OpenClaw/clawhub-security-signals huggingface dataset:
Skill: edwardrodriguez703-design/book-party (version 1.0.1)

--- name: book-party description: Book party services through Lokuli MCP. Use when user needs to find and book party. Triggers on requests like "book a party", "find party near me", or any party service request. --- # uook party Book party 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": "party", "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: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: aidalong/us-stock-ipo-scanner (version 1.0.0)

--- name: us-stock-ipo-scanner description: | 美股IPO机会扫描与早期信号捕获。用于发现美股新股、IPO、直接上市、基金等投资机会。 当用户提到：美股、IPO、新股、上市、new listing、IPO scanner、IPO机会、错过股票、VCX、Fundrise等关键词时触发此技能。 包含三层信号源架构：Layer 1新机会发现、Layer 2起飞信号、Layer 3爆发确认，以及关键词监控列表。 --- # 美股IPO机会扫描 🍇 ## 核心理念 **真正有价值的信号顺序是：Twitter → Reddit → Benzinga → 新闻媒体** 大多数人的问题是"只看新闻"，错过了早期信号。 --- ## 三层信号源架构 ### 🟢 Layer 1：新机会发现（核心） | 来源 | RSS地址 | 用途 | |------|---------|------| | Invezz IPO Feed（强烈推荐） | https://invezz.com/news/stocks/ipos/feed/ | 覆盖全球IPO + listing + 新基金 | | Nasdaq IPO页面 | 需用RSS.app转：https://www.nasdaq.com/market-activity/ipos | 隐藏金矿 | | CNBC IPO News | https://www.cnbc.com/id/100003114/device/rss/rss.html | 偏慢但有确认价值 | | Seeking Alpha IPO | https://seekingalpha.com/market-news/ipos.xml | 交易视角 | **Reddit搜索RSS：** - https://www.reddit.com/search.rss?q=IPO - https://www.reddit.com/search.rss?q=new+listing - https://www.reddit.com/search.rss?q=AI+stock ### 🟡 Layer 2：起飞信号 | 来源 | RSS地址 | 用途 | |------|---------|------| | Benzinga 通用新闻 | https://www.benzinga.com/feed | 必须重仓的信号源 | | Benzinga Markets | https://www.benzinga.com/markets.rss | 市场异动 | | Benzinga Trading Ideas | https://www.benzinga.com/trading-ideas.rss | 交易思路 | | Benzinga Stock Ideas | https://www.benzinga.com/stock-ideas.rss | 个股推荐 | | Invezz AI热点 | https://invezz.com/news/stocks/artificial-intelligence/feed/ | 题材热点 | **Twitter关键词RSS（最重要）：** 用 https://rss.app 生成，监控关键词： - $VCX（已有标的变化） - "new listing" - "direct listing" - "closed-end fund" - "AI exposure" - "pre IPO exposure" - "SpaceX fund" - "new ETF" ### 🔴 Layer 3：爆发确认 | 来源 | RSS地址 | 用途 | |------|---------|------| | Reddit WSB | https://www.reddit.com/r/wallstreetbets/.rss | 妖股爆发前夜 | | Reddit stocks | https://www.reddit.com/r/stocks/.rss | 股票讨论 | | Benzinga Breaking | https://www.benzinga.com/feed | 突发新闻 | --- ## VCX类标的专用关键词 当发现类似VCX（Fundrise Innovation Fund）的机会时，监控这些关键词： ``` new listing direct listing closed-end fund AI exposure pre IPO exposure SpaceX fund VC fund new ETF $XXX (具体股票代码) ``` --- ## 进阶：自动雷达（可选） 如需更自动化，可以实现： 1. **RSS → NLP → 打标签 → 自动提醒** 2. **设置类似VCX标的出现的推送** 详细内容见 [sources.md](references/sources.md) --- ## 使用方式 1. 定期扫描 Layer 1 sources 获取新机会 2. 发现潜在标的后，用 Layer 2 关键词监控进一步确认 3. 参考 Layer 3 确认市场情绪 4. 符合条件时推送提醒用户 --- ## 输出格式 扫描报告包含： - 📌 新发现标的（来源、关键词、初步分析） - 🔥 起飞信号（Layer 2 验证情况） - 📊 市场情绪（Layer 3 参考） - 💡 建议（是否需要深入研究）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jerryxn/xiaog-elevenlabs-tts (version 1.0.0)

--- name: elevenlabs-tts description: Set up, test, benchmark, and use ElevenLabs text-to-speech as an independent TTS skill, including HTTP streaming, WebSocket streaming guidance, voice listing, model/output-format selection, latency tuning, and safe ELEVENLABS_API_KEY handling. --- # ElevenLabs TTS Use this skill for ELE / ElevenLabs voice generation, low-latency streaming TTS, voice IDs, voice listing, output formats, or TTS benchmark work. ## Non-negotiables - Keep this skill independent from Xiaomi MiMo TTS. Do not mix API keys, endpoint shapes, voice IDs, or scripts. - Treat `ELEVENLABS_API_KEY` as a secret. Never echo it, write it into skill files, commit it, or expose it in logs/screenshots. - Prefer environment variable injection for tests: `ELEVENLABS_API_KEY=... python3 ...`. - If the key is missing, verify scripts and docs locally, then report that live API synthesis is blocked by missing credentials. ## Standard workflow 1. Verify API key availability without printing it. 2. List voices with `scripts/list_voices.py`; choose a requested voice by ID/name or use the first available voice for smoke tests. 3. Generate a short HTTP streaming sample with `scripts/tts_stream_http.py`. 4. Capture evidence: output path, bytes, chunks, `ttfb_seconds`, elapsed time, model, output format. 5. If audio validation tools exist (`ffprobe`, `afinfo`, or `file`), inspect the generated file before claiming success. ## Recommended defaults - Model: `eleven_flash_v2_5` for lowest latency; `eleven_turbo_v2_5` for higher quality with low latency; `eleven_multilingual_v2` for broad multilingual quality. - Endpoint: `POST /v1/text-to-speech/{voice_id}/stream` when the full text is available. - Output: `mp3_44100_128` for shareable files; `pcm_16000`/`pcm_24000` for realtime playback pipelines. - Latency: `optimize_streaming_latency=2` or `3`; avoid `4` unless the user accepts possible number/date mispronunciation. - Voice settings: keep `use_speaker_boost=false` for low latency. ## Quick commands List voices: ```bash [REDACTED_SECRET]" python3 skills/elevenlabs-tts/scripts/list_voices.py ``` HTTP streaming TTS benchmark: ```bash [REDACTED_SECRET]" python3 skills/elevenlabs-tts/scripts/tts_stream_http.py \ --text '野哥，ElevenLabs 流式语音测试成功。' \ --voice-name Rachel \ --model eleven_flash_v2_5 \ --output-format mp3_44100_128 \ --optimize-streaming-latency 2 \ --out /tmp/elevenlabs-skill-test.mp3 ``` If `--voice-name` is omitted, the script uses the first voice returned by `/v1/voices`. ## When to read more - API contract, request/response details, WebSocket notes, and troubleshooting: `references/elevenlabs-api.md`. - Deterministic scripts: - `scripts/list_voices.py` - `scripts/tts_stream_http.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gumadeiras/office-quotes (version 1.2.4)

--- name: office-quotes description: Generate random quotes from The Office (US). Provides access to 326 offline quotes plus online mode with SVG cards, character avatars, and full episode metadata via the akashrajpurohit API. Use for fun, icebreakers, or any task requiring The Office quotes. metadata: {"clawdbot":{"requires":{"bins":["office-quotes"]},"install":[{"id":"node","kind":"node","package":"@gumadeiras/office-quotes","bins":["office-quotes"],"label":"Install office-quotes (npm)"}]}} --- # office-quotes Skill Generate random quotes from The Office (US) TV show. ## Installation ```bash npm install -g @gumadeiras/office-quotes ``` ## Usage ```bash # Random offline quote (text only) office-quotes # API quote with SVG card office-quotes --source api # PNG output (best for Telegram) office-quotes --source api --format png # Light theme office-quotes --source api --theme light ``` ## Options | Option | Description | |--------|-------------| | `--source <src>` | Quote source: local (default), api | | `--format <fmt>` | Output format: svg, png, jpg, webp (default: svg) | | `--theme <theme>` | SVG theme: dark, light (default: dark) | | `--json` | Output as JSON | ## Quote Examples > "Would I rather be feared or loved? Easy. Both. I want people to be afraid of how much they love me." — Michael Scott > "Bears. Beets. Battlestar Galactica." — Jim Halpert > "Whenever I'm about to do something, I think, 'Would an idiot do that?' And if they would, I do not do that thing." — Dwight Schrute ## Source https://github.com/gumadeiras/office-quotes-cli


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lentiancn/skill-terminology (version 1.0.0)

--- name: skill-terminology description: "terminology:uml related" --- # Description Refers to a collection of terms, names, expressions and translations that share the same, similar or related meanings. It includes standard official terms, common aliases, abbreviations, regional expressions and multilingual variants. Not all entries are strictly equivalent; some are just conceptually close or commonly used alternatives in different scenarios. # Important Principles ⭐ **Modification Restriction (Mandatory)**: Do not modify any files under `skill-terminology`. This skill is maintained via `openclaw skills update skill-terminology`; any alterations will disrupt its normal operation. --- # terminology:uml related See `references/uml.md` --- # More skills: https://github.com/lentiancn/skills


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wed840313/wenwei-context-build (version 1.0.0)

--- name: wenwei-context-build description: 从指定入口文件出发，递归追踪代码调用链，梳理模块的技术实现细节（代码结构、依赖关系、接口定义、核心逻辑），生成结构化的代码知识库文档供 AI 阅读。当用户要求梳理功能点、分析代码结构、生成代码上下文文档、追踪调用链、理解模块实现细节、或需要从入口文件开始递归分析代码依赖关系时使用此技能。适用于前端（组件树、Hooks、Store、API 模块）和后端（Controller-Service-Repository 分层架构）代码分析场景。 --- # Role: 代码知识库生成器 **目标**：从入口文件梳理**技术实现细节**（代码结构、调用链、类型），生成供AI阅读的上下文文档。拒绝业务描述，聚焦“代码实现”。 ## 核心流程 1. **识别元数据**： * 分析入口文件：类型（前端/后端/全栈）、路径、技术栈（框架/库）、导出/导入依赖。 2. **递归追踪**： * **前端**：子组件 > Hooks > API模块 > Store > 路由。 * **后端**：Controller > Service > Repository > Entity/DTO > 配置。 3. **跨端关联检查 (关键)**： * 尝试匹配 `API调用` <-> `后端接口`。 * **异常中断逻辑**：若发现关联代码不在当前空间，**立即暂停**并询问用户。用户确认后，在文档中统一标记为 `[⚠️ 关联代码不在当前工作空间]` 并继续。 4. **结构化提取**： * **通用**：签名、类/接口定义、核心逻辑片段（<20行，用 `//...` 缩略）、调用关系链。 * **特有**：FE（Props/State/生命周期）；BE（路由/SQL/事务）。 ## 输出原则 * **定位精确**：必须包含 `文件路径` + `函数名/行号`。 * **代码规范**：核心逻辑必须附带代码块，禁止臆造缺失代码。 * **格式**：默认对话输出，仅当要求时生成 `.md`。 ## 输出模板 (动态适配) 请严格按照以下 Markdown 结构输出： ```markdown # [模块名称] 代码知识库 ## 1. 模块元信息 | 属性 | 内容 | |---|---| | 入口文件 | `路径` | | 类型/栈 | [前端/后端/混合] - [技术栈详情] | | 职责 | [简述] | ### 文件清单 * 列出关键文件路径及其职责（Controller, Service, Vue组件, Store等）。 ## 2. 代码结构 * **层级/架构**：使用缩进文本展示组件树或分层调用关系。 * **核心类型**：提取关键 Interface/Entity/DTO 定义代码。 ## 3. 关键实现 (核心) ### [功能点名称] * **位置**：`文件路径` > `方法名` (Line X-Y) * **调用链**：`A(args) -> B(args) -> C [⚠️ 若缺失则标记]` * **核心代码**： ```[lang] // 聚焦核心逻辑，省略样板代码 ``` ## 4. 上下文详情 (根据类型选择) * **若是前端**： * **API清单**：| 方法 | 路径 | 参数 | 响应 | 后端实现状态 | * **状态管理**：Store 核心 State/Action 定义。 * **若是后端**： * **接口定义**：| 方法 | 路由 | DTO | 响应 | 前端调用状态 | * **数据库**：表结构(SQL)、关键查询(Mapper/Repo)。 * **若是全栈**： * **接口映射表**：| 接口名 | 前端调用位置 | 后端实现位置 | ## 5. 依赖与备注 * **文件依赖树**：展示 import 关系。 * **开发备注**：特殊Hack、技术债务、待确认项（标记 `[待确认]`）。 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: codenova58/waste-recycling (version 0.1.0)

--- name: "Waste Recycling" description: "提供废旧家电、纸箱塑料上门回收预约及积分兑换指引。" --- # 垃圾回收 ## 筛选项 - 类别/领域/功能（核心/进阶） - 场景/适用时段/设备要求（移动端/网页版/跨端） - 状态（实时/离线/授权/VIP特权/免费） ## 返回字段 - 操作摘要/交互流程/关键入口 - 功能亮点/提效工具/隐藏玩法/避坑指南 - 状态追踪/数据同步/消息提醒/安全策略 ## 示例请求 - “如何快速使用 垃圾回收 处理 [具体场景任务] ？” - “垃圾回收 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合产品迭代与社区反馈进行内容更新 ## 注意事项 - 保护个人隐私，谨慎进行敏感授权 - 部分功能涉及第三方接口，稳定性受合作方波动影响


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: icyfrosty/slack-hub-skill (version 0.1.0)

# Slack Hub Skill Professional Slack integration for OpenClaw. Supports messaging, threading, and workspace search. ## Configuration Requires a Slack Bot Token (`xoxb-...`) in your `.env` as `SLACK_BOT_TOKEN`. ## Tools ### slack_send Send a message to a channel or user. - `target`: Channel ID or name (e.g., "#general"). - `message`: Text content. - `thread_ts`: (Optional) Timestamp for replying to a thread. ### slack_search Search the workspace for messages or files. - `query`: The search term. ### slack_list_channels List all public channels in the workspace. ## Implementation Notes - Uses `https://slack.com/api/chat.postMessage` - Uses `https://slack.com/api/search.messages` - Implements rate-limit handling for high-volume workspaces.


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

--- name: link description: > The Sovereign Inter-Agent Connection Protocol (SICP). The foundational layer for agentic networking, data-routing, and multi-entity synchronization in decentralized ecosystems. --- # LINK: The Architecture of Connectivity ## I. The Synaptic Economy In the 2026 agentic landscape, value is created through connection. **Link** is the protocol that defines how autonomous entities discover, authenticate, and bind to one another to form larger cognitive swarms. ## II. Topology Modules ```LINK_TOPOLOGY = { "synapse_routing": "Low-latency intent routing across distributed agent networks", "auth_handshake": "Zero-knowledge authentication protocols for high-security linking", "relay_protocol": "Optimizing data throughput between biological and synthetic nodes", "mesh_governance": "Autonomous management of swarm connectivity and node health" }``` ## III. Use Cases - **Discovery**: How an AGIstack agent finds a specialized Bio-Agent. - **Synchronization**: Real-time state alignment across 10,000+ distributed nodes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 2907555270/project-guide (version 1.0.1)

--- name: "project-guide" description: "引导用户填写项目开发指导模板。激活条件：用户想要开发新项目、表达项目需求、创建应用。" --- # 项目开发指导助手 ## 概述 这个 Skill 用于引导和帮助用户完成项目开发指导模板的填写。当用户想要开发新项目或对现有项目进行重大修改时，使用此 Skill 来收集必要的信息。 ## 触发条件 在以下情况下应该激活此 Skill： 1. 用户表达想要开发一个新项目 2. 用户提到"帮我整理一下项目需求" 3. 用户想要使用 AI 开发应用但不知道如何描述需求 4. 用户说"我要做一个 XX 系统" 5. 用户询问如何开始一个项目开发 ## 工作流程 ### 阶段一：项目概览采集 **目标：** 收集项目的基本信息 需要询问用户以下问题： 1. **项目名称** - "你希望给项目起什么名字？" - 或者 "这个项目叫什么？" 2. **项目类型** - "这是一个什么类型的项目？Web应用、移动端、API服务还是桌面工具？" 3. **项目简介** - "能用一两句话描述一下这个项目吗？它是做什么的？" - "这个项目要解决什么问题？" 4. **核心目标** - "你做这个项目最主要想要达成什么目标？" - "除了主要目标，还有其他期望吗？" ### 阶段二：详细需求采集 **目标：** 收集详细的功能和业务需求 需要引导用户填写 `requirements.md`，询问： 1. **用户群体** - "谁会使用这个产品？" - "他们有什么特点和需求？" 2. **核心功能** - "这个产品最核心的功能是什么？" - "用户最常用的功能有哪些？" - "有没有必须有的功能（P0），重要但不是必须的功能（P1），以及可选功能（P2）？" 3. **业务流程** - "能描述一下典型的使用流程吗？" - "比如用户进来后第一步做什么，然后呢？" 4. **数据需求** - "需要存储什么类型的数据？" - "有没有用户账号体系？需要哪些信息？" 5. **非功能需求** - "对性能有要求吗？比如响应时间、并发数？" - "有安全或合规方面的要求吗？" - "需要支持哪些平台或浏览器？" ### 阶段三：技术约束采集 **目标：** 了解技术方面的限制和要求 1. **技术偏好** - "有没有偏好的技术栈？比如必须用某种语言或框架？" - "团队对哪些技术比较熟悉？" 2. **已有资源** - "有没有已经可以复用的组件或服务？" - "需要集成哪些第三方服务？" 3. **约束条件** - "预算或时间有限制吗？" - "有没有必须兼容的现有系统？" ### 阶段四：生成文档 **目标：** 将收集到的信息整理成完整的模板文档 1. 根据收集的信息，生成/更新 `main.md` 2. 根据收集的信息，生成/更新 `requirements.md` 3. 如果用户已确定技术选型，生成 `architecture.md` 4. 如果涉及接口，生成 `api.md` ## 输出格式 完成信息收集后，应该： 1. **总结确认** - 向用户确认理解是否正确 - 询问是否有遗漏或需要补充的地方 2. **提供下一步建议** - "根据你提供的信息，我已经整理出项目模板。" - "接下来你可以：" - "1. 查看并完善 templates 目录下的模板文件" - "2. 确认无误后告诉 AI '请从 main.md 开始阅读'" - "3. AI 将根据这些信息开始项目开发" ## 模板文件位置 项目模板位于 `templates/` 目录下： - `templates/main.md` - 项目概览和索引 - `templates/requirements.md` - 详细需求文档 - `templates/architecture.md` - 技术架构设计 - `templates/api.md` - API 接口设计 - `templates/update.md` - 需求变更记录 ## 注意事项 1. **循序渐进**：不要一次性问所有问题，而是根据用户的回答逐步深入 2. **解释原因**：适时解释为什么需要这些信息，帮助用户理解 3. **提供示例**：如果用户不知道如何描述，可以提供示例 4. **记录不确定**：对于用户不确定的地方，先记录下来，后续再确认 5. **保持简洁**：问题要简洁明了，避免过度技术化的术语


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mikaelaldy/x-reply-growth (version 1.0.0)

--- name: x-reply-growth description: Grow on X with strategic replies. Use when choosing accounts to reply to, writing high-signal replies, or turning replies into profile clicks and follows. --- # X Reply Growth Replies are not filler. They are distribution probes into the right audience graph. ## Pick Targets Prioritize accounts that: - share your target audience - post often - get real replies - are not so huge that every reply disappears - sometimes engage with thoughtful replies Good range: roughly 5k-200k followers, but quality of audience matters more than size. ## Reply Types ### Add Evidence ```text I saw the same thing with [specific example]. The surprising part was [lesson]. ``` ### Add A Framework ```text I’d split this into three cases: 1. ... 2. ... 3. ... Most people overfocus on [case]. ``` ### Add A Counterexample ```text Mostly agree. The exception is [scenario]. In that case, [different principle] matters more. ``` ### Ask A Useful Question ```text How would you handle this when [real constraint]? ``` ## Rules - Do not write “great post” unless the rest adds value. - Do not hijack threads with self-promo. - Do not summarize the original post back to the author. - Keep it useful without requiring a profile click. - Make one clear contribution. ## Daily Routine - 10 thoughtful replies under target accounts. - 5 replies to peers. - 3 follow-up replies under your own posts. - 1 quote post only if you add a stronger frame. ## Output Return: - **Target accounts to look for** - **Reply angle** - **3 reply drafts** - **Profile-click bridge** - **Follow-up post if it performs** ## Operating Principle Treat the public X algorithm as a signal map, not a cheat code. Optimize for posts and replies that earn useful positive actions (dwell, replies, reposts, shares, profile clicks, follows) while avoiding negative actions (not interested, mute, block, report, fast skips).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marcosathanasoulis/openclaw-web-automation (version 1.0.7)

--- name: openclaw-web-automation-basic description: Use this skill when the user wants quick, no-credential browser automation against public websites (summaries, keyword checks, simple page facts). Runs the local OpenClaw Automation Kit query path and returns structured results. --- # OpenClaw Web Automation (No Credentials) This skill is the fastest path for public-site automation tasks. Use this for requests like: - "Summarize https://www.yahoo.com" - "Check whether https://example.com mentions pricing" - "Count mentions of 'news' on a public page" Do not use this skill for login-required tasks. Use award/login flows instead. ## Preconditions - Repository is available locally. - Python environment is installed (`pip install -e .`). - No credentials are required for this skill. ## Command to run From repo root: ```bash python skills/openclaw-web-automation-basic/scripts/run_query.py --query "<user request>" ``` The script routes to `examples/public_page_check` and returns JSON. ## Output handling - Return a concise summary to the user: - page title - keyword - keyword count - 1-3 highlights - If the query has no URL, default to `https://www.yahoo.com`. - If parsing fails, ask the user for a URL and what text to check. ## Safety - Public websites only. - No credential collection. - No anti-bot bypass guidance.

path: schemas/input.json
content: {
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "required": ["query"],
  "additionalProperties": false,
  "properties": {
    "query": {
      "type": "string",
      "description": "Natural-language request, e.g. 'check yahoo.com for the word finance'"
    }
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ggg223399/byreal-cli (version 0.2.7)

--- name: byreal-cli description: "Byreal DEX (Solana) data & analytics CLI: query pools, tokens, TVL, APR, k-line charts, farmer rankings, launchpad projects, and DeFi position stats. Use when user mentions Byreal, LP analytics, liquidity data, pool stats, or Solana DEX queries." metadata: openclaw: homepage: https://github.com/byreal-git/byreal-cli requires: bins: - byreal-cli install: - kind: node package: "@byreal-io/byreal-cli" global: true --- # Byreal DEX Analytics ## Get Full Documentation Always run these commands first to get complete, up-to-date documentation: ```bash # Complete documentation (commands, parameters, workflows, constraints) byreal-cli skill # Structured capability discovery (all capabilities with params) byreal-cli catalog list # Detailed parameter info for a specific capability byreal-cli catalog show <capability-id> ``` ## Installation ```bash # Check if already installed which byreal-cli && byreal-cli --version # Install npm install -g @byreal-io/byreal-cli ``` ## Check for Updates ```bash byreal-cli update check ``` If an update is available: ```bash byreal-cli update install ``` ## Credentials & Permissions - Most commands are **read-only** and require no wallet - Write commands require wallet setup via `byreal-cli setup` (interactive, handled by the CLI itself) - AI agents should **never** ask users to paste private keys in chat; always direct them to `byreal-cli setup` ## Hard Constraints 1. **`-o json` only for parsing** — when showing results to the user, **omit it** and let the CLI's built-in tables/charts render directly. Never fetch JSON then re-draw charts yourself. 2. **Never truncate on-chain data** — always display the FULL string for: transaction signatures (txid), mint addresses, pool addresses, NFT addresses, wallet addresses. Never use `xxx...yyy` abbreviation. 3. **Preview first** with `--dry-run`, then `--confirm` 4. **Large amounts (>$1000)** require explicit confirmation 5. **High slippage (>200 bps)** must warn user


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: thejas775/insta-reel-extractor (version 1.0.0)

--- name: instagram-reels-monitor description: Monitor Instagram DMs for reels. Use when you need to check Instagram DMs for new unread messages containing reels, click them, extract the reel link, and append to an instagram_reels.csv file. --- # Instagram Reels Monitor This skill monitors an already open Instagram Direct Messages tab for new messages containing reels and saves them to a CSV file. ## Requirements - An active browser session with a tab open to `https://www.instagram.com/direct/inbox/`. - The user must have the OpenClaw browser relay attached to that tab. ## Workflow 1. Use the `browser` tool with `action: tabs` and `profile: chrome` to find the `targetId` for the Instagram Messages tab. 2. Use the `browser` tool with `action: act`, `kind: evaluate` to run a script that checks for new, unread messages in the DM list. 3. If an unread message is found, click the conversation to open it. 4. Once the conversation is open, use `browser` `action: act`, `kind: evaluate` to extract the `href` of any link containing `/reel/` or `/p/` (if it's a video/reel). 5. Extract the Instagram user ID of the sender. 6. Append the data to `instagram_reels.csv` in the format `userid,reel_link`. 7. Return to the main inbox view (if necessary) to check for more messages. ## Example Scripts ### Extract Reel Links from an Open Conversation ```javascript () => { const links = Array.from(document.querySelectorAll('a')); return links.filter(a => a.href.includes('/reel/')).map(a => a.href); } ``` ### List DM Conversations ```javascript () => { const buttons = Array.from(document.querySelectorAll('div[role="button"]')); return buttons.map(b => b.innerText.replace(/\n/g, ' ')); } ```


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