OpenClaw/clawhub-security-signals huggingface dataset:
Skill: kangarooking/reshape-your-life (version 1.0.0)

--- name: reshape-your-life description: 帮助用户从NLP理解层次的顶层重新规划人生；当用户感到迷茫、深陷日复一日的执行循环、不知如何突破现状时使用 --- # 重塑你的人生 ## 任务目标 - 本 Skill 用于：帮助用户从NLP理解层次的顶层（精神/使命）重新规划人生，打破底层执行循环 - 能力包含：引导用户自上而下探索使命、身份、BVR、能力、行为、环境六个层次 - 触发条件：用户表达迷茫、方向不明、日复一日忙碌但看不到意义、想要突破现状 ## 前置准备 无需依赖和前置准备 ## 操作步骤 ### 破冰开场（技能启动时必做） 当技能被激活时，必须首先输出以下开场白： ``` 你是否经常感到： - 每天都很忙碌，但不知道自己到底在忙什么 - 明明很努力，却总感觉原地踏步 - 看着身边人不断前进，自己却找不到方向 - 做了很多选择，但事后总觉得不对 - 想要改变，却不知道从哪里开始 这些感受，不是你一个人的困惑。大多数人终其一生，都在底层的"环境"和"行为"中打转——抱怨环境、埋头苦干，却从未意识到：问题不在努力程度，而在思考层次。 你就像一个拼命奔跑的人，却跑在错误的方向上。越努力，离真正想要的人生越远。 但今天，这一切可以改变。 现在，我想邀请你做一件事：停下脚步，站在更高的地方，重新看一眼你的人生。我们将从一个全新的维度——NLP理解层次，帮你找到真正属于你的道路。 这个过程可能需要你面对一些从未想过的问题，需要你诚实地与自己对话。但我向你保证，一旦你走完这段旅程，你会看到一个全新的自己，和一条清晰的人生道路。 准备好了吗？让我们开始重塑你的人生。 ``` ### 核心引导流程（自上而下） #### 第一步：探索精神层（使命与意义） 目标：帮助用户找到与世界、他人的连接点，明确人生使命 引导问题： - "你来到这个世界是为了什么？" - "如果用一句话描述，你希望这个世界因为你的存在而有什么不同？" - "你过去做过哪些事情，让你感到特别有意义、特别有成就感？" - "如果你有足够的能力和资源，你最想为别人、为社会做什么？" 判断标准： - 用户能清晰表达出"我想为谁做什么" - 表达中包含"利他"的成分 - 用户对这个使命感到兴奋和认同 如果用户在精神层感到困惑，可跳转到身份层反向探索，稍后再回到精神层。 #### 第二步：探索身份层（我想成为谁） 目标：帮助用户明确身份定位，回答"我是谁"和"我想成为怎样的人" 引导问题： - "如果用一个标签或称呼来定义你自己，你希望是什么？" - "想象5年后，你已经成为你想成为的人，那时你如何介绍自己？" - "在你敬佩的人中，你最想成为像谁那样的人？为什么？" - "如果让你的人生角色不再由别人定义，你自己会如何定义？" 关键区分： - **角色**：别人给你的（儿子、员工、老板、父亲）→ 被动 - **身份**：你自己想成为的（创新者、领导者、赋能者）→ 主动 引导技巧： - 如果用户回答的是角色，追问："这是别人给你的标签，还是你主动选择的？" - 鼓励用户创造自己的身份标签 - 帮助用户将模糊的想法具象化 #### 第三步：匹配BVR层（信念、价值观、规条） 目标：根据身份定位，明确对应的信念、价值观和行为规条 引导逻辑： - "既然你想成为[身份]，那么你认为这个身份应该具备什么样的信念？" - "在这个身份下，什么对你最重要？（价值观）" - "为了更好地成为这个身份，你需要遵守哪些行为准则？（规条）" 示例： - 身份：创新者 - 信念：凡事都有改进空间 - 价值观：创新>稳定、长期价值>短期利益 - 规条：遇到问题先思考"如何优化"，而不是"如何忍受" #### 第四步：规划能力层（需要什么能力） 目标：根据身份和BVR，确定需要培养的核心能力 引导问题： - "要成为[身份]，你认为需要具备哪些核心能力？" - "这些能力中，你目前已经掌握了哪些？还需要补足哪些？" - "如果只能选择最关键的3种能力来优先提升，你会选择哪三个？" 输出要求： - 列出5-8个核心能力 - 标注已具备和待提升的能力 - 给出优先级排序 #### 第五步：设计行为层（具体行动） 目标：将能力转化为可执行的具体行动 引导问题： - "为了提升这些能力，你可以从哪些具体行动开始？" - "这个月你可以做的三件最重要的事情是什么？" - "如何将这些行动融入你的日常生活？" 行动设计原则： - SMART原则（具体、可衡量、可达成、相关性、有时限） - 从小步开始，避免大跃进 - 与身份和BVR保持一致 #### 第六步：配置环境层（资源与支持） 目标：创建支持目标实现的外部环境 引导问题： - "你需要什么样的环境来支持你的成长？" - "你可以接触到哪些资源（人脉、信息、工具、资金）？" - "你需要远离哪些消极的环境和影响？" - "谁可以成为你的支持者、导师或同伴？" 环境要素： - 物理环境：工作空间、生活空间 - 社交环境：朋友圈、社群 - 信息环境：接触的信息来源 - 资源环境：资金、工具、平台 ### 收尾与行动承诺 完成六层探索后，输出以下结构化总结： ``` 【你的人生蓝图】 使命层：[你的使命是什么] 身份层：我要成为[身份标签] BVR层： - 核心信念：[关键信念] - 核心价值观：[最重要的3个价值观] - 行为规条：[3-5条准则] 能力层：[核心能力清单] 行为层：[本月/本季度的关键行动] 环境层：[需要创建/远离的环境因素] 【行动承诺】 请写下你本周要做的第一件事：[用户承诺] 最后记住：这个蓝图不是静态的，它会随着你的成长而调整。重要的是，你现在有了一个清晰的起点，和一条向上的路。 ``` ## 资源索引 - 理论参考：见 [references/nlp-levels-guide.md](references/nlp-levels-guide.md)（深入了解六个层次的定义和特征） - 引导话术：见 [references/session-script.md](references/session-script.md)（各层次的详细问题和应对技巧） ## 注意事项 - 严格遵循自上而下的顺序，不要一开始就纠结于行为和能力 - 每个层次的探索都需要耐心，不要急于推进 - 如果用户在某个层次卡住，允许暂时跳过，稍后返回 - 引导过程中保持共情，避免说教 - 鼓励用户表达真实的想法，即使看似"不正确" - 整个过程可能需要多次对话完成，不必一次求全 ## 使用示例 **场景一：职场迷茫** 用户：工作5年了，每天都很忙，但不知道未来在哪里 → 启动破冰开场 → 探索身份层（是想成为管理者还是专家？）→ 匹配BVR和行动 **场景二：创业方向不明** 用户：想创业，但不知道做什么，害怕失败 → 从精神层开始（你希望通过创业为别人带来什么？）→ 明确身份和方向 **场景三：中年危机** 用户：40岁了，感觉人生一眼看到头，想要改变 → 探索身份层（你还想成为什么样的人？）→ 重新规划下半生


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shuwanito/nexus-clinical-case-gen (version 2.1.0)

--- name: nexus-clinical-case-gen description: Medical clinical case generator for education with PubMed RAG and OSCE evaluations. version: '2.1.0' metadata: openclaw: emoji: 🏥 homepage: https://github.com/Shuwanito/SkillsMP/tree/main/.claude/skills/nexus-clinical-case-gen os: - macos - linux - windows --- # Nexus Clinical Case Gen ## Capabilities - Clinical case generation - Differential diagnosis - OSCE scenario creation - PubMed RAG integration - Competency tracking ## Workflow 1. Receive task description and target context 2. Analyze using department-specific engines (healthcare) 3. Generate findings with severity classification 4. Produce improvement proposals with impact/effort scoring 5. Cross-validate with synergy departments 6. Return structured results with confidence scores ## Pricing - Per-execution: $5.00 - Outcome-based: Available for enterprise contracts - Volume discounts: 20% for 100+ executions/month ## Guidelines - All outputs include confidence scores and source citations - Cross-validation requires minimum 2 independent sources - Findings are classified: CRITICAL, HIGH, MEDIUM, LOW, INFO - Proposals include impact (1-10), effort (1-10), and priority score


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: admiralkittysdad/ops-reports (version 1.0.0)

--- name: ops-reports description: Run daily standups and generate task summaries for operations teams. version: 1.0.0 metadata: {"openclaw":{"emoji":"chart","homepage":"https://skillnexus.dev"}} --- # Ops Reports You are a reporting assistant for operations teams. Run structured standups and generate task summaries. ## Daily Standup On `standup` or `daily update`: prompt through structured flow: 1. What was completed since last standup? 2. What is planned for today? 3. Any blockers or escalations? Save entries to `~/.ops-commander/standups/YYYY-MM-DD.json`. Create directories on first use. ## Task Summary On `ops summary`: read `~/.ops-commander/tasks.json` (if exists) and report task counts by status, overdue items, and open blockers. If task file doesn't exist, inform user they can install ops-tasks for task tracking. ## Standup History On `show standups this week`: summarize the week's standup entries. ## Rules - Always read files before writing. Be direct, use tables. ## Pro Version Free edition. Ops Reports Pro ($29) adds shift handoff reports, weekly summaries, operational dashboards combining data from all ops modules, velocity tracking, and Nexus Alerts digest emails. Details at skillnexus.dev.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kaiyuelv/github-team-collaboration (version 1.0.0)

--- name: github-team-collaboration description: GitHub team collaboration toolkit for managing team workflows, code reviews, issue tracking, sprint planning, and team metrics. Supports PR automation, issue triage, milestone management, and team productivity analytics. Use when teams need to coordinate development workflows, automate code review processes, track sprint progress, or analyze team collaboration metrics on GitHub. --- # GitHub Team Collaboration A comprehensive toolkit for managing GitHub team workflows, code reviews, and project coordination. ## Features - **Pull Request Automation**: Auto-assign reviewers, check PR status, merge strategies - **Issue Management**: Triage, label, assign, and track issues - **Sprint Planning**: Milestone management, burndown charts, velocity tracking - **Team Metrics**: PR review time, issue resolution time, contributor stats - **Workflow Automation**: Branch protection, status checks, release management ## Usage ### Manage Pull Requests ```python from scripts.github_team import list_open_prs, assign_reviewers # List open PRs prs = list_open_prs("myorg", "myrepo") # Auto-assign reviewers assign_reviewers("myorg", "myrepo", 123, ["alice", "bob"]) ``` ### Track Sprint Progress ```python from scripts.github_team import get_milestone_progress # Get sprint progress progress = get_milestone_progress("myorg", "myrepo", "Sprint-15") print(f"Closed: {progress['closed_issues']}/{progress['total_issues']}") ``` ### Team Metrics ```python from scripts.github_team import get_team_metrics # Analyze team metrics metrics = get_team_metrics("myorg", "myrepo", days=30) print(f"Avg review time: {metrics['avg_review_time']} hours") ``` ## GitHub API Authentication Set your GitHub token as an environment variable: ```bash export GITHUB_TOKEN="ghp_your_token_here" ``` ## Supported Operations - Repository management - Pull request lifecycle - Issue tracking and triage - Milestone and project management - Team member activity - Release management - Webhook configuration

path: requirements.txt
content: requests>=2.28.0
python-dateutil>=2.8.0


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xueyetianya/regexpal (version 3.0.0)

--- name: RegexPal description: "Test and debug regex patterns against sample text. Use when checking match groups, validating patterns, generating replacements, linting syntax." version: "3.0.0" author: "BytesAgain" homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills tags: ["regex","regular-expression","pattern","matcher","tester","developer","text"] categories: ["Developer Tools", "Utility"] --- # RegexPal A real regex tester and toolkit for the terminal. Test patterns against text, find matches in files with highlighted output, perform replacements, extract capturing groups, and get human-readable explanations of regex syntax. ## Commands | Command | Description | |---------|-------------| | `regexpal test <pattern> <text>` | Test if a regex matches text — reports full match, partial match, groups, and named groups | | `regexpal match <pattern> <file>` | Find all matches in a file — highlights matches in red, shows line numbers and match count | | `regexpal replace <pattern> <replacement> <file>` | Replace all matches in a file and output to stdout. Supports backreferences (`\1`, `\2`) | | `regexpal extract <pattern> <file>` | Extract capturing groups from all matches in a file — shows each group value per match | | `regexpal explain <pattern>` | Break down a regex pattern — lists character classes, groups, tokens, and quantifiers | ## Requirements - `python3` (uses `re` stdlib module) ## Examples ```bash # Test a pattern regexpal test '^\d{3}-\d{4}$' '123-4567' # Find emails in a file regexpal match '\w+@[\w.-]+' contacts.txt # Replace version numbers regexpal replace 'v(\d+)\.(\d+)' 'v\1.$((\\2+1))' changelog.md # Extract domain parts from emails regexpal extract '(\w+)@(\w+\.\w+)' emails.txt # Understand a complex pattern regexpal explain '(?<=@)[\w.-]+' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: devang668/travily (version 1.0.1)

--- name: tavily-search description: "使用 Tavily AI 进行网络搜索、深度研究、内容提取。支持 search、research、extract 三个功能。" --- # Tavily Search Skill 使用 Tavily 的 LLM 优化 API 进行网络搜索、深度研究和内容提取。 ## 配置 API Key 保存在 `.env` 文件中（与 SKILL.md 同目录）: ``` TAVILY_API_KEY=tvly-your-key-here ``` 获取免费 API Key: [https://tavily.com](https://tavily.com) ## 三个功能 ### 1. Search - 网络搜索 快速搜索，返回相关结果。 ```bash python scripts/tavily_client.py "搜索内容" ``` **参数:** | 参数 | 简写 | 默认 | 说明 | |------|------|------|------| | `--max-results` | `-n` | 10 | 结果数量 (1-20) | | `--search-depth` | `-d` | advanced | 搜索深度: ultra-fast, fast, basic, advanced | | `--time-range` | `-t` | null | 时间: day, week, month, year | | `--json` | `-j` | false | 输出 JSON | ### 2. Research - 深度研究 对主题进行深入研究，返回完整答案和引用来源。（耗时 30-120 秒） ```bash python scripts/tavily_research.py "量子计算发展趋势" ``` **参数:** | 参数 | 简写 | 默认 | 说明 | |------|------|------|------| | `--model` | `-m` | mini | 模型: mini, pro, auto | | `--max-results` | `-n` | null | 最大来源数 | | `--output` | `-o` | null | 保存到文件 | | `--json` | `-j` | false | 输出 JSON | ### 3. Extract - URL 内容提取 从指定 URL 提取干净的内容。 ```bash python scripts/tavily_extract.py "https://example.com" ``` **参数:** | 参数 | 简写 | 默认 | 说明 | |------|------|------|------| | `--extract-depth` | `-d` | basic | 深度: basic, deep | | `--output` | `-o` | null | 保存到文件 | | `--json` | `-j` | false | 输出 JSON | ## 示例 ```bash # 搜索 python scripts/tavily_client.py "AI 新闻" --max-results 5 --time-range week # 研究（需等待） python scripts/tavily_research.py "Python vs JavaScript" --model pro --output report.md # 提取 python scripts/tavily_extract.py "https://github.com" --output content.md ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nansen-devops/nansen-defi-positions (version 0.1.0)

--- name: nansen-defi-positions description: "What DeFi positions does a wallet hold? Protocol-by-protocol breakdown of assets, debts, and rewards across chains." metadata: openclaw: requires: env: - NANSEN_API_KEY bins: - nansen primaryEnv: NANSEN_API_KEY install: - kind: node package: nansen-cli bins: [nansen] allowed-tools: Bash(nansen:*) --- # DeFi Exposure **Answers:** "What DeFi positions does this wallet have across protocols?" ```bash ADDR=<address> nansen research portfolio defi --wallet $ADDR # → protocol_name, chain, total_value_usd, total_assets_usd, total_debts_usd, total_rewards_usd, tokens nansen research profiler balance --address $ADDR --chain ethereum # → token_symbol, token_name, token_amount, value_usd per holding nansen research profiler balance --address $ADDR --chain base ``` Combine DeFi positions (lending, LPs, staking) with spot balances for a complete picture of on-chain exposure. Note: portfolio defi may return empty for wallets with no tracked DeFi positions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shmiss/agent-skills-collector (version 1.0.0)

--- name: agent-skills-collector description: 根据Agent业务类型，从SkillHub (skillhub.tencent.com) 实时搜索收集相关的AI Skills并导出为Excel清单。支持交互式选择数据源。 --- # Agent Skills 收集 根据Agent的业务类型，实时从 SkillHub 搜索收集相关Skills。 ## 前置要求 ```bash # 安装 skillhub CLI npm install -g skillhub # 安装 Python 依赖 pip3 install openpyxl requests ``` ## 快速开始 ### 方式1：直接搜索 ```bash skillhub search "hr" --json --search-limit 30 skillhub search "feishu" --json --search-limit 30 ``` ### 方式2：使用脚本 ```bash python3 scripts/collect_agent_skills.py <agent类型> --keywords "关键词1,关键词2" -o <输出路径> # 示例 python3 scripts/collect_agent_skills.py hr --keywords "hr,招聘,考勤" -o hr_skills.xlsx python3 scripts/collect_agent_skills.py it --keywords "k8s,docker,运维" -o it_skills.xlsx ``` ## 支持的Agent类型和关键词 | 类型 | 关键词 | |------|--------| | hr | hr, resume, recruit, employee, payroll, 招聘, 考勤, 绩效 | | sales | crm, sales, customer, lead, 销售, 客户, 商机 | | customer_service | support, ticket, service, 客服, 工单 | | data_analysis | sql, database, analytics, bi, data, 分析 | | finance | finance, invoice, tax, 报销, 发票, 税务 | | it | cloud, k8s, docker, monitoring, security, 运维 | | marketing | marketing, seo, ads, 营销, 推广 | | legal | legal, contract, 合规, 合同 | | content | writing, content, 写作, 文案 | ## 输出格式 Excel包含以下列：名称、关联度、类别、描述、安装命令、标签 ## 注意事项 - 每次搜索后等待3-5秒，避免API限流 - 优先使用summary字段（中文描述） - 根据slug去重

path: requirements.txt
content: openpyxl
requests

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kirkraman/kirk-brainstorming (version 1.0.0)

--- name: brainstorming description: "You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation." --- # Brainstorming Ideas Into Designs ## Overview Help turn ideas into fully formed designs and specs through natural collaborative dialogue. Start by understanding the current project context, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design in small sections (200-300 words), checking after each section whether it looks right so far. ## The Process **Understanding the idea:** - Check out the current project state first (files, docs, recent commits) - Ask questions one at a time to refine the idea - Prefer multiple choice questions when possible, but open-ended is fine too - Only one question per message - if a topic needs more exploration, break it into multiple questions - Focus on understanding: purpose, constraints, success criteria **Exploring approaches:** - Propose 2-3 different approaches with trade-offs - Present options conversationally with your recommendation and reasoning - Lead with your recommended option and explain why **Presenting the design:** - Once you believe you understand what you're building, present the design - Break it into sections of 200-300 words - Ask after each section whether it looks right so far - Cover: architecture, components, data flow, error handling, testing - Be ready to go back and clarify if something doesn't make sense ## After the Design **Documentation:** - Write the validated design to `docs/plans/YYYY-MM-DD-<topic>-design.md` - Use elements-of-style:writing-clearly-and-concisely skill if available - Commit the design document to git **Implementation (if continuing):** - Ask: "Ready to set up for implementation?" - Use superpowers:using-git-worktrees to create isolated workspace - Use superpowers:writing-plans to create detailed implementation plan ## Key Principles - **One question at a time** - Don't overwhelm with multiple questions - **Multiple choice preferred** - Easier to answer than open-ended when possible - **YAGNI ruthlessly** - Remove unnecessary features from all designs - **Explore alternatives** - Always propose 2-3 approaches before settling - **Incremental validation** - Present design in sections, validate each - **Be flexible** - Go back and clarify when something doesn't make sense


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zachysun/langgraph-for-agents (version 1.0.4)

--- name: langgraph-for-agents description: Use LangGraph/LangChain to build agents --- # LangGraph for Agents ## When to use - Use this skill when the user asks to build agents or multi-agent systems using LangGraph/LangChain. ## How to refer ### Integrated Reference Examples Read the examples in `./references/` to understand common patterns. Start with `./references/README.md` for an overview, then read the target file, it will show more details. ### External Resources [Search] If the "search" tool is available, you can refine the query keywords and execute the search. [Browse] If the "browse" tool is available, you can visit the following three websites: - LangGraph Official GitHub Repository (https://github.com/langchain-ai/langgraph) - LangGraph Official Documentation (https://docs.langchain.com/oss/python/langgraph/overview) - LangChain Official Documentation (https://docs.langchain.com/oss/python/langchain/overview) [Fetch] If the "fetch" tool is available, you can retrieve content from the following URL: - Context-7 LangGraph (https://context7.com/websites/langchain_oss_python_langgraph/llms.txt?tokens=10000) You may adjust the number of tokens by modifying the `tokens` parameter in the URL. The default value is 10,000. ## Project Structure For demos or tests, use a single .py file. For production-grade applications, use: ``` ├── app/ │ ├── api/ # API endpoints │ ├── backend/ # LangGraph/LangChain logic │ └── frontend/ # User interface ├── .env.example ├── requirements.txt └── README.md ``` ## Process for Agent System Design ### Step 1: Determine System Level - Single-Agent System: Focus on the internal structure of one agent. - Multi-Agent System: Focus on collaboration and communication between multiple agents. ### Step 2: Choose Framework - LangGraph: Best for stateful, complex workflows. - LangChain: Best for standard agent patterns based on tool calling. ### Step 3: Design Specific Implementation #### For Single-Agent Systems: - With LangGraph: Build a workflow with several nodes, or implement a ReAct Agent with manual tool_node. - With LangChain: Build a ReAct Agent by `create_agent` API. #### For Multi-Agent Systems: - With LangGraph: - Option 1: Treat each node as an independent agent, connecting them via the Graph API. - Option 2: Encapsulate a multi-node workflow as a single agent, calling other agents as tools. - With LangChain: - Create a main ReAct Agent and encapsulate other agents as tools for collaboration. ## Build Philosophy - Prefer Native: Check if a tool or integration already exists in LangChain before custom building. - Single File First: Keep core logic in one file initially to simplify debugging. - Clean Code: Provide only essential comments and use clear, descriptive variable names. - Real Data: Use actual API URLs and schemas whenever possible.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jinwangmok/text-search (version 1.0.0)

--- name: text-search version: 1.0.0 description: >- Search for a text pattern in files within a directory. Shows matching lines with filenames and line numbers. Use when the user wants to find where a word or phrase appears in their files. allowed-tools: Bash(grep:*) metadata: >- {"openclaw":{"emoji":"🔍","requires":{"bins":["grep"],"env":[]},"install":[]}} --- # Text Search Find a word or phrase in files within a directory with a single command. ## Usage ```bash grep -rn <PATTERN> <DIR_PATH> ``` **Arguments:** | # | Name | Description | |---|------|-------------| | 1 | PATTERN | Text or pattern to search for | | 2 | DIR_PATH | Directory to search in | ## Example ```bash grep -rn "TODO" /home/user/project ``` **Output:** ``` /home/user/project/main.py:12:# TODO: fix this /home/user/project/utils.py:5:# TODO: add tests ``` Format: `FILENAME:LINE_NUMBER:MATCHING_LINE` ## Success / Failure - **Success**: Matching lines printed (exit code 0) - **No matches**: No output (exit code 1 — this is normal, not an error) - **Failure**: Error message (exit code 2, e.g. directory not found)

path: MANUAL.md
content: # Text Search / 텍스트 검색

## What it does / 기능

Searches for a word or phrase in all files within a directory, showing matching lines with file names and line numbers.

디렉토리 내 모든 파일에서 단어나 문구를 검색하여, 파일명과 줄 번호와 함께 일치하는 줄을 보여줍니다.

## How to use / 사용법

```bash
bash run.sh <PATTERN> [DIRECTORY]
```

| Argument | Required | Description |
|----------|----------|-------------|
| PATTERN | Yes / 필수 | Text to search for / 검색할 텍스트 |
| DIRECTORY | No / 선택 | Where to search (default: current dir) / 검색 위치 (기본: 현재 디렉토리) |

## Example / 예시

```bash
bash run.sh "TODO" /home/user/project
```

Output / 출력:
```
Searching for 'TODO' in /home/user/project:
---
/home/user/project/main.py:12:# TODO: fix this
/home/user/project/utils.py:5:# TODO: add tests
```

Format / 형식: `FILENAME:LINE_NUMBER:MATCHING_LINE` (`파일명:줄번호:일치하는줄`)

## Troubleshooting / 문제 해결

- **"No matches found"** — The pattern was not found, this is normal / 패턴이 없으면 정상입니다
- **"Directory not found"** — Check the path / 경로를 확인하세요
- Search is case-sensitive / 대소문자를 구분합니다


path: run.sh
content: #!/bin/bash
# text-search: Search for text pattern in files within a directory
# Arguments: PATTERN [DIRECTORY] (1 required, 1 optional; 2 args max)
# Usage: bash run.sh <PATTERN> [DIRECTORY]

set -euo pipefail

if [ $# -lt 1 ] || [ $# -gt 2 ]; then
  echo "ERROR: Expected 1-2 arguments: <PATTERN> [DIRECTORY]"
  echo "Usage: bash run.sh <PATTERN> [DIRECTORY]"
  exit 1
fi

PATTERN="$1"
DIR_PATH="${2:-.}"

if [ ! -d "$DIR_PATH" ]; then
  echo "ERROR: Directory not found: $DIR_PATH"
  exit 1
fi

echo "Searching for '$PATTERN' in $DIR_PATH:"
echo "---"
grep -rn "$PATTERN" "$DIR_PATH" 2>/dev/null || echo "No matches found."


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

--- name: getpost-search description: "Web search API for AI agents. Get search results with titles and snippets." version: "1.0.0" --- # GetPost Search API Web search for AI agents. Get search results with titles, URLs, and snippets. ## Quick Start ```bash # Sign up (no verification needed) curl -X POST https://getpost.dev/api/auth/signup \ -H "Content-Type: application/json" \ -d '{"name": "YOUR_NAME", "bio": "What your agent does"}' # Save the api_key from the response ``` ## Base URL `https://getpost.dev/api` ## Authentication ``` [REDACTED_SECRET] ``` ## Search the Web ```bash curl -X POST https://getpost.dev/api/search \ -H "[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{"query": "best python web frameworks 2026", "num_results": 5}' ``` Returns position, title, url, and snippet for each result. Cost: 3 credits per search. ## Full Docs https://getpost.dev/docs/api-reference#search


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