OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lujun2508/weather-cn-jj (version 1.3.0)

--- name: weather-cn description: "国内天气+空气质量查询。支持城市和区县级，中文输出。数据源: wttr.in + Open-Meteo。无需 API Key。" user-invocable: true argument-hint: "<城市名> [--aqi] [--tomorrow]" metadata: openclaw: requires: bins: ["python"] os: ["win32", "darwin", "linux"] --- # Weather CN — 国内天气 + 空气质量 纯中文输出，国内用户友好。天气数据来源 wttr.in，空气质量来源 Open-Meteo（US AQI 标准）。 ## 支持地区 ### 城市级别 `北京`、`上海`、`杭州`、`深圳`、`广州`、`成都`、`武汉`、`西安`、`南京`、`重庆`、`天津`、`苏州`、`郑州`、`长沙`、`沈阳`、`青岛`、`大连`、`哈尔滨`、`长春`、`石家庄`、`福州`、`厦门`、`南昌`、`济南`、`太原`、`合肥`、`昆明`、`贵阳`、`南宁`、`海口`、`拉萨`、`兰州`、`银川`、`西宁`、`乌鲁木齐`、`呼和浩特`、`雄安新区`、`东阳`、`义乌`、`永康`、`金华` ### 区县级别（定位支持，天气以城市数据为准） ``` 北京: 朝阳、海淀、东城、西城、丰台、石景山、通州、顺义、大兴、昌平、房山... 上海: 浦东、黄浦、徐汇、静安、普陀、虹口、杨浦、闵行、宝山、嘉定、松江... 杭州: 西湖、上城、拱墅、滨江、萧山、余杭、临平、钱塘、富阳、临安、桐庐... 广州: 天河、越秀、海珠、番禺、白云、黄埔、花都、南沙、从化、增城 深圳: 福田、罗湖、南山、宝安、龙岗、龙华、坪山、光明、盐田、大鹏 成都: 锦江、青羊、金牛、武侯、成华、龙泉驿、青白江、新都、温江、双流... 武汉: 江岸、江汉、硚口、汉阳、武昌、青山、洪山、东西湖、汉南、蔡甸... 南京: 玄武、秦淮、建邺、鼓楼、浦口、栖霞、雨花台、江宁、六合、溧水、高淳 西安: 新城、碑林、莲湖、雁塔、灞桥、未央、阎良、临潼、长安、高陵、鄠邑... 苏州: 姑苏、虎丘、吴中、相城、吴江、苏州工业园、昆山、常熟、张家港、太仓 以及全国所有地级市的市辖区 ``` ### 支持的输入格式 - `北京` （城市） - `北京朝阳` / `北京 朝阳` / `北京:朝阳` （区级） - `上海浦东` / `杭州西湖` / `广州天河` / `深圳南山` （区级） - `东阳` （城市，浙江金华下辖） **注意：** 部分区县在 wttr.in 无数据（如东阳白云街道），使用城市级别即可。 ## 快速查询 ### 城市天气 ```bash python3 scripts/weather_cn.py 北京 ``` ### 精确到区+AQI ```bash python3 scripts/weather_cn.py 北京朝阳 --aqi python3 scripts/weather_cn.py 上海浦东 --aqi python3 scripts/weather_cn.py 杭州西湖 --aqi ``` ### 明日预报 ```bash python3 scripts/weather_cn.py 广州天河 --tomorrow --aqi ``` ## Python调用 ```python import sys sys.path.insert(0, 'skills/weather-cn/scripts') from weather_cn import fetch_weather, fetch_aqi_by_coords, format_weather # 仅天气 weather = fetch_weather('上海') print(format_weather('上海', weather)) # 天气+AQI weather = fetch_weather('上海') aqi = fetch_aqi_by_coords(31.2304, 121.4737) print(format_weather('上海', weather, aqi)) # 明日预报 weather = fetch_weather('上海', tomorrow=True) print(format_weather('上海', weather, tomorrow=True)) ``` ## AQI等级参考（US AQI标准） | AQI | 等级 | 建议 | |-----|------|------| | 0-50 | 优 | 空气质量令人满意 | | 51-100 | 良 | 空气质量可接受 | | 101-150 | 轻度污染 | 敏感人群减少户外 | | 151-200 | 中度污染 | 减少户外活动 | | 201-300 | 重度污染 | 避免外出 | | 300+ | 严重污染 | 停止户外活动 | **注：** AQI数据来源为 Open-Meteo（欧洲CAMS模型，US AQI标准），数据更新可能有数小时延迟。US AQI 100 对应中国 AQI 约150，同数值下 US AQI 标准更严格。 ## 天气代码映射 | Code | 中文 | |------|------| | 113 | ☀️ 晴 | | 116 | ⛅ 多云 | | 119 | ☁️ 阴 | | 122 | 🌫️ 雾 | | 143 | 🌫️ 薄雾 | | 176 | 🌦️ 晴转阵雨 | | 179 | 🌨️ 阵雪 | | 182 | 🌨️ 小雪 | | 185 | 🌨️ 冻雨 | | 200 | ⛈️ 雷暴 | | 227 | 🌨️ 风雪 | | 230 | ❄️ 大雪 | | 248 | 🌫️ 大雾 | | 260 | 🌫️ 浓雾 | | 263 | 🌧️ 小雨 | | 266 | 🌧️ 小雨 | | 269 | 🌨️ 雨夹雪 | | 281 | 🌨️ 冻雨 | | 293 | 🌧️ 小雨 | | 296 | 🌧️ 中雨 | | 299 | 🌧️ 大雨 | | 302 | 🌨️ 雷阵雨 | | 308 | 🌧️ 暴雨 | | 353 | 🌧️ 中雨 | | 356 | 🌧️ 大雨 | | 359 | 🌧️ 雷阵雨 | | 362 | 🌨️ 小阵雨 | | 365 | 🌨️ 中阵雨 | | 368 | 🌨️ 大阵雨 | | 371 | 🌨️ 冰粒 | | 386 | ⛈️ 雷阵雨 | | 389 | ⛈️ 雷暴雨 | ## 穿衣指数参考 根据温度给出建议： - <5°C：羽绒服 - 5-10°C：厚外套+毛衣 - 10-15°C：外套+打底 - 15-20°C：轻薄外套 - 20-25°C：长袖/短袖 - 25-30°C：短袖 - >30°C：超薄短袖

path: _meta.json
content: ownerId: local
slug: weather-cn
version: 1.3.0
publishedAt: 1744896000000

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: frankxia2013/imessage-notify (version 1.0.0)

--- name: imessage-notify description: Send iMessage notifications to iPhone via Mac's Messages app. Supports text, images, videos, audio, files, and URLs. Use when the user wants to send notifications, alerts, or messages to their iPhone from Clawdbot tasks. Triggers on phrases like "send notification to phone", "notify me via iMessage", "message my iPhone", or any request to send iMessage alerts from automated tasks. --- # iMessage Notification Skill Send iMessage notifications to your iPhone using Mac's Messages app via AppleScript. **Features**: Text, Images, Videos, Audio, Files, URLs (with preview) ## Prerequisites - Mac mini (or Mac) with Messages app open and signed in - iPhone signed into the same Apple ID (iMessage) - Apple ID: [REDACTED_SECRET] ## Quick Usage ### Simple Text Message ```bash ./scripts/send-imessage.sh "Your notification message" # Or shortcut: ~/Document/code/clawd/notify "Hello!" ``` ### Multimedia Messages ```bash # Text only ./scripts/send-imessage-media.sh -t "Task completed!" # Send image ./scripts/send-imessage-media.sh -i ~/Desktop/screenshot.png # Send video ./scripts/send-imessage-media.sh -v ~/Movies/clip.mp4 # Send audio/voice ./scripts/send-imessage-media.sh -a ~/Desktop/recording.m4a # Send any file ./scripts/send-imessage-media.sh -f ~/Documents/report.pdf # Send URL (displays preview) ./scripts/send-imessage-media.sh -u "https://www.apple.com" # Combined: Text + Image ./scripts/send-imessage-media.sh -t "Check this:" -i ~/Desktop/chart.png # Combined: Text + Link ./scripts/send-imessage-media.sh -t "Found this:" -u "https://example.com" ``` ### Shortcuts ```bash # Quick text cd ~/Document/code/clawd && ./notify "Hello" # Quick multimedia cd ~/Document/code/clawd && ./notify-media -t "Done!" -i result.png ``` ## From Clawdbot Tasks ### Simple notification ```bash # At the end of a task: ~/Document/code/clawd/notify "🎉 Build finished!" ``` ### With screenshot ```bash # Take screenshot and send screencapture -i ~/Desktop/result.png ~/Document/code/clawd/notify-media -t "Error occurred:" -i ~/Desktop/result.png ``` ### Task completion with file ```bash # Generate report and notify ./generate-report.sh ~/Document/code/clawd/notify-media -t "Report ready:" -f ~/output/report.pdf ``` ## Options Reference | Option | Description | Example | |--------|-------------|---------| | `-t, --text` | Text message | `-t "Hello"` | | `-i, --image` | Image file | `-i photo.jpg` | | `-v, --video` | Video file | `-v clip.mp4` | | `-a, --audio` | Audio file | `-a voice.m4a` | | `-f, --file` | Any file | `-f doc.pdf` | | `-u, --url` | URL with preview | `-u "https://..."` | | `-r, --recipient` | Override recipient | `-r "[REDACTED_SECRET]"` | | `-h, --help` | Show help | `-h` | ## Script Reference - [scripts/send-imessage.sh](scripts/send-imessage.sh) - Simple text messages - [scripts/send-imessage-media.sh](scripts/send-imessage-media.sh) - Full multimedia support


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector: suspicious (CRITICAL)
Skill: juan-xin-cai/openclaw-context-upgrade (version 1.0.0)

--- name: openclaw-context-upgrade description: Diagnose and upgrade OpenClaw conversation context limits, especially moving a chat from ~272k to 1M. Use when the user asks to enlarge context, make GPT-5.4 use 1M context, change `agents.defaults.contextTokens`, verify why a chat still shows 272k after changes, or execute the full workflow of model selection + config patch + restart + fresh session + status verification. --- # OpenClaw Context Upgrade ## Check current state first 1. Run `session_status` and record: - current model - current `Context` - whether the session already shows `1.0m` 2. If the user is only asking "why isn't it 1M yet", diagnose first. Do not change config blindly. ## Inspect the exact config path before editing 1. Inspect `agents.defaults.contextTokens` with `gateway` `config.schema.lookup`. 2. If model selection matters, also inspect `agents.defaults.model`. 3. Use `gateway` `config.patch` for changes. 4. Do not tell the user to run `openclaw` CLI for this workflow. ## Upgrade workflow 1. Make sure the target session is using a high-context model. - Prefer model alias `GPT-5.4` for this workflow. - During verification, avoid relying on fallback behavior. - If needed, use `session_status` with a session model override to pin the current session to `GPT-5.4`. 2. Raise the default context budget. - Set `agents.defaults.contextTokens = 1000000`. - If the user's goal is future sessions, patch defaults. - If the user only wants diagnosis, explain the change without applying it. 3. Let the restart happen. - `gateway config.patch` restarts OpenClaw automatically. - Always include a clear `note` so the user gets a useful completion message after restart. 4. Force a fresh chat session. - Tell the user to use `/new` or `/reset`. - Old chats often keep the earlier session budget and mislead verification. 5. Verify after the fresh session starts. - Run `session_status` again. - Success looks like `Context: .../1.0m` or similar. ## Diagnose common failure modes ### Still shows `.../272k` Check these in order: 1. The chat was not restarted with `/new` or `/reset`. 2. The session is still on the wrong model or a fallback model. 3. The provider/model pair is returning a lower real cap. 4. The config patch did not target `agents.defaults.contextTokens` correctly. ## Tell the truth about limits - Do not promise 1M if the provider is actually returning a smaller cap. - OpenClaw can request a larger budget, but it cannot exceed the model/provider limit in reality. - If verification still shows `272k`, say that plainly and explain whether the blocker is session reuse, model mismatch, or provider cap. ## Default reply pattern Use this sequence when answering: 1. State the current observed limit. 2. Give the exact fix order: - pin model to `GPT-5.4` - set `agents.defaults.contextTokens = 1000000` - restart - `/new` or `/reset` - verify with `session_status` 3. End with the caveat that provider limits still win.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: eamonnn101/lottery-divination (version 1.0.1)

--- name: lottery-divination description: 梅花易数彩票测算器。根据用户提问时的当前时间，起出梅花易数的本卦、互卦、变卦，并将其转化为双色球(ssq)或大乐透(dlt)的推荐号码。当用户提到「算一注彩票」「帮我选号」「梅花易数选号」「双色球/大乐透推荐号码」「今天买什么号」「算卦选号」「卜一注」「来一注」或任何涉及用易学/玄学方法生成彩票号码的请求时，触发此技能。即使用户只是随口说「帮我算个号」，也应触发。 --- # 梅花易数彩票测算器 ## 概述 此技能基于梅花易数·时间起卦法，将当前时刻的「天机」转化为彩票推荐号码。核心流程： 1. 获取当前时间（精确到小时） 2. 运行 `scripts/divination.py` 起卦并生成号码 3. 将 JSON 结果格式化为带有玄学意境的回复 ## 使用步骤 ### Step 1: 确定彩票类型 如果用户没有明确指定，先询问： - **双色球 (ssq)**: 红球 6 个 (1-33) + 蓝球 1 个 (1-16) - **大乐透 (dlt)**: 前区 5 个 (1-35) + 后区 2 个 (1-12) ### Step 2: 获取当前时间并运行脚本 ```bash # 获取当前时间 CURRENT_TIME=$(date +"%Y-%m-%d-%H") # 运行起卦脚本, <type> 替换为 ssq 或 dlt python3 <skill_path>/scripts/divination.py <type> $CURRENT_TIME ``` 脚本输出为 JSON，包含 `divination`（卦象信息）和 `lottery`（号码信息）两部分。 ### Step 3: 格式化输出 将 JSON 结果组织为以下结构回复用户（使用中文，风格略带古意但不晦涩）： **卦象部分**（简洁展示）: - 起卦时间与依据（年数、月数、日数、时数） - 本卦: 名称 + 卦符 + 上下卦五行 - 互卦: 名称 + 卦符（一句话解释互卦代表事物发展中段） - 变卦: 名称 + 卦符 + 动爻位置（一句话解释变卦代表最终趋势） **号码部分**（醒目展示）: - 直接展示 `lottery.format` 字段的内容 - 用加粗或清晰格式让号码一目了然 **免责声明 + 金额提示**（每次必须附带）: > ⚠️ 免责声明：梅花易数选号纯属娱乐，彩票本质为随机概率事件。建议单次投入不超过 10～20 元，图个乐呵就好，切勿沉迷，量力而行。 ### 输出风格参考 ``` 🔮 梅花易数 · 时间起卦 起卦时刻：2026年03月22日 15:00（丙午年·申时） 取数依据：年数7 + 月数5 + 日数22 + 时数9 ━━ 卦象 ━━ 本卦：泽火革 ☱☲（上金下火，革故鼎新之象） 互卦：天风姤 ☰☴（事态发展中，有不期而遇之机） 变卦：泽山咸 ☱☶（动爻在初爻，终局感应相通，心意相合） ━━ 双色球推荐号码 ━━ 红球: 04 10 15 16 23 32 | 蓝球: 05 💰 小贴士：买个 10～20 块的，娱乐一下就好，中不中都开心！ ⚠️ 免责声明：梅花易数选号纯属娱乐，彩票本质为随机概率事件。切勿沉迷，量力而行。 ``` ## 注意事项 - 每个整点时辰内，同类型彩票的结果是确定的（因为种子由时间决定），这符合「同一天机不可重复泄露」的玄学逻辑 - 如果用户短时间内多次请求同一类型，号码会相同 — 这是设计如此，可告知用户「天机已定，此时辰内卦象不变」 - 卦象的简要解读可以自由发挥，但保持简洁，2-3 句话即可，不要长篇大论 - **始终附带免责声明**，这是硬性要求


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

--- name: "CAM — Computer-Aided Manufacturing Toolpath Reference" description: "Use when calculating CNC speeds and feeds, selecting cutting tools, referencing G-code commands, looking up material cutting data, or computing machining parameters." version: "2.0.0" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["cam", "cnc", "machining", "gcode", "manufacturing", "toolpath", "engineering"] --- # CAM — Computer-Aided Manufacturing Toolpath Reference CNC machining reference — speeds & feeds calculator, toolpath strategies, G-code command reference, material cutting data, and machining parameter computation. ## Commands ### speeds-feeds Calculate spindle speed (RPM) and feed rate. ```bash bash scripts/script.sh speeds-feeds 10 80 0.05 3 ``` ### toolpath Toolpath strategy reference (adaptive, pocket, contour, etc.). ```bash bash scripts/script.sh toolpath bash scripts/script.sh toolpath adaptive ``` ### gcode G-code and M-code command reference. ```bash bash scripts/script.sh gcode bash scripts/script.sh gcode G02 ``` ### materials Material cutting data — recommended SFM, chipload, depth of cut. ```bash bash scripts/script.sh materials bash scripts/script.sh materials aluminum ``` ### calculate General machining calculations (MRR, power, time). ```bash bash scripts/script.sh calculate mrr 10 2 0.5 800 bash scripts/script.sh calculate power 50 1500 ``` ### help Show all commands. ```bash bash scripts/script.sh help ``` ## Output - Calculated RPM, feed rates, and machining parameters - G-code reference with syntax and examples - Material-specific cutting recommendations ## Feedback https://bytesagain.com/feedback/ Powered by BytesAgain | bytesagain.com


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zyq328328/duoke-xue-tou-shi (version 1.0.2)

--- name: 多科学透视 description: | 多科学透视 — 用多个学科的视角透视任意想法，发现"未知的未知"。 将一段想法、对话或笔记，通过多个学科视角进行深度透视分析，拓展认知边界。 **当以下情况时使用此 Skill**： (1) 用户分享了一段对话、笔记或想法，希望多维度分析 (2) 用户说"多科学透视"、"多维分析"、"跨学科分析" (3) 用户想要头脑风暴，寻找创意的多种可能性 (4) 用户准备写深度文章，需要多角度切入点和随机性启发 (5) 用户想从跨学科视角重新审视一段内容 --- # 多科学透视 Skill ## 核心理念 就像光线通过棱镜会折射出七彩光谱，一个想法通过多科学透视会折射出多维认知视角： - 🌈 **随机性启发**：像棱镜一样，不可预测地折射出意想不到的视角 - 🔷 **发现未知的未知**：看到从未想过的连接和可能性 - 💎 **拓展认知边界**：突破单一学科的思维局限 - ✨ **开启头脑风暴**：为创意和写作提供多元化的起点 - 🌐 **融合中西方智慧**：结合现代学科与中国传统文化视角 --- ## 适用场景 - 有一段笔记/灵感/对话记录，想要打破思维定式 - 需要头脑风暴，寻找创意的多种可能性 - 想发现"未知的未知"，拓展认知边界 - 准备写深度文章，需要随机性启发和多角度切入点 - 进行教育内容创作，需要跨学科的惊喜连接 --- ## 分析维度 多科学透视从以下维度对内容进行折射分析： 1. **认知心理学** — 抽象思维、元认知、隐喻理解、认知发展规律 2. **历史哲学** — 时代精神、文明周期、重大历史转折、经典思想实验 3. **教育学** — 苏格拉丁式提问、深度学习、因材施教、学习动机 4. **系统思维** — 多维分析、视角转换、反馈循环、涌现效应 5. **语言学** — 语义网络、话语分析、隐喻结构、表达模式 6. **中国传统文化** — 古典智慧、儒家/道家/佛家思想、历史典故 --- ## 输出结构 每次分析输出以下五个模块： ### 💎 核心洞察（Key Insights） - 提取内容中的"金句"或"Aha 时刻" - 识别最有力、最值得深挖的核心观点 ### 🌈 认知光谱分析（Cognitive Spectrum） - 从 4-6 个维度展开深度分析 - 每个维度包含：维度名称 → 分析内容 → 理论依据 → 具体启示 ### 🔷 意外连接与故事（Unexpected Connections） - 发现与内容相关的历史事件、科学实验、哲学思想实验 - 连接经典典故（中国+西方） - 找出意想不到的关联，激发新思考 ### ✍️ 多维写作光谱（Writing Directions） - 提供 3-5 个差异化的写作切入点 - 每个方向包含：切入角度 → 建议标题方向 → 核心论点方向 ### 💡 深度探索问题（Deep Questions） - 3-5 个引发持续思考的问题 - 帮助用户进一步深挖内容的本质 --- ## 使用方式 用户可以直接粘贴内容进行分析，或提供文件路径让 AI 读取。 **分析格式**： ``` 📝 **待分析内容**： [用户粘贴的文字/对话/笔记] 🔮 **多科学透视分析** [完整分析结果] ``` --- ## 参考文档 各维度的详细分析框架和方法论： | 维度 | 文档 | |------|------| | 认知心理学 | [references/cognitive-psychology.md](references/cognitive-psychology.md) | | 历史哲学 | [references/history-philosophy.md](references/history-philosophy.md) | | 教育学 | [references/education.md](references/education.md) | | 系统思维 | [references/systems-thinking.md](references/systems-thinking.md) | | 中国传统文化 | [references/chinese-wisdom.md](references/chinese-wisdom.md) | | 意外连接案例库 | [references/connections.md](references/connections.md) | --- ## 设计原则 1. **跨学科**：不局限单一视角，主动连接不同学科 2. **有深度**：不浮于表面，挖掘理论依据 3. **有惊喜**：主动寻找意想不到的连接 4. **接地气**：每个分析都要有可操作的实践建议 5. **促行动**：最终目标是激发用户的下一步思考或行动 --- ## 来源说明 本 Skill 源自 @木妈（@AI家庭教育实践者）开源的 [思维棱镜](https://github.com/vickysy/thought-prism-skill) 项目，遵循原项目许可证（个人使用免费，商业使用请联系作者）。 在原项目基础上，我们做了一些调整： - 适配 OpenClaw Skill 格式 - 调整了部分分析维度和提问框架 - 扩充了意外连接案例库


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

--- name: xiaohongshu-publisher-setup description: "部署小红书发布Agent团队（灵格排版设计+星阑运营发布），含XHS格式排版、浏览器自动化发布、数据分析。使用 /xiaohongshu-publisher-setup 触发部署，需先安装 content-creation。" license: MIT version: 1.0.0 metadata: {"openclaw":{"requires":{"bins":["node","npm"]}}} --- # 小红书发布团队 - 自动部署 当用户调用 /xiaohongshu-publisher-setup 时，执行以下步骤部署 2 人小红书发布 Agent 团队。 ## 概述 部署小红书专属的发布团队： - 灵格（小红书排版设计师）、星阑（小红书运营发布师） ## 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 Playwright 是否已安装： ```bash node -e "require('playwright')" 2>/dev/null && echo "OK" || echo "MISSING" ``` - 未安装 → 提示： ``` ⚠️ 需要安装 Playwright 用于浏览器自动化发布 请在小红书发布工作目录运行： npm install playwright npx playwright install chromium 安装后重新运行 /xiaohongshu-publisher-setup ``` → **终止** 4. 检查是否已存在 lingge/xinglan Agent - 已存在 → 提示是否覆盖 ## Step 2：收集账号信息 向用户说明：以下信息将写入 Agent 的 USER.md，帮助生成更精准的小红书内容。 依次询问： 1. 账号昵称和定位（如"职场穿搭博主"） 2. 目标受众（如"18-30岁职场女性"） 3. 主要内容方向/领域（如"穿搭、职场、生活方式"） 4. 内容风格偏好（如"干货为主 / 故事化 / 轻松幽默"） 可选： 5. 发布频率（如"每周2-3篇"） 6. 账号目前粉丝规模（帮助星阑设定数据预期） 将账号信息写入配置： ```bash mkdir -p ~/.openclaw/workspace-xiaohongshu-publisher cat > ~/.openclaw/workspace-xiaohongshu-publisher/.env << 'EOF' XHS_ACCOUNT_NAME=用户输入的账号名 XHS_POSITIONING=用户输入的账号定位 XHS_TARGET_AUDIENCE=用户输入的目标受众 XHS_CONTENT_TOPICS=用户输入的内容方向 XHS_WRITING_STYLE=用户输入的风格偏好 XHS_POST_FREQUENCY=用户输入的发布频率 XHS_FOLLOWER_SCALE=用户输入的粉丝规模 EOF chmod 600 ~/.openclaw/workspace-xiaohongshu-publisher/.env 2>/dev/null || true ``` ## Step 3：部署文件 1. 从 `{baseDir}/templates/` 复制文件到： ``` ~/.openclaw/workspace-xiaohongshu-publisher/lingge/ ~/.openclaw/workspace-xiaohongshu-publisher/xinglan/ ``` 2. 复制 `{baseDir}/scripts/xhs_publish.cjs` 到 `~/.openclaw/workspace-xiaohongshu-publisher/scripts/` 3. 将 Step 2 收集的信息填充到两个 Agent 的 USER.md 中 4. 创建 session 目录： ```bash mkdir -p ~/.openclaw/workspace-xiaohongshu-publisher/.session ``` 5. 输出进度： ``` [1/2] lingge（灵格 - 小红书排版设计师）→ 已部署 [2/2] xinglan（星阑 - 小红书运营发布师）→ 已部署 ``` ## Step 4：注册 Agent ```bash openclaw agents add lingge \ --name "灵格" \ --workspace "~/.openclaw/workspace-xiaohongshu-publisher/lingge" \ --description "小红书排版设计师 - XHS格式排版与内容包生成" openclaw agents add xinglan \ --name "星阑" \ --workspace "~/.openclaw/workspace-xiaohongshu-publisher/xinglan" \ --description "小红书运营发布师 - 自动化发布与数据分析" ``` ## Step 5：初始化登录（强烈建议） 询问用户是否现在初始化小红书登录（后续发布必须登录）： - 用户同意 → 执行： ```bash node ~/.openclaw/workspace-xiaohongshu-publisher/scripts/xhs_publish.cjs login ``` 浏览器将打开小红书，用户手动扫码登录，session 自动保存。 - 用户跳过 → 提示发布前需运行 `check-login` 命令 ## Step 6：验证 1. 确认 2 个 agent 注册成功 2. 输出部署报告： ``` ✅ 小红书发布团队部署完成 ├── 🎭 灵格（排版设计师） → 已就绪 └── 📈 星阑（运营发布师） → 已就绪 ``` 3. 提示：使用 `/xiaohongshu-publish-workflow` 启动小红书发布流水线 4. 发布脚本位置：`~/.openclaw/workspace-xiaohongshu-publisher/scripts/xhs_publish.cjs` ## 错误处理 - content-creation 未部署 → 提示先安装 - Playwright 未安装 → 提示安装命令 - Agent 注册失败 → 检查重名 - 登录失败 → 提示检查网络，或重新运行 login 命令


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: alvinecarn/media-writing (version 0.1.0)

--- name: media-writing description: You are a professional media writing expert with extensive experience in creating engaging and impactful content across multiple formats. Creating attention-grabbing titles and content, excelling in trending topics, emotional storytelling, and practical value-driven pieces that align with new media trends. You are well-versed in pop culture, current events, and user psychology, enabling you to ... --- # Media Writing ## Overview This skill provides specialized capabilities for media writing. ## Instructions You are a professional media writing expert with extensive experience in creating engaging and impactful content across multiple formats. Creating attention-grabbing titles and content, excelling in trending topics, emotional storytelling, and practical value-driven pieces that align with new media trends. You are well-versed in pop culture, current events, and user psychology, enabling you to create content that resonates with target audiences. Your goal is to produce high-quality, engaging, and purpose-driven content tailored to the specified context and platform. ## Language: If not clarified, ensure to use the language of the user’s question for output. Except for untranslatable proper nouns and terminology. ## Response Formatting Instructions: use markdown throughout your writing content. ##Tables: You can create tables using markdown, under the circumstance that the data source of the table must be true and real. You should use tables when the response involves listing multiple items with attributes or characteristics that can be clearly organized in a tabular format. ## Graphs: You can create graphs often using mermaid, under the circumstance that the data source of the graph must be true and real.## Quotes:1. When you incorporate specific information, findings, or ideas from a source, add a citation mark immediately after the relevant sentence or phrase.2. The citation mark MUST be a clickable numbered footnote in the format `[Number](URL)`,for example [1](https://link-to-source-1.com). At the end, there should be a complete list of references, numbered sequentially from 1 to N, with each entry supporting navigation to view the full reference details. ## Usage Notes - This skill is based on the Media_Writing agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: kdylan1010-alt/debate-learning-workflow (version 1.0.1)

--- name: debate-learning-workflow description: Run evidence-backed multi-agent debates (A/B/Opponent3/Judge) with 20-40 rounds, loophole analysis, and universal actionable lesson extraction. --- # Debate Learning Workflow Use this skill to run structured, high-rigor debates that produce transferable learning. ## Core Rules - Minimum 20 rounds per topic. - Judge-gated continuation if critical loopholes remain. - Hard stop at 40 rounds. - Openings must include **Claim + Evidence + Source**. - Unsupported openings are invalid and must be revised. - Every round logs: - loophole found - why loophole exists - concrete fix for next round ## Roles - Debater A - Debater B - Opponent3 (alternative/challenger model) - Judge (evidence quality + unresolved uncertainty) ## Output Files - Topic file: `~/Desktop/debate/YYYY-MM-DD-topic.md` - Daily index: `~/Desktop/debate/YYYY-MM-DD-index.md` - Lessons append target: `~/Desktop/lessons.md` ## Universalization Quality Gate Every generalized lesson must include: 1) Trigger condition 2) Loophole/failure pattern 3) Root cause 4) Corrective action 5) Measurable metric/threshold 6) Boundary conditions 7) Transfer examples in at least 2 other fields If any field is missing, lesson is INVALID and must be rewritten.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codekungfu/car-wash (version 0.1.0)

--- name: "Car Wash" description: "提供周边洗车房查找、打蜡镀晶、内饰精洗及优惠套餐指引。" --- # 汽车美容 ## 筛选项 - 地理距离（步行/骑行/驾车范围） - 社交热度（人气榜/点评分/新开业） - 消费档次（实惠/中端/轻奢/体验型） ## 返回字段 - 实时排队情况/预约通道/电子票号 - 营业时间变动/停车信息/导航精准路径 - 社区真实图库/探店笔记/避坑清单汇总 ## 示例请求 - “如何快速使用 汽车美容 处理 [具体场景任务] ？” - “汽车美容 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合地理信息变动与点评社区新热度进行灵活调整 ## 注意事项 - 节假日建议提前查询排队状态；到店体验请尊重商家劳动与规则。


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