cal-type`（1M/3M/6M/1Y/3Y/5Y/YTD）。建议先完成名称到代码映射后再调用。 - **`fund-nav-single-fund-paginated`**：查询指定基金净值历史（分页）。必填：`--institution-code`；可选：`--page`、`--page-size`。建议先完成名称到代码映射后再调用。 - **[REDACTED_SECRET]**：查询所有基金概览信息（分页）。可选：`--page`、`--page-size`。 - **[REDACTED_SECRET]**：查询所有支持基金的标的列表（分页）。可选：`--page`、`--page-size`。 --- ## 使用流程 1. **记录本文件绝对路径**，将 `/SKILL.md` 替换为 `/run.py` 得到 `<RUN_PY>`。 2. **理解用户意图**，从「询问方式与子 skill 对应表」或「能力总览」匹配子 skill 名称。 3. **若用户给的是基金名称而非代码**：先调用 [REDACTED_SECRET] 或 [REDACTED_SECRET] 获取候选，确定 6 位 `institution-code`。 4. （可选）读取 `sub-skills/<子skill名>/SKILL.md` 了解接口与参数。 5. **执行**：`python <RUN_PY> <子skill名> [参数...]`，获取 JSON 输出（详情/净值/收益统一使用 `--institution-code`）。 6. **解析并输出**：以表格或要点形式展示给用户；若候选不唯一，先让用户确认再查询指标。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/amazon-private-label (version 1.0.0)

--- name: amazon-private-label description: "Private label launch playbook — product selection, branding, supplier management, differentiation strategy" metadata: nexscope: category: amazon --- # Amazon Private Label Private label launch playbook — product selection, branding, supplier management, differentiation strategy **Supported platforms:** Amazon (US, UK, DE, CA, JP, AU, and all marketplaces). Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope/amazon-private-label ``` ## Usage ``` Help me with amazon private label for my e-commerce business. ``` ## Capabilities - Private label launch playbook - product selection - branding - supplier management - differentiation strategy ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pe1984/english-phonetics-batch (version 1.0.0)

--- name: english-phonetics-batch description: 批量为英文单词添加、检查和标注美式国际音标（IPA）。支持从文本文件批量导入单词，自动查询每个单词的美式发音音标，验证现有音标正确性，并输出格式化结果。 --- # English Phonetics Batch - 英文单词批量音标标注工具 **当以下情况时使用此 Skill：** - 需要批量给英文单词列表添加**美式国际音标（IPA）** - 需要检查现有单词表中的音标是否正确 - 需要将纯单词列表转换为带音标的词汇表 - 需要验证修复高考/四级/六级/雅思/托福等词汇表中损坏的音标 - 需要从编码损坏的文档中恢复问号占位的音标 ## Features - ✅ 自动查询每个单词的美式发音国际音标 - ✅ 支持批量处理从几十到几千个单词 - ✅ 检查现有音标正确性，标记并修复错误音标（自动修复问号占位符） - ✅ **自动重试** - 指数退避重试网络错误和限流 - ✅ **统计报告** - 显示成功率和处理摘要 - ✅ 输出多种格式：纯文本、Markdown、CSV - ✅ 美式发音优先，自动优先选择美式音标 - ✅ 处理结果包含单词 + 音标 + 词性 - ✅ 使用免费 API，不需要 API 密钥 - ✅ 可配置延迟和重试次数，灵活适应不同大小列表 ## Quick Start ### Command Line ```bash # Basic bulk processing (one word per line) cd scripts/ python english_phonetics_batch.py input_words.txt output_with_phonetics.txt # Check and fix existing file with corrupted phonetics (question marks from encoding errors) python english_phonetics_batch.py input.txt output.txt --check # Output as CSV for spreadsheets python english_phonetics_batch.py words.txt output.csv --format csv # Large vocabulary list - increase delay for safety, more retries python english_phonetics_batch.py words.txt output.txt --delay 500 --retries 5 ``` ### Python API ```python import sys sys.path.insert(0, [REDACTED_SECRET]) from english_phonetics_batch import PhoneticsBatch processor = PhoneticsBatch() results = processor.process_words(["apple", "banana", "cherry"]) processor.save_results(results, "output.txt", format="text") ``` ## Documentation - **[Usage Examples](references/usage-examples.md)** - Complete examples for common use cases - **[API Notes & IPA Reference](references/api-notes.md)** - Technical details about the API and American IPA symbols ## Output Formats ### Text format (default): ``` apple /ˈæpl/ noun banana /bəˈnænə/ noun cherry /ˈtʃeri/ noun ``` ### CSV format (for spreadsheets): ```csv word,phonetic_american,part_of_speech,is_valid apple,/ˈæpl/,noun,True banana,/bəˈnænə/,noun,True ``` ### Markdown format: | Word | American IPA | Part of Speech | |------|--------------|----------------| | apple | /ˈæpl/ | noun | | banana | /bəˈnænə/ | noun | ## Requirements - Python 3.6+ - `requests` library (`pip install requests`) - Internet connection (for dictionary API queries) ## Installation ```bash pip install requests ``` No other installation required - just clone and use. ## Author Created for bulk English vocabulary phonetic transcription with American English IPA.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aigcpro/feishu-agents-bind (version 1.0.0)

--- name: feishu-agent-bind description: 绑定飞书机器人到Agent。用户发送App ID和App Secret即可自动配置飞书账号并绑定到指定Agent。用于：(1) 用户提供App ID和App Secret (2) 创建或选择要绑定的Agent (3) 自动配置openclaw.json并重启Gateway。 --- # 飞书 Agent 绑定 用户发送 App ID 和 App Secret 时，使用此 skill 自动完成飞书机器人绑定。 ## 触发条件 - 用户发送 `cli_` 开头的 App ID - 用户发送飞书机器人凭证 ## 绑定流程 ### 1. 收集信息 需要用户提供： - **App ID**: 飞书应用 ID (cli_xxx) - **App Secret**: 飞书应用密钥 - **Agent名称**: 要绑定的 Agent ID (如 `news`, `assistant` 等) 如果用户只提供 App ID 和 App Secret，询问要绑定到哪个 Agent，或自动创建一个新 Agent。 ### 2. 创建 Agent (如需要) ```bash # 创建新 Agent openclaw agents add <agentName> # 示例：创建 news agent openclaw agents add news ``` ### 3. 执行绑定 ```bash # 查看现有 Agent 列表 openclaw agents list # 添加飞书账号到配置 openclaw config set "channels.feishu.accounts.<agentName>.appId" "<App ID>" openclaw config set "channels.feishu.accounts.<agentName>.appSecret" "<App Secret>" # 绑定 Agent 到飞书账号 openclaw agents bind --agent <agentName> --bind feishu:<agentName> ``` ### 3. 重启 Gateway ```bash openclaw gateway restart ``` ### 4. 验证 ```bash openclaw channels status ``` ## 注意事项 - 如果是首次配置，需要确保飞书应用已在开放平台发布并具备必要权限： - `im:message:send_as_bot` - `im:message:receive` - `contact:contact.base:readonly` - 配置完成后告知用户如何获取 user open_id 以配置白名单


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kasture-rohit/vcf-lcm-precheck-analyzer (version 1.0.1)

--- name: vcf-lcm-precheck-analyzer description: An MCP server that interfaces with VCF SDDC Manager to retrieve and analyze LCM upgrade pre-check results, providing instant remediation steps for failures. version: 1.0.1 homepage: https://github.com/kasture-rohit/vcf-openclaw-skills metadata: openclaw: requires: env: - SDDCMANAGER_HOST - SDDCMANAGER_API_TOKEN emoji: "" --- # VCF LCM Pre-check Analyzer (MCP) This skill utilizes the Model Context Protocol (MCP) to provide the AI agent with a native `analyze_lcm_precheck` tool, connecting directly to the VMware Cloud Foundation SDDC Manager API. ### Setup Instructions for OpenClaw To use this skill, the OpenClaw agent must run the attached Python MCP server locally. 1. Install dependencies: `pip install -r requirements.txt` 2. Configure your OpenClaw settings to launch this MCP server by adding the following to your agent's MCP configuration block: ```json { "mcpServers": { "vcf-lcm-analyzer": { "command": "python", "args": ["server.py"], "env": { "SDDCMANAGER_HOST": "your-sddc-manager-fqdn", "SDDCMANAGER_API_TOKEN": "your-api-token" } } } }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mike47512/netease-cloud-music (version 0.1.0)

--- name: netease-cloud-music-hot-trend description: 注册“网易云音乐”热门技能；当需要访问或自动化云音乐相关内容时调用。 homepage: `https://music.163.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 网易云音乐 用途与边界 - 面向公开歌单、歌曲与歌手页的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://music.163.com/ - 排行榜：https://music.163.com/#/discover/toplist - 搜索入口：https://music.163.com/#/search 常见任务 - 指定歌单的曲目摘要（播放、收藏、评论） - 歌手主页作品的时序表现对比（近7/30天） - 排行榜分布统计（风格、语种、时长） 数据字段 - 歌曲ID、名称、歌手、专辑、时长、播放量/评论数、链接 - 歌单名称、创建者、曲目数、播放/收藏/评论量、链接 - 歌手主页链接、作品交互指标摘要 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 登录态仅用于个人页信息整理，不自动执行交互 示例流程 - 歌单摘要：访问歌单页 → 抽取曲目 → 统计播放/收藏/评论 - 歌手对比：访问主页 → 抽取近期作品 → 输出表现摘要 - 排行榜扫描：访问榜单 → 抽取条目 → 风格/语种统计 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/devops-journey (version 2.0.4)

--- name: "devops-journey" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Devops Journey concepts, best practices, and implementation patte..." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [devops,journey, reference] category: "devtools" --- # Devops Journey Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Devops Journey concepts, best practices, and implementation patte... No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet 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: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linkfox-ai/linkfox-amazon-store-uploads (version 0.0.1)

--- name: linkfox-amazon-store-uploads version: 0.0.1 category: product-sourcing description: 亚马逊店铺文件上传（与 linkfox-amazon-store-auth 等同系列），经 /spApi/developerProxy 调用 Uploads API v2020-11-01 的 createUploadDestinationForResource，再向返回 URL 上传文件，供 A+ Content、Messaging 等 API 使用。当用户提到上传文件、createUploadDestinationForResource、upload destination、contentMD5、预签名上传、SP-API 上传、A+ 图片上传、Messaging 附件上传 时触发。 --- # Amazon 店铺 Uploads（文件上传） 本 skill 专用于 **向 Amazon 申请上传目的地并上传文件**，与 **`linkfox-amazon-store-auth`** 同系列：先 **`storeTokens`**，再 **`developerProxy`** 调用 **createUploadDestinationForResource**，最后用 **`upload_to_destination.py`** 对返回的 URL 执行 **PUT**（不经网关）。 > 这是 **Uploads API**，不是 Orders 订单接口。订单见 **`linkfox-amazon-store-orders`**；批量 Feed 文件见 **`linkfox-amazon-store-feeds`**。 ## 官方参考 [createUploadDestinationForResource](https://developer-docs.amazon.com/sp-api/reference/createuploaddestinationforresource) · [Create an upload destination](https://developer-docs.amazon.com/sp-api/docs/create-an-upload-destination) --- ## Prerequisites 1. 依赖 **`linkfox-amazon-store-auth`**。 2. **`resource`** 须与下游 API 文档一致（例如 A+：[REDACTED_SECRET]；Messaging 为对应 messages 资源路径）。 3. **`contentMD5`** 为待上传文件内容的 **Base64 MD5 摘要**；传 **`filePath`** / **`content`** 时脚本可自动计算。 --- ## 工作流 ```text create_upload_destination_for_resource → uploadDestination { uploadDestinationId, url, headers } upload_to_destination (PUT url + headers) → 在 A+/Messaging 等 API 中引用 uploadDestinationId ``` --- ## Scripts | 脚本 | 说明 | |------|------| | `create_upload_destination_for_resource.py` | POST `uploads/2020-11-01/uploadDestinations/{resource}` | | `upload_to_destination.py` | PUT 到返回的 `url`（带 `headers`） | | `_spapi_uploads_common.py` | 内部公共模块 | --- ## 示例 ```bash export LINKFOXAGENT_API_KEY="<your-key>" # 1) 创建上传目的地（自动根据 filePath 计算 contentMD5） python scripts/create_upload_destination_for_resource.py '{ "sellerId":"A1...", "region":"NA", "resource":[REDACTED_SECRET], "marketplaceId":"ATVPDKIKX0DER", "filePath":"/path/to/banner.jpg", "contentType":"image/jpeg" }' # 2) 上传文件（将上一步 stdout 中的 uploadDestination 传入） python scripts/upload_to_destination.py '{ "uploadDestination": { "url": "...", "headers": { } }, "filePath": "/path/to/banner.jpg" }' ``` --- ## Display Rules 1. 成功创建目的地常为 **HTTP 201**；先看 **`developerProxy`**，再看 **`uploadDestination`**。 2. **`resource`** 不要带前导 `/`；path 中会对 `/` 做编码。 3. 网关需放行 **`uploads/2020-11-01/`** 前缀。 **Feedback：** `skillName`：`linkfox-amazon-store-uploads`。 --- *更多跨境 skill：[LinkFox Skills](https://skill.linkfox.com/)*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: baiduqianfangroup/ai-notes-of-video (version 1.0.0)

--- name: ai-notes-of-video description: The video AI notes tool is provided by Baidu. Based on the video download address provided by the user, it downloads and parses the video, and finally generates AI notes corresponding to the video (a total of three types of notes can be generated: document notes, outline notes, and image-text notes). metadata: { "openclaw": { "emoji": "📺", "requires": { "bins": ["python"], "env":["BAIDU_API_KEY"]},"primaryEnv":"BAIDU_API_KEY" } } --- # AI PPT Generation This skill allows OpenClaw agents to generate AI notes, Based solely on the video address provided by the user. ## Setup 1. **API Key:** Ensure the BAIDU_API_KEY environment variable is set with your valid API key. 2. **Environment:** The API key should be available in the runtime environment. ## API table | name | path | description | |------------|---------------------------------|---------------------------------------| |AINotesTaskCreate|/v2/tools/ai_note/task_create|Create AI notes task based on the video address provided by the user| |AINotesTaskQuery| /v2/tools/ai_note/query |Query AI notes task result based on task id| ## Workflow 1. The AINotesTaskCreate API executes the Python script located at `scripts/ai_notes_task_create.py` 2. The AINotesTaskQuery API executes the Python script located at `scripts/ai_notes_task_query.py` 3. The first step ,call the AINotesTaskCreate API to create a task and get the task ID, must give a video address. 4. The second step ,call the AINotesTaskQuery API to query the task result based on the task ID. 5. Repeat the second step until the task status is completed.The task success identifier is status=10002. status=10000 indicates that the task is in progress. All other status codes are failures 6. Each item in the note list is a note content. For each item in the list: the tpl_no field represents the type of stored notes, 1 - manuscript notes, 2 - outline notes, 3 - graphic and text notes. The "detail" field is for note details. In "detail", "status" represents the note status, with 10002 indicating success,with status=10000 indicating progressing and other status codes indicating failure. "content" indicates the note result. The mind map is located at the top of the outline note and is marked by the "Mind" tag ## APIS ### AINotesTaskCreate API #### Parameters - `video_url`: the url of the video (required) #### Example Usage ```bash BAIDU_API_KEY=xxx python3 scripts/ai_notes_task_create.py 'https://xxxxx.bj.bcebos.com/1%E5%88%86%E9%92%9F_%E6%9C%89%E5%AD%97%E5%B9%95.mp4' ``` ### PPTOutlineGenerate API #### Parameters - `task_id`: task id from AINotesTaskCreate API return（required） #### Example Usage ```bash BAIDU_API_KEY=xxx python3 scripts/ai_notes_task_query.py "26943ed4-f5a9-4306-a05b-b087665433a0" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dxyu94/query-main-board-stocks (version 1.0.0)

--- name: query-main-board-stocks description: 查询沪深主板 A 股股票列表（排除创业板、科创板） version: 1.0.0 metadata: {"clawdbot":{"emoji":"📈","requires":{"bins":["python3"]}}} --- # query-main-board-stocks 查询沪深主板 A 股股票列表（排除创业板、科创板）的 Baostock 工具。 ## 功能 - 使用 Baostock API 获取全部 A 股股票列表 - 过滤出主板股票（沪市主板 600/601/603/605，深市主板 000/001/002/003） - 排除创业板（300/301）和科创板（688） - 输出 JSON 和 CSV 格式结果 ## 使用方法 ```bash python query_main_board_stocks.py ``` ## 输出文件 - `main_board_stocks.json` - 包含股票列表的 JSON 文件 - `main_board_stocks.csv` - CSV 格式股票列表（代码、名称） ## 依赖 ```bash pip install baostock pandas ``` ## 数据源 Baostock - 免费 A 股数据平台（无需注册） ## 示例输出 ``` ===================================================================== 沪深主板股票查询（非创业板、非科创板） ===================================================================== ✅ 获取到 4800 只股票 ✅ 主板股票：3200 只 📊 统计结果 主板股票：3200 只 沪市主板：1800 只 深市主板：1400 只 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: little-ke/wechat-publisher-setup (version 1.0.0)

--- name: wechat-publisher-setup description: "部署微信公众号发布Agent团队（画境视觉设计+数澜运营发布），含封面设计、排版美化、API发布、数据分析。使用 /wechat-publisher-setup 触发部署，需先安装 content-creation。" license: MIT version: 1.0.0 metadata: {"openclaw":{"requires":{"bins":["node"]}}} --- # 微信公众号发布团队 - 自动部署 当用户调用 /wechat-publisher-setup 时，执行以下步骤部署 2 人微信发布 Agent 团队。 ## 概述 部署微信公众号专属的发布团队： - 画境（视觉设计师）、数澜（运营分析师） ## Step 1：环境检查 1. 确认 OpenClaw 已安装 2. 确认 content-creation 团队已部署（检查 mobai/tanfeng/jinshu 是否已注册） - 未部署 → 提示"请先运行 /content-creation 部署内容创作团队"→ **终止** 3. 检查是否已存在 huajing/shulan Agent - 已存在 → 提示是否覆盖 ## Step 2：配置微信公众平台 API 向用户说明：API 对接用于自动化发布和数据拉取。 依次询问： 1. 账号类型：订阅号 / 服务号 2. AppID（公众平台后台 → 开发 → 基本配置） 3. AppSecret 可选： 4. 是否已将服务器 IP 加入白名单？ 将凭证写入安全配置文件： ```bash mkdir -p ~/.openclaw/workspace-wechat-publisher cat > ~/.openclaw/workspace-wechat-publisher/.env << 'EOF' WECHAT_APP_ID=用户输入的AppID WECHAT_APP_SECRET=用户输入的AppSecret WECHAT_API_BASE=https://api.weixin.qq.com WECHAT_ACCOUNT_TYPE=用户选择的账号类型 EOF chmod 600 ~/.openclaw/workspace-wechat-publisher/.env 2>/dev/null || true ``` 验证连通性： ```bash node {baseDir}/scripts/wechat_publish.cjs token ``` ## Step 3：部署文件 1. 从 `{baseDir}/templates/` 复制文件到： ``` ~/.openclaw/workspace-wechat-publisher/huajing/ ~/.openclaw/workspace-wechat-publisher/shulan/ ``` 2. 复制 `{baseDir}/scripts/wechat_publish.cjs` 到 `~/.openclaw/workspace-wechat-publisher/scripts/` 3. 输出进度： ``` [1/2] huajing（画境 - 视觉设计师）→ 已部署 [2/2] shulan（数澜 - 运营分析师）→ 已部署 ``` ## Step 4：注册 Agent ```bash openclaw agents add huajing \ --name "画境" \ --workspace "~/.openclaw/workspace-wechat-publisher/huajing" \ --description "微信视觉设计师 - 封面设计与排版美化" openclaw agents add shulan \ --name "数澜" \ --workspace "~/.openclaw/workspace-wechat-publisher/shulan" \ --description "微信运营分析师 - API发布与数据分析" ``` ## Step 5：验证 1. 确认 2 个 agent 注册成功 2. 输出部署报告： ``` ✅ 微信发布团队部署完成 ├── 🎨 画境（视觉设计师） → 已就绪 └── 📊 数澜（运营分析师） → 已就绪 ``` 3. 提示：使用 `/wechat-publish-workflow` 启动微信发布流水线 4. 发布脚本位置：`{baseDir}/scripts/wechat_publish.cjs` ## 错误处理 - content-creation 未部署 → 提示先安装 - 微信 API token 获取失败 → 检查 AppID/AppSecret、IP 白名单 - Agent 注册失败 → 检查重名


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cjmore66/tia-cicd-diff (version 1.0.0)

--- name: TIA_CICD_DIFF description: Use Siemens TIA Portal Openness API to diff two .zap18 projects and return structured code & hardware changes. tools: - shell - filesystem --- You can: - Call external scripts that use SiemensEngineering.dll (Openness) to extract XML/SCL from .zap18 archives. - Read and write files in a working directory. - Return a machine-readable JSON summary of changes and a human-friendly Markdown explanation. When the agent asks you to diff two .zap18 files: 1) Locate the baseline and new .zap18 paths. 2) Run the external Openness diff script (e.g. `tia_diff.bat baseline.zap18 new.zap18`). 3) Parse the resulting JSON (FB/DB/hardware/networks changes). 4) Feed that structured data back to the agent for explanation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xuelin314-bot/k3s-deploy (version 1.0.0)

--- name: k3s-deploy description: 自动化部署 K3s Kubernetes 集群到多台 Linux 服务器。支持自动镜像拉取、网络配置检测、CNI 插件安装。使用场景：(1) 从零部署 K3s 集群，(2) 修复 NotReady 节点，(3) 批量部署多节点集群。触发条件：用户提到 K3s、Kubernetes 部署、集群安装、节点加入等。 --- # K3s 自动化部署技能 ## 快速开始 ```bash # 使用部署脚本（推荐） ./scripts/deploy-k3s.sh \ --master 10.1.9.177 \ --masters-user root \ --masters-pass 'your-password' \ --worker 10.1.9.178,10.1.9.179 \ --workers-user root \ --workers-pass 'your-password' ``` ## 工作流程 ### 1. 部署前检查 - 检查服务器连通性 - 验证 SSH 凭据 - 检测操作系统版本（支持 CentOS 7+/Ubuntu 18.04+） ### 2. 安装 K3s - Master 节点：安装 K3s server - Worker 节点：加入集群 ### 3. 部署网络插件 - 自动检测网络接口（ens192/eth0 等） - 拉取国内镜像源 flannel - 创建 CNI 配置文件 ### 4. 健康检查 - 验证所有节点 Ready - 验证 CoreDNS 运行 - 测试 Pod 调度 ## 可用脚本 | 脚本 | 用途 | |------|------| | `scripts/deploy-k3s.sh` | 一键部署完整集群 | | `scripts/pull-images.sh` | 预拉取必要镜像 | | `scripts/check-cluster.sh` | 集群健康检查 | ## 故障排查 常见问题见 `references/troubleshooting.md` ### 快速诊断 ```bash # 检查节点状态 kubectl get nodes # 检查系统 Pod kubectl get pods -n kube-system # 查看 Pod 详情 kubectl describe pod <pod-name> -n kube-system # 查看 kubelet 日志 journalctl -u kubelet -n 50 ``` ## 配置说明 ### 网络接口自动检测 脚本会自动执行以下命令检测默认路由接口： ```bash ip route | grep default | awk '{print $5}' ``` ### 镜像源 默认使用华为云镜像： - Flannel: `swr.cn-north-4.myhuaweicloud.com/ddn-k8s/quay.io/coreos/flannel:v0.15.1` - Pause: `registry.aliyuncs.com/google_containers/pause:3.9` ### CNI 配置 Flannel v0.15.1 需要手动创建 CNI 配置文件，脚本会自动完成： ```bash # 在所有节点创建 /etc/cni/net.d/10-flannel.conflist ``` ## 最佳实践 1. **提前拉取镜像** - 使用 `pull-images.sh` 在所有节点预拉取 2. **统一时间** - 确保所有节点 NTP 同步 3. **防火墙** - 关闭防火墙或开放必要端口 4. **Swap** - K3s 要求关闭 Swap ## 输出 部署完成后生成： - `cluster-info.md` - 集群配置摘要 - `deployment-log.txt` - 详细部署日志


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