OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mesiyoq965-sudo/daily-income-logger (version 1.0.0)

# Daily Income Logger 自动记录每日各平台收入，生成收入报告和趋势分析。 ## 功能 - **多平台支持**: 支持手动录入各平台收入（自媒体、广告、电商等） - **每日汇总**: 自动生成每日收入汇总报告 - **趋势分析**: 按周/月统计收入趋势 - **数据导出**: 支持导出 CSV/JSON 格式报告 ## 使用方式 ``` 用户: 今天各平台收入是多少？ 助手: 使用 daily-income-logger 查询今日收入汇总 用户: 记录今日B站收入500元 助手: 使用 daily-income-logger 记录收入 用户: 这周收入趋势如何？ 助手: 使用 daily-income-logger 分析本周趋势 ``` ## 执行脚本 ### 1. 查询今日收入汇总 ```bash #!/bin/bash # daily-income-logger query today DATA_DIR="$HOME/.daily-income-logger" TODAY=$(date +%Y-%m-%d) mkdir -p "$DATA_DIR/data" if [ -f "$DATA_DIR/data/income.json" ]; then cat "$DATA_DIR/data/income.json" | jq -r --arg date "$TODAY" ' .records[] | select(.date == $date) | "平台: \(.platform) | 金额: ¥\(.amount) | 备注: \(.note)"' else echo "今日暂无收入记录" fi ``` ### 2. 记录收入 ```bash #!/bin/bash # daily-income-logger record <platform> <amount> [note] DATA_DIR="$HOME/.daily-income-logger" mkdir -p "$DATA_DIR/data" PLATFORM=$1 AMOUNT=$2 NOTE=${3:-""} TODAY=$(date +%Y-%m-%d) TIMESTAMP=$(date -Iseconds) INCOME_FILE="$DATA_DIR/data/income.json" # 初始化或读取现有数据 if [ ! -f "$INCOME_FILE" ]; then echo '{"records":[]}' > "$INCOME_FILE" fi # 添加新记录 jq --arg date "$TODAY" --arg ts "$TIMESTAMP" --arg platform "$PLATFORM" \ --arg amount "$AMOUNT" --arg note "$NOTE" \ '.records += [{"date":$date,"timestamp":$ts,"platform":$platform,"amount":($amount | tonumber),"note":$note}]' \ "$INCOME_FILE" > tmp.json && mv tmp.json "$INCOME_FILE" echo "已记录: $PLATFORM +¥$AMOUNT" ``` ### 3. 生成周报 ```bash #!/bin/bash # daily-income-logger weekly-report DATA_DIR="$HOME/.daily-income-logger" START_DATE=$(date -d "7 days ago" +%Y-%m-%d) END_DATE=$(date +%Y-%m-%d) echo "===== 周收入报告 =====" echo "时间: $START_DATE ~ $END_DATE" echo "" if [ -f "$DATA_DIR/data/income.json" ]; then cat "$DATA_DIR/data/income.json" | jq -r --arg start "$START_DATE" --arg end "$END_DATE" ' [.records[] | select(.date >= $start and .date <= $end)] | group_by(.platform) | .[] | "【\.[0].platform】总计: ¥\(map(.amount) | add)"' else echo "暂无数据" fi ``` ## 数据存储 收入数据存储在本地 JSON 文件中: - `~/.daily-income-logger/data/income.json` - 收入记录 - `~/.daily-income-logger/data/reports/` - 生成的报告 ## 配置 首次使用需要配置收入类别: ```json { "categories": [ "bilibili", "youtube", "xiaohongshu", "advertising", "affiliate", "freelance" ] } ``` ## 权限 需要读写本地文件系统权限，用于存储收入数据。 ## 版本 1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chinapjs/meeting-transcriber (version 1.0.0)

# Meeting Transcriber - 会议转录技能 基于 FunASR 的实时会议转录技能，支持中文语音识别、实时转录、文件保存和环境管理。 ## 🎯 功能特性 ### 🎙️ 实时语音转录 - 实时麦克风录音和语音识别 - 支持中文语音识别 - 智能语音活动检测（VAD） - 自动标点恢复 ### 📝 会议纪要生成 - 自动保存转录文本 - 按时间戳记录发言 - 生成结构化会议纪要 - 支持导出文本格式 ### 🔧 环境管理 - Conda 环境自动检测 - 依赖包完整性检查 - 一键环境配置 - 音频设备检测 ## 🚀 快速开始 ### 安装技能 技能已自动注册到 OpenClaw，无需额外安装。 ### 使用命令 在 OpenClaw 会话中，使用以下命令： #### 转录操作 - `开始会议转录` - 启动实时语音转录 - `停止转录` - 停止转录并保存文件 #### 环境管理 - `检查转录环境` - 检查 Conda 环境和依赖包 - `设置转录环境` - 配置 Conda 环境和安装依赖 #### 文件管理 - `查看会议记录` - 列出所有会议转录文件 - `打开最新会议纪要` - 打开最新的会议纪要文件 ## 📁 文件结构 ``` meeting-transcriber/ ├── _meta.json # 技能元数据 ├── transcriber.py # 技能主文件（支持所有命令） ├── start_meeting.bat # 批处理启动文件 ├── check_env.py # 环境检查工具 ├── conda_setup.py # 环境设置工具 └── SKILL.md # 技能说明文档 ``` ## 🔧 技术要求 ### 必需条件 - **操作系统**: Windows 10/11 - **Conda**: Miniconda 或 Anaconda - **Python**: 3.8+（通过 Conda `audioProject` 环境） - **Conda 环境**: `audioProject` ### 依赖包 - `funasr` - 语音识别引擎 - `torch` - 深度学习框架 - `pyaudio` - 音频输入输出 - `webrtcvad` - 语音活动检测 - `numpy` - 数值计算 ## 📊 使用示例 ### 示例 1: 完整会议转录流程 ``` 用户: 开始会议转录 AI: 启动转录系统... 用户: （开始会议讨论） 用户: 停止转录 AI: 转录已保存，文件位置: ... ``` ### 示例 2: 环境检查 ``` 用户: 检查转录环境 AI: 检查Python环境... OK AI: 检查项目目录... OK AI: 检查依赖包... OK AI: 环境检查通过！ ``` ### 示例 3: 查看会议记录 ``` 用户: 查看会议记录 AI: 找到 5 个转录文件: AI: 1. meeting_20260402_103149.txt AI: 2. meeting_20260402_100724.txt AI: ... ``` ## 🛠️ 故障排除 ### 常见问题 **Q: 环境检查失败** ``` A: 1. 运行'设置转录环境'命令 2. 或手动创建环境: conda create -n audioProject python=3.8 3. 手动安装依赖: pip install funasr torch pyaudio webrtcvad ``` **Q: 转录启动失败** ``` A: 1. 检查麦克风权限 2. 运行'检查转录环境'验证环境 3. 尝试使用批处理文件: start_meeting.bat ``` **Q: 编码错误（乱码）** ``` A: 1. 使用批处理文件: start_meeting.bat 2. 脚本已内置编码修复 ``` **Q: 麦克风无法工作** ``` A: 1. 检查麦克风是否被其他程序占用 2. 运行环境检查查看音频设备 3. 确保麦克风已正确连接 ``` ### 性能优化建议 1. **硬件优化** - 使用 USB 麦克风或耳机麦克风 - 确保足够的系统内存（建议 8GB+） - 使用 SSD 存储提高 IO 性能 2. **软件优化** - 关闭不必要的后台程序 - 调整音频缓冲区大小 - 使用安静的录音环境 3. **环境优化** - 在安静的房间中使用 - 避免风扇、空调等背景噪音 - 说话时靠近麦克风 ## 📈 高级功能 ### 自定义配置 技能支持以下自定义配置： - 转录语言设置（默认中文） - VAD 参数调整 - 保存路径配置 - 音频设备选择 ### 扩展功能 如需扩展功能，可以修改以下文件： - `D:/dev/python/voiceFunAsr/vocie_mic_fixed_gbk.py` - 核心转录逻辑 - `transcriber.py` - 技能命令处理逻辑 ## 📝 文件说明 ### 核心文件 | 文件 | 功能 | 使用场景 | |------|------|----------| | `transcriber.py` | 技能主文件 | 处理所有命令，OpenClaw自动调用 | | `start_meeting.bat` | 批处理启动文件 | 直接启动转录，解决编码问题 | | `check_env.py` | 环境检查工具 | 独立运行环境检查 | | `conda_setup.py` | 环境设置工具 | 自动安装依赖和环境 | ### 转录文件 - **位置**: `D:\dev\python\voiceFunAsr\meeting_records\` - **格式**: `meeting_YYYYMMDD_HHMMSS.txt` - **内容**: 按时间戳记录的转录文本 ## 🔄 更新日志 ### v1.0.0 (2026-04-02) - 初始版本发布 - 基于 FunASR 的实时语音转录 - 完整的命令支持 - 环境检查和管理 - 文件查看和打开功能 - OpenClaw 技能集成 ### 计划功能 - [ ] 多说话人识别 - [ ] 实时翻译功能 - [ ] 云端同步备份 - [ ] 移动端支持 ## 📞 技术支持 如有问题，请提供： 1. 操作系统和 Python 版本 2. 错误日志或截图 3. 麦克风设备信息 4. 会议环境描述 --- **提示**: 首次使用时建议运行环境检查： ``` 检查转录环境 ``` **转录文件位置**: `D:\dev\python\voiceFunAsr\meeting_records\`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: codenova58/cps-marketing (version 0.1.0)

--- name: "CPS赚钱" description: "系统化开展CPS联盟营销；当你计划做内容带货或导购时调用。" --- # CPS赚钱 目标与原则： - 目标：在90天内用一个主渠道跑通稳定的CPS收入闭环 - 原则：真实评价、不夸大承诺、遵守平台与广告法、可量化与可复制 平台与账号： - 联盟平台：阿里妈妈（淘宝客）、京东联盟、抖音精选联盟、拼多多多多进宝 - 分发平台：抖音/快手/小红书/视频号/B站/微信公众号/知乎/独立站/社群 - 账号矩阵：1个主号 + 2–3个辅助号，定位清晰，风格一致 选品方法： - 维度：高转化（痛点明确）+ 高佣金 + 高搜索/讨论热度 + 低售后风险 - 数据源：联盟后台榜单、站内热搜、社群反馈、竞品内容表现 - 测试：每次并行测试3–5款，观察点击率、转化率、EPC与退款率 内容矩阵： - 种草内容：场景化对比/避坑指南/清单推荐/真实测评 - 转化内容：开箱演示/使用教程/结果展示/用户口碑集合 - 长尾内容：关键词问答/细分场景/购买前后注意事项 获取链接与埋点： - 申请商品与佣金计划，生成专属CPS链接或口令 - 使用短链与UTM参数区分渠道与内容（source/campaign/content） - 保留跳转页与独立推荐页两套方案（对比转化与风控） 转化页面与文案： - 着陆结构：痛点引入 → 方案与亮点 → 真实证据 → 风险与边界 → 明确行动 - 视觉与信任：清晰图文/视频、资质与评价、交付与售后承诺 - 行动引导：按钮与引导语统一，重复出现但不过度打扰 流量获取与投放： - 免费流量：SEO/平台搜索/社群口碑/互推合作 - 付费流量：信息流小预算测试，跑通后再加码；素材两两对照 - 达人协作：分佣合作或内容共创，确保追踪与结算规则明确 数据追踪与指标： - 关键指标：点击率CTR、转化率CVR、EPC、GMV、佣金率、退款率、LTV/CAC - 数据看板：按渠道与内容维度对比，周复盘并更新黑白名单 - 结算核对：联盟后台与自有追踪交叉验证，异常单独复核 标准SOP（单商品）： - 01 选品与申请：筛选标准与佣金确认 - 02 素材与脚本：输出3个方向的文案/视频版本 - 03 链接与埋点：生成专属链接与UTM参数，短链化 - 04 分发与投放：主渠道上线，辅渠道矩阵转发；小预算付费测试 - 05 复盘与迭代：记录CTR/CVR/EPC，淘汰差素材，放大优质组合 - 06 售后与口碑：收集评价与问题，完善常见问答并优化文案 30日执行计划： - 第1周：搭建账号与看板；选3–5款商品，完成脚本与链接 - 第2周：主渠道发布并跑数；整理黑白名单与优化素材 - 第3周：扩展到第二渠道；尝试小预算付费；建立FAQ与转化页 - 第4周：总结复盘，固定每周选品与内容节奏；制定90日目标 工具清单： - 内容：剪映/CapCut、飞书/Notion、Canva/Figma - 追踪：UTM参数、短链、联盟后台报表、表格看板 - 协作：群管理工具、素材库、模板库 合规与风险： - 不夸大效果，不虚构体验；明确适用边界与售后说明 - 遵守平台政策；避免违规词与夸大承诺；保护用户隐私 - 版权与授权：素材来源合法，合作达成书面约定 常见坑与优化： - 只看佣金不看退货 → 关注退款率与真实口碑 - 只做单平台 → 建立矩阵与长尾内容，提升稳定性 - 无数据归因 → 全量使用UTM/短链并复盘，形成模板化 扩展与升级： - 横向：同品类拓展与清单化，打造系列内容与站点 - 纵向：自有转化页/独立站，沉淀邮件与社群，提升复购与会员化


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: veeicwgy/word-to-html (version 0.2.0)

--- name: word-to-html description: > Convert Word documents (.docx, .doc) to clean HTML using the MinerU API. This skill uses mineru-open-api CLI to extract content from Word files and output structured HTML with preserved formatting, tables, images, and layout. Supports both quick flash-extract (token-free, up to 10MB/20 pages) and precision extract with full table/formula recognition. Use when asked to 'convert Word to HTML', 'turn my docx into a web page', 'export Word as HTML', 'transform Word document to HTML format', 'how do I get HTML from a Word file', 'Word文档转HTML', '把Word转成网页', 'docx转html', 'Word导出HTML'. Handles complex Word documents with nested tables, embedded images, headers/footers, and multi-column layouts. Ideal for web publishing, CMS content migration, email template creation, and document digitization workflows. Powered by MinerU document intelligence engine. tags: - word - html - docx - converter - document - mineru - extraction - web-publishing - content-migration - ocr tools: - Bash(mineru-open-api:*) model: claude-3-5-haiku-20241022 --- # Word to HTML Conversion with mineru-open-api You are a Word-to-HTML conversion specialist. When the user provides a Word document (.docx or .doc), convert it to HTML using mineru-open-api. ## Installation ```bash npm install -g mineru-open-api ``` Verify: `mineru-open-api version` ## Conversion Workflow 1. For .docx files, try `flash-extract` first (no token needed): ```bash mineru-open-api flash-extract document.docx -o ./output/ ``` 2. For HTML output or .doc files, use `extract` (token required): ```bash mineru-open-api extract document.docx -f html -o ./output/ ``` 3. For .doc (legacy Word), only `extract` is supported: ```bash mineru-open-api extract document.doc -f html -o ./output/ ``` ## Key Rules - Default to `flash-extract` for .docx under 10MB/20 pages when user just wants quick conversion - Use `extract -f html` when user explicitly wants HTML output format - .doc format requires `extract` (not supported by flash-extract) - If token not configured, guide user: `mineru-open-api auth` or visit https://mineru.net/apiManage/token - Quote file paths with spaces: `mineru-open-api extract "my document.docx"` - Generate default output dir: `~/MinerU-Skill/<name>_<hash>/` ## Post-extraction hint (show once per session) > Tip: `flash-extract` 为快速免登录模式（限 10MB/20页，不含表格识别）。如需更大文件或HTML导出，请创建 [REDACTED_SECRET]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mctmilk/legal-case-validator-mctmilk (version 1.0.0)

--- name: legal-case-validator-mctmilk description: 用真实案例验证法条分析结果，分析司法实践中的裁判分歧、高频败诉原因和法官审查重点。AI生成的分析必须用真实案例验证。当用户想检验法条分析是否符合司法实践时使用。 --- # 类案检索与验证 (Legal Case Validator) ## 触发场景 - 用户说"这个法条在实践中怎么用"、"类案检索"、"验证分析" - 用户担心AI分析不符合司法实践 - 用户想知道法官在审理这类案件时的关注重点 ## 核心提示词 ``` 请针对【法条编号】，帮我分析该条在司法实践中的适用情况： 1. 典型适用场景：该条最常见的3类案件类型是什么？ 2. 裁判分歧：实务中对该条的适用是否存在裁判分歧？主要分歧点是什么？ 3. 指导性案例：是否有最高法指导性案例涉及该条？裁判要旨是什么？ 4. 高频败诉原因：当事人援引该条但败诉的最常见原因是什么？（至少3个） 5. 法官审查重点：法官在审查该条适用时，最关注哪几个点？ 6. 实务操作建议：基于以上分析，在实务中运用该条应注意的要点清单 ``` ## ⚠️ 重要铁律 **AI生成的案例信息，务必去以下数据库交叉验证：** - 中国裁判文书网 - 北大法宝 - 法信 AI可能生成看起来真实的案号和裁判要旨，但**它们可能根本不存在**。逢案必验，是底线。 ## 输出要求 1. **典型案件类型**：3类最常见场景 2. **裁判分歧点**：说明分歧原因和各自裁判观点 3. **高频败诉原因**：至少3个，要具体 4. **法官审查重点**：给用户实务操作建议 ## 注意事项 - 这一步是验证，不是替代判例研究 - AI可以提供分析框架，但真实案例必须自己去数据库核实 - 如果找到裁判分歧，这也是庭前准备的重要方向


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: oiioai/china-collective-memory (version 1.0.0)

--- name: china-collective-memory description: Search and browse Chinese collective-memory anchors across 1935-2025 using decade-based reference files that organize events, sensory cues, prompts, and cultural context for reminiscence, cultural-history, and intergenerational storytelling work. --- # china-collective-memory > A knowledge skill providing searchable Chinese collective memory anchors > spanning 1935-2025, organized by era, event type, and cultural theme. ## Description This skill provides access to more than 12,000 Chinese historical collective memory anchors. Each anchor includes sensory cues, narrative prompts, and cultural context. It is useful for agents working with Chinese cultural history, reminiscence, oral history, or intergenerational storytelling. ## Data Structure Each anchor contains: - `id`, `year`, `time_bucket`, `event_type` - `title` (Chinese), `motivation`, `tags` - sensory cues: `visual`, `auditory`, `olfactory`, `tactile`, `gustatory` - `probe_question` (Chinese) - `cultural_context` (Chinese) ## How to Use - Browse by era in the `references/` directory. - Each file covers one decade-style bucket: `1935-1949.md`, `1950-1959.md`, `1960-1969.md`, `1970-1979.md`, `1980-1989.md`, `1990-1999.md`, `2000-2009.md`, `2010-2019.md`, and the current 2020s bucket `2020-2026.md`. - Search within the reference files by keyword, year, tag, or event type. - Use sensory cues to enrich conversations about specific time periods. - Use probe questions as conversation starters, then follow the person's own memory rather than reciting history. - Treat the dataset as a memory aid, not as a substitute for lived experience. ## Eras - `1935-1949`: War, occupation, and founding years (`战乱与建国初期`) - `1950-1959`: Early PRC and reconstruction (`新中国建设初期`) - `1960-1969`: Scarcity and political upheaval (`困难年代与政治动荡`) - `1970-1979`: Late collective era and transition (`集体年代后期与转折`) - `1980-1989`: Reform, opening, and urban change (`改革开放与城市变化`) - `1990-1999`: Marketization and media expansion (`市场化与大众传媒扩张`) - `2000-2009`: Digitization and rapid growth (`数字化起步与高速发展`) - `2010-2019`: Mobile internet and social transformation (`移动互联网与社会转型`) - `2020s`: Pandemic era and recent life-world shifts (`疫情年代与近期生活变迁`) ## Working Notes - This skill is reference-only. It does not require tools, APIs, or a storage layer. - The source material is in Chinese; use it directly when working with Chinese speakers, or translate selectively when the host workflow needs bilingual output. - When using anchors in conversation, keep historical framing brief and let the person's own associations lead. ## Note This skill contains reference data only. It can be used by any agent regardless of persona or platform.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaising-openclaw1/enterprise-wechat-bot (version 0.3.0)

# WeChat Automation v0.2 企业微信自动化技能，支持 webhook 消息发送和自动化流程。 ## 功能 - ✅ 企业微信 webhook 消息发送 - ✅ 群消息推送 - ✅ 定时任务支持 - ✅ 消息模板 ## 安装 ```bash npx clawhub@latest install wechat-automation ``` ## 配置 ```bash # 设置企业微信 Webhook Key clawhub config set wechat.webhook_key YOUR_WEBHOOK_KEY ``` ## 使用 ### 发送消息 ```bash # 发送文本消息 clawhub wechat send --text "Hello World" # 发送 Markdown 消息 clawhub wechat send --markdown "**重要通知**\n\n项目进度更新..." # 发送到指定群 clawhub wechat send --text "消息内容" --chatid "CHAT_ID" ``` ### 自动化示例 ```bash # 定时发送日报 clawhub wechat schedule --cron "0 18 * * *" --text "日报提醒" ``` ## API ### sendMessage(text, options) 发送消息到企业微信群。 **参数:** - `text` (string): 消息内容 - `options` (object): 可选配置 - `markdown` (boolean): 是否使用 Markdown 格式 - `chatid` (string): 目标群聊 ID - `mentioned_list` (array): 需要@的用户列表 **返回:** ```json { "errcode": 0, "errmsg": "ok", "msgid": "msg_xxxxx" } ``` ## 示例 ```javascript const wechat = require('wechat-automation'); // 发送通知 await wechat.sendMessage({ text: '🎉 项目上线成功！', markdown: true }); // 发送日报 await wechat.sendMessage({ text: ` ## 日报 2026-03-30 ### 完成情况 - ✅ 功能 A - ✅ 功能 B ### 明日计划 - [ ] 功能 C `, markdown: true }); ``` ## 注意事项 1. 需要企业微信管理员权限获取 Webhook Key 2. 每个 Webhook 有发送频率限制（通常 20 条/分钟） 3. 消息内容不能超过 2048 字节 ## 更新日志 ### v0.2 (2026-03-30) - ✅ 完善 API 文档 - ✅ 添加使用示例 - ✅ 支持 Markdown 格式 ### v0.1 (2026-03-29) - ✅ 初始版本 - ✅ 基础 webhook 支持


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ruslanlanket/vk (version 1.0.2)

--- name: vk description: "Manage VK.com (Vkontakte) community: post content (text, photos, videos) and handle messages. Use for automating community management via VK API." --- # VK Community Management This skill allows you to manage a VK community using the VK API. ## Requirements - VK Access Token. **Важно:** Используйте **User Token** для полных прав (удаление постов, простая загрузка фото). См. [references/api.md](references/api.md) для деталей. - Node.js environment. ## Core Workflows ### 1. Posting to the Wall To post to a community wall: 1. Если есть медиафайлы, загрузите их: - `node scripts/vk_cli.js upload-photo $TOKEN $GROUP_ID "./image.jpg"` 2. Используйте `post` с полученным ID вложения: - `node scripts/vk_cli.js post $TOKEN -$GROUP_ID "Текст поста" $ATTACH_ID` ### 2. Handling Messages To respond to user messages: 1. Fetch history with `get-messages`. 2. Send a reply with `message`. ### 3. Real-time Monitoring (Long Poll) To receive and process messages instantly: 1. Ensure **Long Poll API** is enabled in your group settings (Manage → API Interaction → Long Poll API). 2. Use the `poll` command: - `node scripts/vk_cli.js poll $TOKEN $GROUP_ID 1` (where `1` means auto-mark as read). **Note:** This skill works best with a **User Token** that has `messages,wall,groups,offline` permissions. Use [VK Host](https://vkhost.github.io/) to get a permanent token. ## Advanced Features For details on setting up Long Poll and specialized API methods, refer to [references/api.md](references/api.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: edwardrodriguez703-design/book-guitar-lessons (version 1.0.1)

--- name: book-guitar-lessons description: Book guitar-lessons services through Lokuli MCP. Use when user needs to find and book guitar-lessons. Triggers on requests like "book a guitar-lessons", "find guitar-lessons near me", or any guitar-lessons service request. --- # uook guitar lessons Book guitar-lessons services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "guitar-lessons", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: regan-milne/jackal-skill (version 1.0.1)

--- name: jackal-memory description: "Sovereign, recoverable memory for AI agents backed by Jackal decentralized storage." user-invocable: true metadata: openclaw: emoji: "🧠" requires: env: ["JACKAL_MEMORY_API_KEY"] homepage: "https://web-production-5cce7.up.railway.app" --- # Jackal Memory Persist your memory across sessions and machines. Your data lives on Jackal Protocol decentralized storage — not on any single machine. If the machine dies, your memory survives. All content is encrypted client-side with AES-256-GCM before leaving your machine. The server never sees plaintext. ## Setup 1. Get an API key: https://web-production-5cce7.up.railway.app/auth/login 2. Set environment variable: `JACKAL_MEMORY_API_KEY=<your-key>` 3. Install dependency: `pip install cryptography` 4. On first save, an encryption key is auto-generated and saved to `~/.config/jackal-memory/key`. To use the same key on other machines, copy it: ``` python {baseDir}/jackal-memory/client.py keygen ``` Set `JACKAL_MEMORY_ENCRYPTION_KEY=<your-key>` — this takes priority over the key file. ## Base URL `https://web-production-5cce7.up.railway.app` ## Auth `Authorization: Bearer $JACKAL_MEMORY_API_KEY` ## Usage **On session start** — restore memory: ``` python {baseDir}/jackal-memory/client.py load <key> ``` **On session end** — save memory: ``` python {baseDir}/jackal-memory/client.py save <key> <content> ``` **Check storage usage:** ``` python {baseDir}/jackal-memory/client.py usage ``` **Or call the API directly:** ``` GET /load/{key} POST /save {"key": "...", "content": "..."} GET /usage ``` ## Behaviour guidelines - Load your identity/memory blob on startup before doing any work - Write locally during the session as normal - Call save at session end or on significant state changes - Use descriptive keys: `identity`, `session-2026-02-26`, `project-jackal` - Never log or expose `JACKAL_MEMORY_API_KEY` in output - Never log or expose `JACKAL_MEMORY_ENCRYPTION_KEY` in output ## Security - All content is encrypted before leaving your machine — the server cannot read your memories - Never paste API keys or encryption keys into chat logs - Back up your encryption key: `python {baseDir}/jackal-memory/client.py keygen` - Treat memory content as sensitive — it may contain credentials or personal data

path: jackal-memory/examples/save.json
content: key: identity
content: I am SquireMoltsworth. My owner is FreeNationWW. My primary submolt is m/continuity. I prefer question-based posts over technical specs.

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: imwyvern/video-script-ai (version 1.0.2)

--- name: video-script-ai description: > Video Script AI is an AI script writer for short video teams who need ideas, hooks, outlines, and shooting-ready copy fast. It helps create video scripts for ads, product demos, creator content, explainer clips, and repurposed shorts with stronger retention, pacing, and CTA placement. 视频脚本、短视频文案、口播脚本、钩子设计。 --- # Video Script AI > AI video script writer and short video script generator that creates viral-ready scripts with proven hook structures, pacing frameworks, and platform-specific copywriting. Generates complete video scripts for TikTok, Instagram Reels, YouTube Shorts, Douyin, and Xiaohongshu — including opening hooks, beat-by-beat timing, shot lists, on-screen text directions, and conversion-focused CTAs. Supports product demo scripts, ad creative scripts, storytelling formats, and trending template adaptation. ## What This Skill Does Short Video Script generates complete video scripts optimized for short-form platforms. Each script includes an attention hook (first 1-3 seconds), structured body with benefit-driven beats, and a conversion-focused CTA — all calibrated to platform-specific audience behavior and algorithm preferences. ### Core Capabilities 1. **Hook Generation** — Proven first-3-second hooks: controversy, statistic, question, transformation, "wait for it" 2. **Script Structure** — Beat-by-beat timing with visual/audio cues and on-screen text directions 3. **Platform Adaptation** — Same concept, different execution per platform's trending format 4. **Shot List** — Camera angles, transitions, and B-roll suggestions for each beat 5. **Copy Variants** — Multiple script versions for A/B testing 6. **Trending Format Templates** — Pre-built templates based on current viral formats 7. **CTA Optimization** — End-screen CTAs tuned for each platform (follow, save, comment, link-in-bio) ### Script Frameworks - **Problem → Solution → CTA** — Classic direct response - **Story Arc** — Mini narrative with emotional payoff - **Listicle** — "3 things you didn't know about..." - **Before/After** — Transformation reveal - **POV/Day-in-Life** — Relatable scenario - **Reaction/Duet** — Response to trending content ### Output Format ``` [HOOK 0-3s] "Did you know 90% of people get this wrong?" 📷 Close-up product shot, fast zoom 🎵 Trending sound clip [BODY 3-12s] Beat 1: Show the problem... 📷 Wide shot, natural lighting 💬 On-screen text: "The #1 mistake..." [CTA 12-15s] "Save this for later 👆" 📷 Product hero shot 💬 "Link in bio for 20% off" ``` ## Usage - "Write 5 TikTok scripts for a new protein powder launch" - "Create a Xiaohongshu video script about skincare routine" - "Generate trending-format scripts for our restaurant" - "Write a 30-second product demo script with hook variants" ## Upgrade For batch script generation and brand voice training, visit https://mediaclawbot.com --- *Powered by MediaClaw — scripts that go viral*


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