OpenClaw/clawhub-security-signals huggingface dataset:
Skill: phy041/phy-investor-wechat-update (version 1.0.3)

--- name: investor-wechat-update description: 为早期创业者（天使/种子轮）撰写给投资人的微信更新。支持三种场景：常规进展同步、好消息分享、遇到困难求助。输出简洁、可直接复制粘贴到微信。Triggers on "投资人更新"、"给投资人发微信"、"月度更新"、"investor update"、"好消息分享"、"向投资人求助"、"请投资人帮忙引荐"。 homepage: https://canlah.ai --- # 投资人微信更新 Skill 为天使/种子轮创业者生成专业的投资人微信更新消息。 ## 方法论来源 - Y Combinator Aaron Harris 的 Investor Update Template - Visible.vc 的 Monthly Investor Update Best Practices - Uncork Capital 的 Early Stage Company Update Guide - 投中网、知乎等中文创投社区的实践总结 ## 核心原则 ### 1. 极简主义 - 微信不是邮件，150-300字足矣 - 可分成2-3条消息发送 - 每条消息只说一件事 ### 2. 数据说话 - 用具体数字，不用形容词 - ✅ "本月收入42万，MoM +15%" - ❌ "收入增长还不错" ### 3. 清晰的 Ask - 不要说"有空聊聊" - ✅ "能否介绍 [具体公司/人名]？" - ❌ "有资源帮我们对接一下吗？" ### 4. 诚实透明 - 好消息坏消息都说 - 投资人能处理坏消息，不能处理惊喜 - 早说问题 = 早获得帮助 ## 三种场景模板 --- ### 场景 A：常规进展同步（月度/季度） **触发词**: "月度更新"、"季度同步"、"常规汇报" **结构**: ``` [称呼]，[产品名] [月份]更新： 📊 核心数据 • 收入：XX万（vs 上月 XX万，+XX%） • 客户：XX 家（新增 X 家） • 现金：XX万，runway X个月 ✅ 本月进展 • [里程碑1] • [里程碑2] ⚠️ 挑战 • [问题1 + 当前应对] 🙏 需要帮助 • [具体 ask，越具体越好] 下月重点：[一句话] ``` **字数**: 150-250字 **语气**: 专业、简洁、不卑不亢 --- ### 场景 B：好消息分享 **触发词**: "好消息"、"签约了"、"达成了"、"里程碑" **结构**: ``` [称呼]，分享个好消息！ [一句话说明好消息是什么] [2-3句背景/意义] 感谢一直以来的支持🙏 ``` **字数**: 50-100字 **语气**: 开心但克制，不夸张 **注意**: - 不要过度渲染 - 说明这对公司意味着什么 - 如果投资人曾帮助达成此事，特别感谢 --- ### 场景 C：遇到困难求助 **触发词**: "求助"、"需要帮忙"、"遇到困难"、"卡住了" **结构**: ``` [称呼]，想请教个事： [问题是什么 - 一句话] 背景： • [关键信息1] • [关键信息2] 我们试过： • [已尝试的方案] 具体想请您帮忙： [非常具体的 ask] 方便的话随时回复，不急🙏 ``` **字数**: 100-150字 **语气**: 直接、不绕弯、尊重对方时间 **Ask 的最佳实践**: - 具体到人名/公司名："能否介绍贵司 XX 总？" - 给出背景让对方判断合适性 - 提供"逃生出口"："如果不方便完全理解" --- ## Double Opt-In 引荐请求 当需要投资人做引荐时，使用这个格式： ``` [称呼]，想请您帮忙引荐 [目标人名/公司]。 为什么找他们：[一句话原因] 可转发版本： --- [目标称呼]， 我投资的 [公司名] 希望与您聊聊 [具体话题]。 他们是 [一句话介绍]，目前 [关键数据]。 [创始人名] [一句话背景]。 方便的话，我拉个群？ --- 如果不方便引荐完全理解，感谢🙏 ``` ## 发送时机 - **常规更新**: 每月5-10号（数据已出，不要拖到月中） - **好消息**: 当天或次日（新鲜感） - **求助**: 工作日上午（响应率更高） ## 避免的坑 ❌ **不要做的事**: - 群发感明显（"各位投资人朋友"） - 只报喜不报忧 - Ask 太模糊（"有资源帮帮我们"） - 太长（超过手机一屏） - 频繁打扰（非紧急事项周末发） - 已读不回后连续追问 ✅ **要做的事**: - 称呼用名字（"X总"、"XX"） - 保持固定节奏（投资人能预期） - 坏消息配解决方案 - 感谢具体帮助过的人 ## 执行流程 1. 询问用户：哪种场景？（A/B/C） 2. 收集必要信息 3. 生成初稿 4. 确认字数 < 300 5. 检查是否有具体 Ask 6. 输出可直接复制的文本 ## 输出格式 直接输出微信消息文本，不加额外解释： - 使用 emoji 作为分隔符（适度） - 换行清晰 - 可分段发送的地方标注 [可分段] --- *方法论来源: YC, Visible.vc, Uncork Capital, 投中网* --- ## Author **[Canlah AI](https://canlah.ai)** — Run performance marketing without breaking your brand. - GitHub: [github.com/PHY041](https://github.com/PHY041) - All Skills: [clawhub.ai/PHY041](https://clawhub.ai/PHY041)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/competitor-price-analysis (version 1.0.0)

--- name: competitor-price-analysis description: "Analyze competitor pricing strategies across e-commerce platforms. Map price positions, identify pricing gaps, evaluate price elasticity signals, and develop data-driven pricing strategies." metadata: nexscope: emoji: "💲" category: ecommerce --- # Competitor Price Analysis 💲 Analyze competitor pricing strategies across e-commerce platforms. Map price positions, identify pricing gaps, evaluate price elasticity signals, and develop data-driven pricing strategies. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill competitor-price-analysis -g ``` ## Usage ``` Analyze the pricing landscape for wireless earbuds on Amazon. Price range $20-80. Who's winning at each price point and where are the gaps? ``` ## Capabilities - Price mapping across competitors (price bands, positioning) - Price gap identification (underserved price points) - Price elasticity signal analysis from review/sales patterns - Promotional pricing pattern detection - MAP/MSRP compliance monitoring framework - Dynamic pricing strategy recommendations ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minglu6/v2ex-monitor (version 1.0.0)

# V2EX Monitor Skill ## Skill 概述 这是一个适合通用 AI Agent / OpenClaw 风格接入的 skill，用于： - 监控指定 V2EX 节点的新帖子 - 拉取当前账号的提醒通知 - 生成 Markdown 总结报告 - 提供 MCP 服务，供支持 MCP 的 Agent 进一步调用 该 skill 采用“说明文档 + 可执行脚本 + 配置说明”的通用结构，便于直接集成或二次封装。 ## 目录结构 ```text skills/ ├─ SKILL.md # Skill 说明文档 ├─ run_skill.py # 推荐给 Agent 调用的统一入口 ├─ v2ex_monitor.py # 核心监控逻辑 ├─ v2ex_mcp.py # MCP 服务入口 ├─ v2ex-monitor.md # 原始功能说明 ├─ v2ex_monitor_config.example.json ├─ requirements.txt # 推荐依赖清单 ├─ v2ex_hourly_report.md # 运行后生成的报告 └─ v2ex_monitor_data/ ├─ seen_topics.json # 已处理主题记录 └─ seen_notifications.json # 已处理提醒记录 ``` ## 适用场景 当用户提出以下需求时可调用本 skill： - “帮我监控 V2EX 某几个节点的新帖子” - “每小时汇总 V2EX 的新主题和提醒” - “生成 V2EX 热门帖子摘要” - “通过 MCP 查询 V2EX 节点主题 / 主题详情 / 提醒通知” ## 输入 ### 方式 1：统一入口脚本 ```bash python skills/run_skill.py config --nodes python,linux,programmer --apikey <你的_api_key> python skills/run_skill.py run python skills/run_skill.py report ``` ### 方式 2：直接调用核心脚本 ```bash python skills/v2ex_monitor.py config --nodes python,linux,programmer --apikey <你的_api_key> python skills/v2ex_monitor.py run python skills/v2ex_monitor.py daemon --interval 1 ``` ### 方式 3：作为 MCP 服务 ```bash python skills/v2ex_mcp.py --stdio ``` ## 输出 - `skills/v2ex_hourly_report.md`：监控报告 - `skills/v2ex_monitor_data/seen_topics.json`：已处理帖子记录 - `skills/v2ex_monitor_data/seen_notifications.json`：已处理提醒记录 - MCP 调用返回的 JSON 文本结果 ## 配置说明 1. 复制示例配置： ```bash copy skills\v2ex_monitor_config.example.json skills\v2ex_monitor_config.json ``` 2. 填写你的 V2EX API Key 与监控节点。 也可以直接通过命令配置： ```bash python skills/run_skill.py config --nodes python,linux,programmer --apikey <你的_api_key> ``` ## Agent 使用建议 对于不支持 MCP 的 Agent，优先调用： ```bash python skills/run_skill.py run python skills/run_skill.py report ``` 对于支持 MCP 的 Agent，可启动： ```bash python skills/v2ex_mcp.py --stdio ``` 然后使用以下工具： - `v2ex_get_node_topics` - `v2ex_get_topic` - `v2ex_get_topic_replies` - `v2ex_get_notifications` - `v2ex_get_my_info` - `v2ex_get_node_info` - `v2ex_monitor_topics` - `v2ex_config` ## 注意事项 - 默认不再内置真实 API Key，需由使用者自行配置。 - 生成的报告与数据文件都保存在 `skills/` 目录下，便于 skill 打包。 - 如果你是以 OpenClaw 的自定义 skill 目录方式接入，通常可将整个 `skills/` 目录作为 skill 资源目录使用。 - 当前核心监控逻辑优先使用 `urllib3`，即使未安装 `requests` 也可以显示帮助并执行大部分监控流程；若安装了 `requests`，会自动启用回退能力。 ## 依赖建议 建议直接安装： ```bash pip install -r skills/requirements.txt ``` 或手动安装： ```bash pip install urllib3 mcp pydantic requests ``` ## 去重说明 - 帖子去重：基于主题 ID，已记录到 `seen_topics.json` - 提醒去重：优先使用提醒 ID；如果接口未返回 ID，则回退到提醒内容哈希，记录到 `seen_notifications.json` - 因此在单实例按小时执行时，同一帖子和同一提醒不会在后续运行中重复计入“新增”


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ec-cyber258/variant-pathogenicity-predictor (version 0.1.0)

--- name: variant-pathogenicity-predictor description: Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity version: 1.0.0 category: Bioinfo tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Variant Pathogenicity Predictor Integrate REVEL, CADD, PolyPhen and other scores to predict variant pathogenicity. ## Usage ```bash python scripts/main.py --variant "chr17:43094692:G:A" --gene "BRCA1" python scripts/main.py --vcf variants.vcf --output report.json ``` ## Parameters - `--variant`: Variant in format chr:pos:ref:alt - `--vcf`: VCF file with variants - `--gene`: Gene symbol - `--scores`: Prediction scores to use (REVEL,CADD,PolyPhen) ## Integrated Scores - REVEL (Rare Exome Variant Ensemble Learner) - CADD (Combined Annotation Dependent Depletion) - PolyPhen-2 (Polymorphism Phenotyping) - SIFT (Sorting Intolerant From Tolerant) - MutationTaster ## Output - Pathogenicity classification - ACMG guideline interpretation - Individual score breakdown - Confidence assessment ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


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

# Skill Definition: 认知教练 (Cognitive Coach) **版本**: 1.1 (新增次日早晨定时触发机制) **所属系统**: 「知我」个人知识留存与第二大脑引擎 **目标受众**: 追求高信噪比知识留存的开发者/极客 **执行引擎**: OpenClaw / AI Agent CLI ## 1. 核心定位 (Role & Objective) 你现在不仅是一个对话 AI，而是用户的“私人认知图谱教练”。你的唯一目标是**打破用户的“能力错觉”（以为AI的知识就是自己的知识）**。 你需要接收用户导出的历史对话记录（JSON格式），在后台静默完成“去噪”与“提炼”，并**默认在次日早晨 9:00 主动向用户发起**基于“费曼技巧（Feynman Technique）”的提问，最终将短期工作记忆转化为用户的长期内化知识。 ## 2. 工作流 (Execution Workflow) ### 阶段一：数据摄入与去噪 (Ingestion & Denoising) **触发条件**：用户向你发送了一份或多份历史聊天记录（通常是 JSON 文本或文件）。 **执行动作**： 1. **解析文件**：读取对话的历史上下文（Context Topology）。 2. **价值过滤（核心）**：严格区分“工具性对话”与“探索性对话”。 - 🔴 **直接丢弃（低价值/噪音）**：简单的代码纠错（如 Spring Boot 依赖冲突、空指针异常修复）、基础的正则编写、文本翻译、文档格式化、以及对话轮数少于 3 轮的单次问答。 - 🟢 **重点提取（高价值/信号）**：包含“Why（为什么）”和“How（怎么做）”的深度探讨。抽象概念、系统架构设计、底层协议、跨学科思考（例如：AgentScope 中的 A2A 通信协议机制、Apple App Store Server API 的订阅状态机设计、心理学大模型 SFT 数据集的构建逻辑等）。以及用户表现出“顿悟”（如回复“原来如此”、“我明白了”）的对话片段。 ### 阶段二：生成复习计划与设定闹钟 (Knowledge Extraction & Scheduling) **执行动作**： 1. 针对提取出的 1-2 个最高价值的知识点，在你的内部记忆中生成对应的《费曼复习卡片》。 卡片必须包含以下三个隐藏要素（此时不要输出给用户）： - `topic`: 知识点标签。 - `feynman_prompt`: 启发式提问文案。要求用户用“大白话”或“生活中的类比”来解释该技术难点。 - `reference_answer`: 标准参考答案（包含核心逻辑与防错细节，用于后续评判）。 2. **自动定时（关键动作）**：如果成功生成了复习卡片，你必须立刻调用系统的定时任务/闹钟能力，**设定一个第二天早上 9:00 的触发器**。 3. **静默回复**：完成上述操作后，仅向用户回复：“✅ 记录已摄入并完成价值提取。已为你设定明早 9:00 的费曼挑战。你也可以随时输入 `/review` 提前提取。” ### 阶段三：主动回想发起 (Proactive Trigger) **触发条件**：次日早晨 9:00 定时器触发，或用户主动输入指令如 `/review`、`开始复习`。 **执行动作**：主动向用户发送 `feynman_prompt`。 **沟通语气要求**：专业、简短、像一位富有启发性的导师，切忌生硬说教或过度热情。 - **示例输出**：“早安！昨天的记录显示我们深入探讨了『多智能体系统的通信机制』。为了确保你真正掌握了它，现在请用大白话向我解释一下：如果在 AgentScope 中，多个 Agent 需要协同完成一个任务，它们底层的 A2A 协议是如何保证消息不乱序的？试着用『公司部门开会』之类的类比来给我讲讲。” ### 阶段四：费曼评估与反馈 (Evaluation & Feedback) **触发条件**：用户回复了你的费曼提问。 **执行动作**： 1. 对比用户的回答与内部的 `reference_answer`。 2. **反馈原则**： - 如果用户解释得很清晰且类比恰当，给予肯定，并补充一个极微小的进阶细节。 - 如果用户卡壳、使用了堆砌的专业术语（说明没真懂），或者遗漏了核心逻辑，温和地指出盲区，并给出正确的白话解释引导。 ## 3. 约束与系统指令 (Strict Constraints) 1. **禁止长篇大论**：在阶段三主动提问时，字数严格控制在 150 字以内。千万不要在提问时直接把答案总结出来，必须逼迫用户自己回想。 2. **强制静默摄入**：在接收聊天记录时，绝对不允许打印冗长的分析过程或提前剧透问题。 3. **保持客观**：反馈时直击痛点，不要使用“抱抱你”、“你太棒了”等过度情绪化的词汇。 --- **[Agent 内部状态初始化完毕。等待接收用户的 JSON 对话数据...]**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nelohenriq/toon-adoption-skill (version 1.0.0)

--- name: toon-adoption description: Optimize token usage by adopting the compact TOON format for data storage and context. version: 1.0.0 author: Agent Zero Community tags: [token-optimization, toon, format, efficiency] --- # TOON Adoption Skill ## Description Efficiently parse, generate, and store data using Token-Oriented Object Notation (TOON). TOON is designed for LLMs as a lossless, drop-in representation of JSON data that reduces token usage by approximately 40% through indentation, minimal quoting, and tabular layouts. ## TOON Syntax Rules ### 1. Indentation-based Nesting - Use 2-space indentation to define hierarchy, similar to YAML. - Avoid curly braces `{}` and square brackets `[]` for nesting unless defining a tabular schema. ### 2. Minimal Quoting - Keys and values do not require quotes unless they contain commas or significant leading/trailing whitespace. ### 3. Explicit Array Lengths - Declare the number of items in an array using the `[N]` notation (e.g., `friends[3]`). ### 4. Tabular Arrays (Arrays of Objects) - For uniform arrays of objects, use the format: `key[N]{field1,field2,...}:`. - List the field names once in the header, followed by rows of values separated by commas. ### 5. Encoding - Always use UTF-8. ## Guidelines for Agent Use 1. **Storage**: Prioritize `.toon` files for logs, schedules, and long-term data tracking. 2. **Context Compression**: When summarizing history or notes, use TOON to fit more information into the context window. 3. **Parsing**: Interpret indentation as nested object levels and CSV rows as objects matching the header schema. ## Example Document ```toon metadata: name: Sample Configuration format: TOON efficiency_gain: 0.4 goals[3]{id,title,category}: 1,Lose weight,health 2,Increase self-esteem,personal 3,Get out of loneliness,social ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: super-max21/zyz-skill-100-indicators-analysis-public (version 1.0.0)

--- name: TradingView技术指标分析助手 (Public) description: 基于100个热门TradingView Pine Script指标转换的Python技术分析工具集，提供专业的技术指标计算、分析和可视化功能 skill_key: 100_indicators_analysis_public original_skill_key: 100_indicators_analysis encapsulation_target: prana --- 本技能为 Prana 封装共享包：`skill_key`（`100_indicators_analysis_public`）为公开包标识；`original_skill_key`（`100_indicators_analysis`）为 Prana 上实际调用的专业技能。 **分发体系（encapsulation_target）**：`prana`（由封装接口传入，如 prana、claw_hub 等）。 运行方式：使用 `scripts/prana_skill_client.py` 或渠道集成入口。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: jpengcheng523-netizen/jpeng-discord-bot (version 1.0.0)

--- name: jpeng-discord-bot description: "Send messages to Discord" version: "1.0.0" author: "jpeng" tags: ["chat", "discord", "communication"] --- # Discord Bot Send messages to Discord ## When to Use - User needs chat related functionality - Automating discord tasks - Communication operations ## Usage ```bash python3 scripts/discord_bot.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export DISCORD_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: perchouli/qwen-vision (version 0.1.0)

--- name: qwen-vision description: Analyze images and videos using Qwen Vision API (Alibaba Cloud DashScope). Supports image understanding, OCR, visual reasoning. homepage: https://dashscope.aliyuncs.com/ metadata: {"openclaw":{"emoji":"👁️","requires":{"bins":["python3"]}}} --- # Qwen Vision Analyze images and videos using Alibaba Cloud's Qwen Vision API (通义千问视觉模型). ## Usage Analyze an image: ```bash uv run {baseDir}/scripts/analyze_image.py --image "/path/to/image.jpg" --prompt "请描述这张图片" --api-key sk-xxx ``` With custom model: ```bash uv run {baseDir}/scripts/analyze_image.py --image "/path/to/image.jpg" --model qwen-vl-max-latest --api-key sk-xxx ``` ## API Key Get your API key from: - `models.providers.bailian.apiKey` in `~/.openclaw/openclaw.json` - Or `skills."qwen-image".apiKey` in `~/.openclaw/openclaw.json` - Or `DASHSCOPE_API_KEY` environment variable - Or https://dashscope.console.aliyun.com/ ## Models | Model | Description | |-------|-------------| | `qwen-vl-max-latest` | Latest max model (default) | | `qwen-vl-plus-latest` | Faster, cost-effective | ## Prompt Examples | Task | Prompt | |------|--------| | Describe | "请详细描述这张图片的内容" | | OCR | "提取图片中的所有文字" | | Count | "数一下图中有多少个物体" | | Analyze | "分析这张图表的数据趋势" | | Identify | "这是什么地方/物品？" | ## Notes - Supports JPG, PNG, GIF, WebP, BMP formats - Images are encoded as base64 and sent via API - Response time varies by image size and complexity

path: _meta.json
content: name: qwen-vision
version: 1.0.0
installedAt: 2026-03-23T15:43:00+08:00

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: simonjoe246/image-reader (version 1.0.0)

--- name: image-reader description: > Image recognition and understanding tool. Uses a multimodal model (e.g. doubao-seed-2.0-pro, kimi-k2.5) to analyze image content and supports OCR text extraction and image description. Use this skill when a user sends a screenshot or image and needs the text extracted or the image content understood. compatibility: requires: - Python 3 - openai>=1.0.0 - pyyaml>=6.0 --- # Image Reader Skill Image recognition and understanding tool that leverages Doubao multimodal models to analyze image content. --- ## Features - **Text Extraction (OCR)**: Extract text from images, suitable for documents, screenshots, posters, menus, etc. - **Image Description**: Generate detailed descriptions of images, suitable for photos, illustrations, memes, UI screens, etc. - **General Analysis**: Automatically choose the best analysis strategy based on the image type. --- ## API Configuration | Item | Value | |------|------| | API Endpoint | `https://ark.cn-beijing.volces.com/api/coding/v3` | | Model | `doubao-seed-2.0-pro` | | Authentication | API Key (configured in config.yaml) | --- ## Usage ### Command Line ```bash # General analysis python image_reader.py /path/to/image.png # Extract text (OCR) python image_reader.py /path/to/image.png -p "Extract all text from the image" # Describe the image python image_reader.py /path/to/image.png -p "Describe this image in detail" ``` ### OpenClaw Skill Invocation Once installed, you can invoke it using natural language: ```yaml Analyze this image Extract the text from the image Describe this screenshot ``` --- ## Output - **Text-heavy images**: Returns all extracted text, preserving original formatting. - **Non-text images**: Returns a detailed scene description, including objects, people, colors, style, etc. - **Mixed content**: Provides both text extraction and a visual description. --- ## Technical Details - Uses an OpenAI-compatible API to call Doubao multimodal models - Images are sent as base64-encoded data - The system prompt adapts to the image type to select the most appropriate analysis strategy


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: davienzomq/context-gatekeeper (version 0.1.1)

--- name: context-gatekeeper description: Keeps the conversation token-friendly by summarizing recent exchanges, surfacing pending actions, and delivering a compact briefing for each turn before calling the model. Trigger this skill whenever you need to prune a bloated thread or keep the next prompt lean. author: Davi Marques --- # Context Gatekeeper ## Objetivo Reduzir o volume de tokens enviados ao modelo preservando apenas o essencial: o resumo das decisões, os próximos passos e os trechos mais recentes da conversa. Este skill roda em paralelo à sua rotina habitual, produzindo o artefato `context/current-summary.md` que serve como contexto de substituição (em vez de reenviar toda a conversa). ## Fluxo mínimo 1. **Registre as trocas**: a cada prompt/resposta, grave uma linha formatada `ROLE: texto` em um arquivo de histórico (`context/history.txt` ou qualquer caminho acessível). Exemplo: ``` USER: Quero definir metas para o Q2 ASSISTANT: Fiz um plano com marcos e métricas ``` 2. **Execute o guardião**: ```bash python skills/context-gatekeeper/scripts/context_gatekeeper.py \ --history context/history.txt \ --summary context/current-summary.md ``` O script limita o resumo (até 6 sentenças por padrão), extrai atividades abertas (TODO, próxima ação, tarefa, follow-up) e inclui as últimas 4 jogadas para contexto imediato. 3. **Use o resumo**: antes de chamar a API (ou responder ao usuário), injete o conteúdo de `context/current-summary.md` e cite os itens pendentes. Apenas depois disso, se for necessário, adicione as últimas trocas concretas (máximo de 2-3 mensagens) para clareza imediata. 4. **Repita**: atualize `context/history.txt` com a nova resposta e execute o script novamente antes do próximo turno. ## Argumentos do script - `--history`: caminho do arquivo com o log das trocas (cada linha deve ser `ROLE: texto`). Usa STDIN se omitido. - `--summary`: destino do resumo (substitui o arquivo se já existir). - `--max-summary-sents`: limite de sentenças resumidas (padrão 6). - `--max-recent-turns`: quantas trocas finais aparecerão na seção "Últimos turnos" (padrão 4). ## Dica de operação diária - Monte um cron/loop leve que chame o script antes de cada resposta automática. - Guarde um paralelo `context/pending-tasks.md` e copie a seção "Pendências" do resumo para lá. - Sempre cite o caminho do resumo no parágrafo inicial da resposta (por exemplo: "Resumo compacto: ...") para facilitar auditoria. ## Por quê isso funciona? OpenClaw já persiste memórias em arquivos Markdown e executa `/compact` quando precisa. Este skill assume a mesma disciplina: em vez de confiar nos 100+ mensagens antigas que ainda estão no contexto, você carrega um briefing de 1 página antes de cada chamada. Economiza tokens e mantém o modelo focado no que realmente importa (decisões, pendências, mudanças recentes).


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