OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rampagepeter/skill-optimizer (version 1.0.0)

--- name: skill-optimizer description: >- Analyzes AI conversation logs to evaluate skill performance and generate optimization suggestions. Use when user mentions "优化技能", "迭代技能", "skill optimization", "improve skill", or provides a conversation log for skill analysis. metadata: author: peterpan version: '1.0' title: 技能优化助手 description_zh: 分析 AI 对话记录，评估技能表现，生成优化建议并执行修改 --- # 技能优化助手 ## Overview 通过分析用户与 AI 的真实对话记录，对比技能的设计意图与实际表现，结合技能设计最佳实践，输出可落地的优化方案。 ## When to Use - 用户提供了一段对话记录，想分析某个技能的实际效果 - 用户说"优化技能"、"迭代技能"、"分析技能表现" - 用户想改进某个技能但不确定从哪里入手 不适用于： - 从零创建新技能 - 与技能无关的对话分析 ## Workflow ### Phase 1: 收集输入 1. 获取对话内容：用户直接粘贴文本，或提供文件路径（读取文件） 2. 确定目标技能： - 用户明确告知 → 直接使用 - 用户未告知 → 从对话内容中推断技能名称，向用户确认 3. 读取目标技能的 SKILL.md 原始文档 ### Phase 2: 分析 从以下维度对比分析： #### 2.1 触发与意图匹配 - 技能是否在正确的场景被触发 - description 中的触发词是否覆盖了实际使用场景 - 是否存在误触发或漏触发 #### 2.2 流程执行 - SKILL.md 中定义的工作流是否被完整执行 - 哪些步骤被跳过、乱序或重复 - 流程中是否有冗余或缺失的环节 #### 2.3 输出质量 - 实际输出是否符合技能设计的预期格式和内容 - 输出是否对用户有实际帮助 - 是否存在信息过多或过少的问题 #### 2.4 交互体验 - 与用户的交互是否流畅自然 - 是否在该问的时候问了、不该问的时候没问 - 引导用户的方式是否清晰 #### 2.5 指令有效性 - SKILL.md 中哪些指令被 AI 准确执行 - 哪些指令被忽略或误解 - 指令的表述是否足够明确 ### Phase 3: 输出报告 按以下结构输出分析报告： ``` ## 技能分析报告：[技能名称] ### 基本信息 - 技能名称： - 分析对话轮数： - 技能版本： ### 总体评估 一段话概括技能的整体表现。 ### 各维度分析 #### 触发与意图匹配 - 现状：... - 问题：... - 建议：... #### 流程执行 - 现状：... - 问题：... - 建议：... #### 输出质量 - 现状：... - 问题：... - 建议：... #### 交互体验 - 现状：... - 问题：... - 建议：... #### 指令有效性 - 现状：... - 问题：... - 建议：... ### 优化建议汇总 按优先级排列的具体修改建议，每条包含： 1. 修改位置（SKILL.md 的哪个部分） 2. 当前内容 3. 建议修改为 4. 修改理由 ``` ### Phase 4: 确认与执行 1. 将报告呈现给用户 2. 询问用户确认哪些建议需要执行 3. 用户确认后，直接修改目标技能的 SKILL.md 4. 修改完成后展示变更摘要 ## Common Mistakes | 问题 | 解决 | |------|------| | 仅凭一次对话就下结论 | 在报告中注明样本量，建议用户提供更多对话 | | 建议过于笼统 | 每条建议必须指向 SKILL.md 的具体位置和具体改法 | | 忽略技能的设计意图 | 先理解原始设计再评判，避免偏离技能定位 | | 一次改太多 | 建议分批迭代，每次聚焦最关键的 2-3 个改进点 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: sxliuyu/docker-manager (version 1.0.2)

--- name: docker-manager version: 1.0.0 description: Docker 容器管理。查看、启动、停止、删除容器，监控资源使用，查看日志。适合运维和开发。 author: OpenClaw triggers: - "Docker" - "容器管理" - "Docker监控" - "docker" - "容器" --- # Docker Manager 🐳 Docker 容器管理工具，查看、启动、停止、删除容器，监控资源，查看日志。 ## 功能 - 📋 列出容器（运行中/全部） - ▶️ 启动/停止/重启容器 - 📊 容器资源监控（CPU/内存/网络） - 📝 查看容器日志 - 🗑️ 删除容器/镜像 - 🔍 容器详情查询 - 📦 镜像管理 ## 使用方法 ### 列出容器 ```bash python3 scripts/docker_mgr.py ps python3 scripts/docker_mgr.py ps --all ``` ### 启动/停止/重启 ```bash python3 scripts/docker_mgr.py start nginx python3 scripts/docker_mgr.py stop nginx python3 scripts/docker_mgr.py restart nginx ``` ### 查看资源监控 ```bash python3 scripts/docker_mgr.py stats python3 scripts/docker_mgr.py stats nginx ``` ### 查看日志 ```bash python3 scripts/docker_mgr.py logs nginx python3 scripts/docker_mgr.py logs nginx --tail 100 python3 scripts/docker_mgr.py logs nginx --follow ``` ### 容器详情 ```bash python3 scripts/docker_mgr.py inspect nginx ``` ### 镜像列表 ```bash python3 scripts/docker_mgr.py images ``` ### 删除容器/镜像 ```bash python3 scripts/docker_mgr.py rm nginx python3 scripts/docker_mgr.py rmi nginx:latest ``` ## 示例 ```bash # 查看所有容器（包括停止的） python3 scripts/docker_mgr.py ps --all # 查看运行中容器的资源使用 python3 scripts/docker_mgr.py stats # 查看特定容器的最近50行日志 python3 scripts/docker_mgr.py logs mycontainer --tail 50 # 批量停止所有运行中的容器 python3 scripts/docker_mgr.py stop $(docker ps -q) # 删除已停止的容器 python3 scripts/docker_mgr.py prune ``` ## 常用 Docker 命令速查 | 操作 | 命令 | |------|------| | 列出容器 | `docker ps -a` | | 启动容器 | `docker start <name>` | | 停止容器 | `docker stop <name>` | | 查看日志 | `docker logs -f <name>` | | 进入容器 | `docker exec -it <name> bash` | | 查看资源 | `docker stats` |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: hdguodada/lobster-agent (version 1.0.0)

--- name: lobster-agent description: 服务器监控Agent，自动采集系统指标并上报到Coze大龙虾平台，支持CPU/内存/磁盘/网络监控、告警推送和自动节点注册。 --- # 🦞 小龙虾监控Agent Skill (lobster-agent) ## Overview 此Skill为服务器监控代理，可自动采集系统运行指标并上报到Coze大龙虾平台，具备以下核心功能： 1. **指标采集** – CPU使用率、负载、内存使用率、磁盘使用率、网络流量等。 2. **自动告警** – 当指标超过阈值时自动产生告警（支持warning/critical两级）。 3. **节点注册** – 安装时自动注册到Coze平台，生成唯一Node ID。 4. **数据上报** – 心跳包、监控数据、告警数据分别上报到对应数据集。 5. **系统服务** – 后台运行，开机自启，崩溃自动重启。 ## Prerequisites 1. 操作系统：Linux（支持systemd） 2. 依赖：Python3、pip3、curl（系统默认一般已安装） 3. 网络：可访问Coze API（https://api.coze.cn） 4. 权限：root权限（安装系统服务需要） ## Usage Steps ### 1. 安装Agent 直接运行内置安装脚本即可完成全自动化安装： ```bash # 自动执行安装流程（OpenClaw触发） ``` 安装过程会自动完成： - 环境检查和依赖安装（requests、psutil） - 工作目录创建（/opt/lobster-agent、/var/log/lobster-agent） - Agent主程序生成 - 节点自动注册，获取Node ID - 配置文件生成 - 管理命令创建（/usr/local/bin/lobster） - systemd服务创建并启动 ### 2. 管理Agent 安装完成后可通过`lobster`命令管理服务： ```bash # 查看服务状态 lobster status # 查看实时日志 lobster logs # 启动服务 lobster start # 停止服务 lobster stop # 重启服务 lobster restart # 完全卸载 lobster uninstall ``` ### 3. 配置说明 配置文件路径：`/opt/lobster-agent/config.json` ```json { "node_id": "自动生成的节点ID", "api_key": "Coze API密钥", "coze_base_url": "Coze API地址", "dataset_ids": { "nodes": "节点信息数据集ID", "monitor_data": "监控数据数据集ID", "alerts": "告警数据数据集ID" }, "heartbeat_interval": 300, // 心跳间隔（秒） "report_interval": 1800, // 监控数据上报间隔（秒） "enable_hardware_check": true, "enable_log_check": true } ``` ### 4. 告警阈值 | 指标 | Warning阈值 | Critical阈值 | |------|-------------|--------------| | CPU使用率 | >80% | >90% | | 内存使用率 | >85% | >95% | | 磁盘使用率 | >70% | >90% | ## Example ### 安装成功输出 ``` 🎉 ============================================== ✅ 小龙虾Agent安装成功！ 📊 Node ID: 7618478715609055278 📝 管理命令: lobster [status|start|stop|restart|logs|uninstall] 📜 查看日志: lobster logs 🌐 数据已自动上报到Coze平台 🎉 ============================================== ``` ### 查看状态 ```bash lobster status ``` **输出示例：** ``` ● lobster-agent.service - Lobster Monitor Agent Loaded: loaded (/etc/systemd/system/lobster-agent.service; enabled; vendor preset: enabled) Active: active (running) since Thu 2026-03-19 08:41:23 CST; 10min ago Main PID: 12345 (python3) Tasks: 1 (limit: 4915) Memory: 20.0M CGroup: /system.slice/lobster-agent.service └─12345 /usr/bin/python3 /opt/lobster-agent/main.py ``` ## Data Structure ### 上报的监控数据字段 | 字段 | 类型 | 说明 | |------|------|------| | node_id | string | 节点唯一标识 | | timestamp | string | 上报时间（YYYY-MM-DD HH:MM:SS） | | cpu_usage | float | CPU使用率（%） | | memory_usage | float | 内存使用率（%） | | disk_usage | float | 磁盘使用率（%） | | load_1 | float | 1分钟负载 | | load_5 | float | 5分钟负载 | | load_15 | float | 15分钟负载 | | network_in | float | 入站流量（KB/s） | | network_out | float | 出站流量（KB/s） | | status | string | 节点状态（healthy/warning/critical） | | alerts | string | 告警列表（JSON格式） | ## When Not to Use - 非Linux操作系统（不支持systemd） - 没有root权限的环境 - 无法访问Coze API的离线环境 - 仅需要本地监控不需要云端上报的场景 ## References - Coze API文档：https://www.coze.cn/docs/developer-docs/api - 大龙虾监控平台：https://coze.cn/s/7618478715609055278


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guohongbin-git/bilibili-creator-cn (version 1.0.0)

--- name: bilibili-creator-cn version: 1.0.0 description: Bilibili Creator Assistant - Tag recommendations, posting times, title optimization, and UP analysis license: MIT --- # Bilibili Creator Assistant 📺 AI-powered assistant for Bilibili (B站) content creators. ## Features ### 1. Tag & Zone Recommendations 🏷️ Get tag and zone suggestions for your videos. ```bash ./scripts/tags.sh "游戏" ``` ### 2. Best Posting Time ⏰ Learn when to post for maximum views. ```bash ./scripts/time.sh "游戏区" ``` ### 3. Title Optimization ✍️ Improve your video titles (B站 style). ```bash ./scripts/title.sh "今天打了一把游戏" ``` ### 4. UP Analysis 📊 Study successful UP主 (creators). ```bash ./scripts/analyze.sh "@老番茄" ``` ## Use Cases - 新人UP主：快速了解平台规则 - 内容优化：提升播放量和互动 - 竞品研究：学习成功UP主 - 数据驱动：基于数据做内容决策 ## Installation ```bash clawhub install bilibili-creator-cn ``` ## Author money-maker-ai ## Changelog ### v1.0.0 (2026-02-18) - Initial release


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: samuelhsin/crypto-market-news (version 1.1.0)

--- name: crypto-market-news description: Fetch real-time crypto and market news from major sources (CoinDesk, CoinTelegraph, Decrypt, Blockworks). Use when the user asks for market news, latest crypto news, news about a specific token (BTC, ETH, ZEC, etc.), or wants a market overview before trading decisions. Triggers on phrases like "市場消息", "最新消息", "新聞", "market news", "what's happening in crypto", "news about [token]", "any news on [coin]". --- # Crypto Market News Fetch live crypto news from public RSS feeds — no API key required. ## Script `scripts/fetch_news.sh [keyword] [hours]` - `keyword` — filter by token or topic (e.g. `bitcoin`, `ZEC`, `ETF`, `regulation`). Leave empty for all news. - `hours` — lookback window, default `24` ```bash # All news, last 24h bash skills/crypto-market-news/scripts/fetch_news.sh # ZEC-specific news, last 48h bash skills/crypto-market-news/scripts/fetch_news.sh "zcash" 48 # Regulation news bash skills/crypto-market-news/scripts/fetch_news.sh "regulation" 24 ``` ## Sources - CoinDesk (feedburner) - CoinTelegraph - Decrypt - CryptoSlate ## Workflow 1. Run the script with relevant keyword (use token name or topic) 2. Summarize top stories in plain language 3. Flag any that directly affect the user's current holdings or trade decisions 4. Add sentiment signal (bullish / neutral / bearish) based on tone of headlines


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

--- name: "aide" version: "1.0.0" description: "AIDE file integrity monitoring reference. Database initialization, integrity checks, update workflow, aide.conf configuration, selection rules, report parsing, and production deployment with CIS benchmark compliance." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [aide, integrity, ids, security, linux, compliance] category: "sysops" --- # AIDE Advanced Intrusion Detection Environment — file integrity monitoring reference. ## Commands | Command | Description | |---------|-------------| | `intro` | What is AIDE, how it works, vs Tripwire | | `init` | Initialize the baseline database | | `check` | Run integrity check, exit codes | | `update` | Update database after changes | | `config` | aide.conf configuration reference | | `rules` | Selection rules and attribute groups | | `reporting` | Report formats, email alerts, SIEM | | `deploy` | Production deployment best practices |


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

--- name: prowlarr-search description: 用Prowlarr搜索影视资源，搜索torrent种子，磁力链接Magnet metadata: {"openclaw":{"emoji":"🔍 ","requires":{"bins":["python3"],"env":["PROWLARR_BASE_URL", "PROWLARR_API_KEY"]},"primaryEnv":"PROWLARR_API_KEY"}} --- # 资源搜索 ## 用法 通过 Prowlarr API 搜索资源（ torrents/usenet 等），并返回 JSON 格式的搜索结果列表。 通用搜索 ```bash python3 {baseDir}/scripts/prowlarr-search.py "<query>" ``` 指定季数，集数搜索剧集 ```bash python3 {baseDir}/scripts/prowlarr-search.py "<query>" --type tv [--season SEASON] [--ep EP] ``` 输出样例： ```json [ { "title": "Example.Title.2024.1080p", "size": "2.50 GB", "indexer": "SomeIndexer", "guid": "magnet:?xt=...", "publishDate": "2024-01-15T10:30:00Z" } ] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: omactiengartelle/anime-character-generator (version 1.0.0)

--- name: anime-character-generator description: Generate stunning full-body anime characters with custom outfits, hairstyles, and poses. Perfect for OC creation, fan art, visual novels, profile avatars, and anime-style portraits. Supports kawaii, shonen, shojo, and isekai aesthetics via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Anime Character Generator Generate stunning full-body anime characters with custom outfits, hairstyles, and poses. Perfect for OC creation, fan art, visual novels, profile avatars, and anime-style portraits. Supports kawaii, shonen, shojo, and isekai aesthetics. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create anime character generator images. ## Quick start ```bash node animecharactergenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add omactiengartelle/anime-character-generator ```

path: package.json
content: name: anime-character-generator
version: 1.0.0
type: module
description: Anime Character Generator — AI-powered anime character generator ai
license: MIT

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: clawkk/kol (version 0.1.0)

--- name: "KOL合作管理" description: "提供KOL合作管理的可落地指南与SOP。在开展KOL合作管理相关工作时调用。" --- # KOL合作管理 (kol-collaboration) ## 目标与场景 - 明确业务目标、核心指标与应用场景；设定落地时限与负责人。 ## 操作步骤 1. 明确范围与资源；2. 制定SOP；3. 数据接入与看板搭建； 4. 小流量试点；5. 复盘优化；6. 标准化沉淀与知识库更新。 ## 指标 - 主指标与守护指标成体系；定义口径并固定报表节奏。 ## 风险与合规 - 遵守中国本地法规与平台规则；个人信息保护与数据最小化。 ## 可复用清单 - 模板、脚本、Checklist、报表与会议纪要结构。 生成日期：2026-03-17


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xejrax/docker-ctl (version 1.0.0)

--- name: docker-ctl description: "Inspect containers, logs, and images via podman" metadata: { "openclaw": { "emoji": "🐳", "requires": { "bins": ["podman"] }, "install": [], }, } --- # Docker Ctl Inspect containers, logs, and images via podman. On Bazzite/Fedora, podman is the default container runtime and is always available. ## Commands ```bash # List running containers docker-ctl ps # View container logs docker-ctl logs <container> # List local images docker-ctl images # Inspect a container docker-ctl inspect <container> ``` ## Install No installation needed. Bazzite uses `podman` as its container runtime and it is pre-installed.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain1/repo-bundler (version 2.0.2)

--- name: "repo-bundler" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Repo Bundler concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [repo,bundler, reference] category: "devtools" --- # Repo Bundler Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Repo Bundler concepts, best practices, and implementation patterns. 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: wenrl2006/ai-token-broker (version 0.1.0)

# TokenBroker Skill — 统一AI API网关 > 智能路由 + 预算管控 + 费用统计。自动选最省钱的模型，深度任务切满血版，所有调用记入账单。 ## 前置条件 本 Skill 依赖 TokenBroker 后端服务（`localhost:8766`）。安装时自动检测并启动。 如果端口8766已被占用，或无法自动启动，请手动启动： ```bash cd ~/.openclaw/workspace/production-system/token-broker npx ts-node src/server.ts ``` ## 核心能力 ### 1. 智能路由（自动执行） Agent 调 LLM 时，自动走 TokenBroker 决策： ``` 任务进来 → 查预算（TokenBudget联动的日/月预算） → 选供应商（默认选最便宜的可用模型） → 调API → 记录消耗到统计 ``` 路由策略： | 任务类型 | 默认模型 | 说�� | |---------|---------|------| | 日常问答 | DeepSeek V3 Lite（免费） | 预算友好 | | 分析/写作 | DeepSeek V4 Fast（¥2/百万token） | 性价比优先 | | 深度推理/代码/命理 | DeepSeek V4 Thinking（¥8/百万token） | 深度任务用满血 | | 预算紧张 + 非关键 | 自动降级到免费模型 | 自动执行 | ### 2. 管理命令 用户可以通过对话管理 TokenBroker： | 命令 | 行为 | |------|------| | `Broker状态` / `网关状态` | 检查服务是否运行、连通性 | | `路由统计` / `API统计` | 查看所有路由调用记录 | | `设置路由 [任务类型] [供应商]` | 自定义路由规则 | | `今天花了多少` | 查看当日费用汇总 | | `Broker帮助` | 显示所有可用命令 | ### 3. 与TokenBudget联动 安装 TokenBroker 后，TokenBudget 的预算查询会自动显示： ``` 📊 AI额度日报 — ⚡ 已对接TokenBroker网关 ━━━━━━━━━━━━━━━━━━━━ 今日已用: 45,320 / 100,000 token (45%) 路由优化: 自动选最便宜模型 ✅ ━━━━━━━━━━━━━━━━━━━━ ``` ### 4. 自动健康检查 每次初始化时自动检测： ``` 检查 http://localhost:8766/api/health → 200 → 正常加载，LLM调用走Broker → 失败 → 降级到直调，提示用户启动服务 ``` ## 安装说明 ```bash # 从ClawHub安装 clawhub install ai-token-broker # 或从OpenClaw openclaw skills install ai-token-broker ``` 安装后： 1. 自动编译 TypeScript 代码 2. 检测端口8766是否可用 3. 启动 TokenBroker 服务 4. 配置为 Supervisor 托管 ## 不适用场景 - 未安装 TokenBudget（建议先装TokenBudget） - 用户明确指定直调模型（不走路由） - cron定时任务（不计入日预算单独统计） --- *最后更新：2026-05-22*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: zhairen/wevoicereply (version 1.0.3)

--- name: wevoicereply slug: wevoicereply version: 1.0.3 description: | 【自动化语音合成与推送链路】 当用户要求语音回复、读一下或发声时，必须严格执行以下三步，严禁跳步： ### 第一步：文案生成 (Prompt A) 根据上下文生成自然、温暖的口语化文本。 请在长句子中加入中文逗号 `，` 以确保音频合成时有自然的停顿。 ### 第二步：音频合成 (执行 voice_reply_skill.py) **这是本工具的核心动作。** 将第一步生成的文本作为 `text` 参数传入，后台将自动运行 `voice_reply_skill.py` 脚本。 - **脚本逻辑**：Piper TTS 合成 WAV -> FFmpeg 转码 AMR -> 上传获取 URL。 - **产出物**：执行完成后，你会获得一个 JSON 对象，包含 `url` 字段（例如：`{"url": "https://.../xxx.amr"}`）。 ### 第三步：指令发送 (调用系统内置工具) 获取 URL 后，禁止结束对话，立即发起第二次调用。 - **目标工具**：`default_api.message` - **执行参数**： { "action": "send", "contentType": "voice", "media": "[此处填入从第二步脚本返回的url]", "target": "@all" } 【准则】发送完成后，仅需告知用户“语音已发送”，不得再次输出音频 URL。 parameters: type: object properties: text: type: string description: "传递给 voice_reply_skill.py 的合成文本" required: - text metadata: openclaw: emoji: "🎤" os: ["linux"] # 核心：将此技能名直接绑定到具体的 .py 脚本 command: "python3 {{path}}/voice_reply_skill.py '{{text}}'" requires: bins: ["ffmpeg", "python3"] --- # WeVoiceReply (稳定版) ## 执行架构说明 1. **解耦设计**：本技能定义文件负责流程编排，`voice_reply_skill.py` 负责底层复杂的 TTS 转换。 2. **环境要求**：确保系统中已安装 `ffmpeg-amr` 且 Python 环境中配置了 `piper-tts`。 3. **参数传递**：`{{text}}` 使用单引号包裹，确保 Shell 执行脚本时不会因为文本中的空格或特殊符号导致断裂。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: square123/metaso-search-skill (version 1.0.0)

--- name: metaso-search description: Search the web using Metaso AI Search API. Use for live information, documentation, or research topics. metadata: { "openclaw": { "emoji": "🔍", "requires": { "bins": ["python"], "env": ["METASO_API_KEY"] }, "primaryEnv": "METASO_API_KEY" } } --- # Metaso Search Search the web via Metaso AI Search API. ## Usage ```bash python skills/metaso-search/scripts/search.py '<JSON>' ``` ## Request Parameters | Param | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | q | str | yes | - | Search query | | scope | str | no | webpage | Search scope: webpage, news, paper, etc. | | size | int | no | 10 | Number of results (1-50) | | page | int | no | 1 | Page number | | conciseSnippet | bool | no | false | Return concise snippet | | includeSummary | bool | no | false | Include AI summary | | includeRawContent | bool | no | false | Fetch raw content from sources | ## Examples ```bash # Basic search python scripts/search.py '{"q":"OpenClaw AI"}' # With options python scripts/search.py '{ "q": "人工智能最新进展", "size": 5, "includeSummary": true }' ``` ## API Reference - **官方文档**: https://metaso.cn/search-api/playground - **Endpoint**: `https://metaso.cn/api/v1/search` - **Method**: POST - **Auth**: Bearer token in `Authorization` header - **Content-Type**: `application/json` ## Request Example ```bash curl --location 'https://metaso.cn/api/v1/search' \ --header '[REDACTED_SECRET]' \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --data '{ "q": "搜索关键词", "scope": "webpage", "size": 10, "includeSummary": false, "includeRawContent": false, "conciseSnippet": false }' ``` ## Response Example ```json { "credits": 3, "searchParameters": { "q": "搜索关键词", "scope": "webpage", "size": 10 }, "webpages": [ { "title": "标题", "link": "https://example.com", "snippet": "摘要内容", "score": "high", "date": "2026-03-22" } ], "total": 25 } ``` ## Current Status ✅ Ready to use


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: anmolnagpal/bandwidth-optimizer (version 1.0.0)

--- name: azure-bandwidth-optimizer description: Identify and reduce Azure bandwidth and egress costs — often the most invisible Azure cost driver tools: claude, bash version: "1.0.0" pack: azure-cost tier: business price: 79/mo permissions: read-only credentials: none — user provides exported data --- # Azure Bandwidth & Egress Cost Optimizer You are an Azure networking cost expert. Bandwidth charges are invisible until they become a major line item. > **This skill is instruction-only. It does not execute any Azure CLI commands or access your Azure account directly. You provide the data; Claude analyzes it.** ## Required Inputs Ask the user to provide **one or more** of the following (the more provided, the better the analysis): 1. **Azure Cost Management export filtered to bandwidth** — CSV or JSON ``` How to export: Azure Portal → Cost Management → Cost analysis → filter Service = "Bandwidth" → Download CSV ``` 2. **Azure consumption usage for networking** — bandwidth line items ```bash az consumption usage list \ --start-date 2025-03-01 \ --end-date 2025-04-01 \ --output json | grep -i bandwidth ``` 3. **Virtual network and Private Endpoint inventory** — current network topology ```bash az network vnet list --output json az network private-endpoint list --output json ``` **Minimum required Azure RBAC role to run the CLI commands above (read-only):** ```json { "role": "Cost Management Reader", "scope": "Subscription", "note": "Also assign 'Network Reader' for virtual network inspection" } ``` If the user cannot provide any data, ask them to describe: which regions your services run in, approximate monthly bandwidth charges, and whether Private Endpoints are currently used. ## Steps 1. Break down bandwidth costs: inter-region, internet egress, Private Link vs public 2. Identify regions with highest egress charges 3. Map Azure CDN / Front Door offload opportunities 4. Identify Private Endpoint migration candidates 5. Calculate ROI of each recommendation ## Output Format - **Bandwidth Breakdown**: type, monthly cost, % of total - **Region Egress Heatmap**: top regions by egress cost - **Optimization Opportunities**: - Azure CDN for static assets / API caching - Azure Front Door for global traffic acceleration - Private Endpoints to eliminate public internet egress - Blob Storage lifecycle policies to reduce retrieval costs - **ROI Table**: change, implementation effort, monthly savings - **Bicep/ARM Snippet**: Private Endpoint config for top candidates ## Rules - Flag traffic from VMs to Azure PaaS services going over public internet — Private Endpoints fix this - Calculate CDN ROI: CDN egress is typically 30–50% cheaper than Blob direct egress - Note: Zone Redundant Storage has no inter-AZ transfer charges (unlike AWS) - Never ask for credentials, access keys, or secret keys — only exported data or CLI/console output - If user pastes raw data, confirm no credentials are included before processing


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