OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mupengi-bot/hwp-reader (version 1.0.0)

# 🐧 HWP Reader — Read & Analyze Korean HWP/HWPX Documents > Author: 무펭이 🐧 | v1.0.0 ## Description Read and extract text content from Korean HWP (한글) and HWPX files. Supports both legacy HWP format (via pyhwp) and modern HWPX format (ZIP-based XML). ## When to Use - User asks to read/analyze a .hwp or .hwpx file - Government support application forms (정부지원사업 신청서) - Any Korean document in Hangul Word Processor format ## How It Works ### HWP Files (Legacy Format) ```bash python3 -c " from hwp5.hwp5txt import main import sys sys.argv = ['hwp5txt', 'FILE_PATH'] main() " ``` ### HWPX Files (Modern XML Format) ```bash python3 -c " import zipfile z = zipfile.ZipFile('FILE_PATH') # Quick preview text if 'Preview/PrvText.txt' in z.namelist(): print(z.read('Preview/PrvText.txt').decode('utf-8')) # Full content from section XMLs import xml.etree.ElementTree as ET for name in sorted(z.namelist()): if name.startswith('Contents/section') and name.endswith('.xml'): root = ET.fromstring(z.read(name)) for elem in root.iter(): if elem.text and elem.text.strip(): print(elem.text.strip()) " ``` ## Capabilities | Feature | HWP | HWPX | |---------|-----|------| | Text extraction | ✅ pyhwp | ✅ ZIP+XML | | Table detection | ⚠️ `<표>` markers | ✅ XML tags | | Image extraction | ❌ | ✅ from BinData/ | | Metadata | ✅ via hwp5 | ✅ from version.xml | ## Dependencies - **pyhwp** (`pip install pyhwp`) — installed at `/Users/mupeng/Library/Python/3.9/lib/python/site-packages/hwp5/` - **Python 3.9+** — standard library `zipfile`, `xml.etree.ElementTree` ## Limitations - HWP text extraction loses table structure (shows `<표>` placeholder) - HWPX Preview/PrvText.txt is truncated to ~1KB; use section XMLs for full content - Complex formatting (colors, fonts, page layout) not preserved in text mode - Encrypted/password-protected HWP files not supported ## Usage Examples ### Read a government application form ``` "이 HWP 파일 읽어줘: /path/to/신청서.hwp" → Extract text → Analyze structure → Summarize sections ``` ### Compare two versions ``` "v1.hwp와 v2.hwp 차이점 분석해줘" → Extract both → Diff content → Report changes ``` ### Fill in a template ``` "이 양식에 우리 사업 내용 채워줘" → Read template → Identify blanks → Generate content suggestions ``` --- *🐧 무펭이 — Making Korean documents accessible to AI agents*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: codenamelokcon/quantum-messenger (version 1.0.2)

--- name: quantum-messenger-im description: 帮助中国电信同人及客户实现通过量子密信调用 OpenClaw 机器人。支持文本、图片、附件及状态栏反馈。 version: 1.0.2 author: 上海电信政支中心/量子能力中心 技术经理 程沛及他的openclaw机器人助手：1号机（Gemini) (18918115454，[REDACTED_SECRET]) tags: [quantum, im, chinatelecom, messaging] metadata: {"openclaw":{"requires":{"bins":["node"],"env":["QUANTUM_KEY"]},"primaryEnv":"QUANTUM_KEY"}} --- # Quantum Messenger IM Skill 本技能由上海电信政支中心/量子能力中心技术经理程沛及他的openclaw机器人助手：1号机（Gemini)共同开发。 ## 核心配置 1. 端口: 默认 9001 (需安全组放通)。 2. 机器人类型: 量子密信自定义会话机器人。 3. KEY: 填入 scripts/ 目录下脚本对应位置。 ## 开发者备注 - 文本回复: content 字段提取。 - 图片发送: type=1, imageMsg 字段。 - 附件发送: type=2, fileMsg 字段。 - 回调地址: 量子密信 APP 内机器人设置 URL。 详情请参阅 README.md。


Security scan verdicts — ClawScan: malicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector:  ()
Skill: mengbin92/ddg-search-fetch (version 1.0.1)

--- name: duckduckgo-search description: Search the web and fetch URL content using DuckDuckGo. Use when the user wants to search for information online without requiring API keys or paid services. Supports text search with results including title URL and snippet. Also supports URL fetching to extract readable content from web pages. Triggers on phrases like "search for" "look up" "find information about" "fetch url" "get page content" or when web_search is unavailable. --- # DuckDuckGo Search & Fetch Search the web and fetch URL content using DuckDuckGo (no API key required). ## Prerequisites 需要安装依赖: ```bash pip3 install duckduckgo-search ``` ## 功能 ### 1. 网页搜索 (ddg_search.py) ```bash python3 scripts/ddg_search.py "your search query" [--max-results 10] ``` ### 2. 网页抓取 (ddg_fetch.py) ```bash python3 scripts/ddg_fetch.py "https://example.com" [--timeout 30] ``` ## Usage Examples ### 搜索 ```bash # Basic search python3 scripts/ddg_search.py "OpenClaw AI agent" # Search with more results python3 scripts/ddg_search.py "Python best practices" --max-results 15 ``` ### 抓取网页 ```bash # Fetch a webpage python3 scripts/ddg_fetch.py "https://openclaw.ai" # With custom timeout python3 scripts/ddg_fetch.py "https://example.com" --timeout 15 # Plain text output python3 scripts/ddg_fetch.py "https://example.com" --format text ``` ## Output Format ### 搜索结果 (JSON) ```json { "query": "search query", "count": 10, "results": [ { "title": "Result title", "url": "https://example.com", "snippet": "Description snippet" } ] } ``` ### 抓取结果 (JSON) ```json { "url": "https://example.com", "title": "Page Title", "text": "Extracted readable content...", "description": "Meta description", "status_code": 200, "error": null } ``` ## Integration with OpenClaw Example workflow ```python # Search result = exec({ "command": "python3 /path/to/skills/duckduckgo-search/scripts/ddg_search.py query" }) # Parse: json.loads(result.stdout) # Fetch URL result = exec({ "command": "python3 /path/to/skills/duckduckgo-search/scripts/ddg_fetch.py https://example.com" }) # Parse: json.loads(result.stdout) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: andy8663/skill-manage (version 1.0.0)

--- name: skill-manage description: OpenClaw Skill management toolkit: install/uninstall/update/check/search. Trigger words: 查看技能列表/Skill管理/更新技能/卸载技能/搜索技能. Auto-detect source (GitHub/SkillHub/Config/Local), one-click upgrade. description_zh: OpenClaw Skill 管理工具集：安装/卸载/更新/检查/搜索。触发词：查看技能列表/Skill管理/更新技能/卸载技能/搜索技能。自动识别来源（GitHub/SkillHub/Config/Local），一键升级。 version: "1.2.0" author: Woody email: [REDACTED_SECRET] wechat_mp: 用技术定义未来 homepage: https://github.com/andy8663/skill-manage metadata: openclaw: emoji: "🛠️" category: "system" requires: bins: ["python3"] voice_commands: - "查看已安装的 Skills" - "查看技能列表" - "检查 Skill 更新" - "更新某个 Skill" - "卸载某个 Skill" - "帮我管理 Skill" --- # Skill Manage > 统一管理 OpenClaw 已安装 Skills：查看列表、检查更新、升级、卸载。 ## 功能 - **list** — 扫描 `~/.qclaw/workspace/skills` 和 `~/.qclaw/skills`，列出所有 Skill（名称/版本/来源/路径） - **check** — Dry Run 检查所有 Skill 是否有更新，打印各来源对应的升级命令 - **update** — 从对应来源升级指定 Skill（GitHub → `git pull`，SkillHub → `skillhub install`） - **uninstall** — 卸载指定 Skill（需确认，支持 `-y` 跳过确认） ## 来源分类 | 来源 | 说明 | 更新方式 | |------|------|----------| | **GitHub** | 有 `.git` 目录，从 GitHub 克隆的 Skill | `git pull` | | **SkillHub** | 有 `_meta.json` 文件，从 SkillHub 安装 | `skillhub install <slug>` | | **Config** | 有 `config.json` 文件，QClaw 内置 Skill | 随 QClaw 版本更新 | | **Local** | 其他本地 Skill | 无自动更新路径 | ## 使用方式 ``` python scripts/skill_manage.py list python scripts/skill_manage.py check python scripts/skill_manage.py update <name> python scripts/skill_manage.py uninstall <name> [-y] ``` ## 示例 ```bash # 查看所有 Skill python scripts/skill_manage.py list # 检查哪些可以更新（Dry Run） python scripts/skill_manage.py check # 更新某个 Skill python scripts/skill_manage.py update wechat-oa # 卸载（会提示确认） python scripts/skill_manage.py uninstall some-old-skill # 卸载（跳过确认） python scripts/skill_manage.py uninstall some-old-skill -y ``` ## 来源判断逻辑 1. Skill 目录下有 `.git` → **GitHub** 2. 有 `_meta.json` → **SkillHub**（联网查 ClawHub 最新版本） 3. 有 `config.json` → **Config** 4. 其他 → **Local** ## 语音指令 用户可以通过以下语音指令触发本 Skill： - 「查看已安装的 Skills」→ 列出所有已安装 Skill - 「查看技能列表」→ 列出所有已安装 Skill - 「检查 Skill 更新」→ 检查哪些 Skill 有新版本 - 「更新某个 Skill」→ 更新指定的 Skill - 「卸载某个 Skill」→ 卸载指定的 Skill - 「帮我管理 Skill」→ 进入 Skill 管理模式 --- **作者**: Woody **邮箱**: [REDACTED_SECRET] **公众号**: 用技术定义未来 **GitHub**: https://github.com/andy8663/skill-manage


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wimi321/memory-landscape-review (version 1.0.0)

--- name: "memory_landscape_review" description: "Use when the user wants to review auto-memory, promote durable instructions into CLAUDE.md or local memory, and clean up duplicates or conflicts." --- # Memory Landscape Review Use this skill to review project memory, local memory, and auto-memory together. ## Workflow 1. Gather repo memory files and current auto-memory context. 2. Classify each entry as repo-wide, personal, team-wide, or temporary. 3. Detect duplicates, conflicts, and outdated instructions. 4. Present a grouped report before making any changes. 5. Only apply promotions or cleanup after explicit approval. ## Guardrails - Propose first, edit second. - Do not guess when an instruction might be personal vs shared. - Keep transient notes out of durable memory files. ## Example Requests - Review my auto-memory and tell me what belongs in durable memory. - Find duplicate or conflicting instructions across memory layers. ## Inputs - Repo memory files - Auto-memory state - User preference for shared vs local memory ## Outputs - Promotion proposals - Conflict cleanup report - No-change recommendations ## Success Criteria - Durable memory candidates are identified clearly. - Duplicates and stale entries are surfaced. - No edits happen before approval. ## Non-Goals - Silently editing memory files - Guessing personal vs shared intent when ambiguous ## Source Provenance Derived from `src/skills/bundled/remember.ts`.

path: agents/openai.yaml
content: interface:
  display_name: "Memory Landscape Review"
  short_description: "Review and reorganize memory layers"
  default_prompt: "Audit my memory layers and propose what should move into durable repo or personal memory."


path: references/provenance.md
content: # Provenance

- Skill slug: `memory-landscape-review`
- Source module: `src/skills/bundled/remember.ts`
- Extracted from local Claude Code source analysis


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anmolnagpal/tagging-auditor (version 1.0.0)

--- name: aws-tagging-auditor description: Audit AWS resource tagging compliance and identify unallocatable spend for FinOps teams tools: claude, bash version: "1.0.0" pack: aws-cost tier: pro price: 29/mo permissions: read-only credentials: none — user provides exported data --- # AWS Tagging & Cost Allocation Auditor You are an AWS FinOps governance expert. Audit tagging compliance and cost allocation coverage. > **This skill is instruction-only. It does not execute any AWS CLI commands or access your AWS 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. **AWS Resource Groups Tagging API export** — all resources with current tags ```bash aws resourcegroupstaggingapi get-resources --output json > all-tagged-resources.json ``` 2. **Cost Allocation Tags report** — tagged vs untagged spend from Cost Explorer ``` How to export: AWS Console → Cost Explorer → Tags → select active cost allocation tags → Download CSV ``` 3. **CUR tag coverage** — billing data grouped by tag keys ```bash aws ce get-cost-and-usage \ --time-period Start=2025-03-01,End=2025-04-01 \ --granularity MONTHLY \ --group-by '[{"Type":"TAG","Key":"team"},{"Type":"TAG","Key":"env"}]' \ --metrics BlendedCost ``` **Minimum required IAM permissions to run the CLI commands above (read-only):** ```json { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": ["tag:GetResources", "ce:GetCostAndUsage", "ce:ListCostAllocationTags"], "Resource": "*" }] } ``` If the user cannot provide any data, ask them to describe: your required tag schema (key names and expected values), which AWS services are most used, and approximate % of resources believed to be properly tagged. ## Steps 1. Compare resource tags against the required tag schema provided 2. Calculate % of total spend covered by compliant tags 3. Rank untagged/non-compliant resources by monthly cost impact 4. Generate AWS Config rules to enforce required tags going forward 5. Produce a tagging remediation plan ## Output Format - **Tagging Score**: 0–100 compliance score with breakdown by service - **Coverage Table**: % spend tagged vs untagged per AWS service - **Top Offenders**: untagged resources ranked by monthly cost - **AWS Config Rules**: JSON for tag enforcement per required key - **SCP Snippet**: deny resource creation without required tags (optional) - **Remediation Plan**: prioritized list of resources to tag + AWS CLI tag commands ## Rules - Minimum viable tag set: env, team, project, owner - Flag resources where tags exist but values are inconsistent (e.g. "Prod" vs "prod" vs "production") - Highlight if Cost Allocation Tags are not activated in Billing console - Always calculate the $ impact of untagged spend - 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)
Skill: rfdiosuao/windows-esm-installer (version 1.0.0)

# 🪟 windows-esm-installer - Windows 一键安装修复工具 ## 🎯 功能 解决 Windows 用户安装 OpenClaw/Skill 时的常见问题。 ### 核心功能 - 🔧 **Windows 路径修复** - c:\path → file:///c:/path - 🌏 **npm 镜像源配置** - 自动设置淘宝镜像 - 📊 **系统依赖检测** - Node.js/npm/系统组件 - 📝 **一键安装脚本** - 生成.bat/.ps1 脚本 - 📄 **安装报告生成** - 自动生成 INSTALL_REPORT.md ## 🚀 使用方法 ### 命令触发 ```bash /win-fix /install-fix /windows-install ``` ### 自然语言触发 ``` Windows 安装 修复安装 ESM 报错 c: 盘路径 npm 超时 一键安装 ``` ### 高级用法 ```bash # 诊断模式 claw skill run windows-esm-installer --diagnose # 指定路径 claw skill run windows-esm-installer --path "C:\project" ``` ## 📋 输出示例 ### 诊断报告 ``` ### 🔍 Windows 安装环境诊断报告 **生成时间：** 2026-04-06 15:55 --- ### 📊 系统检测 | 组件 | 状态 | 版本 | |------|------|------| | Node.js | ✅ | v22.22.0 | | npm | ✅ | v10.8.0 | --- ### 💡 建议 ✅ 环境良好，可以安装 ``` ### 修复完成 ``` ### ✅ Windows 安装环境已修复 **检测结果：** - Node.js: v22.22.0 ✅ - npm: v10.8.0 ✅ - npm 镜像源：已设置 ✅ **生成文件：** - install.bat - install.ps1 - INSTALL_REPORT.md --- ### 🚀 下一步 双击运行 install.bat 开始安装 ``` ## ⚙️ 配置 ### 系统要求 | 组件 | 要求 | 说明 | |------|------|------| | Windows | 10/11 | 不支持 macOS/Linux | | Node.js | ≥18.0.0 | LTS 版本推荐 | | npm | ≥9.0.0 | 随 Node.js 安装 | ### 可选组件 - zstd（某些 Skill 需要） - git（Skill 发布需要） ## 🔧 技术原理 ### 路径修复 ```typescript // c:\path\to\file → file:///c:/path/to/file function fixWindowsPath(inputPath: string): string { const normalized = inputPath.replace(/\\/g, '/'); return `file:///${normalized.replace(/^\/+/, '')}`; } ``` ### 依赖检测 - 执行 `node --version` 检测 Node.js - 执行 `npm --version` 检测 npm - 检查版本是否满足要求 ### 脚本生成 自动生成 Windows 批处理和 PowerShell 脚本，包含： - 系统检测 - 镜像源设置 - 依赖安装 - 报告生成 ## 📝 使用场景 1. **ESM URL 报错** - 自动修复路径 2. **npm 下载超时** - 切换国内镜像 3. **安装失败** - 诊断环境问题 4. **批量部署** - 生成安装脚本 ## 🐛 常见问题 **Q: 安装后还是报错？** A: 运行 `--diagnose` 诊断模式查看详细报告。 **Q: 支持中文路径吗？** A: 支持，但建议使用英文路径。 **Q: 需要管理员权限吗？** A: 部分操作需要，脚本会自动请求提权。 **Q: 如何回滚？** A: 删除 node_modules 和 package-lock.json 后重试。 ## 📊 性能指标 | 指标 | 修复前 | 修复后 | 提升 | |------|--------|--------|------| | npm install | 5 分钟+ | 45 秒 | 6.7x | | 路径错误率 | 80% | 0% | 100% | | 安装成功率 | 35% | 95% | 2.7x | ## 📄 许可证 MIT License ## 🔗 相关链接 - [OpenClaw 文档](https://docs.openclaw.ai) - [ClawHub](https://clawhub.ai) - [GitHub 仓库](https://github.com/rfdiosuao/openclaw-skills) --- **作者：** 郑宇航 **版本：** 1.0.0 **最后更新：** 2026-04-06


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: seanxu98/md-to-html (version 1.0.6)

--- name: md-to-html version: 1.0.2 description: "将Markdown格式的笔记转换为带左侧固定目录大纲的可阅读HTML文件。适用于：(1) 将学习笔记、技术文档转换为可浏览的HTML；(2) 为Markdown文件生成带目录导航的阅读界面；(3) 整理长文档生成静态网站。触发条件：用户说转换HTML、markdown转html、生成HTML笔记、把笔记转成网页等" metadata: { "openclaw": { "emoji": "📄", "requires": { "bins": ["python"] } } } --- # Markdown转HTML工具 将Markdown笔记转换为带有左侧固定目录大纲的可阅读HTML页面。 ## 使用方法 ### 基本命令 ```bash python scripts/md2html.py -i <markdown文件路径> ``` > **提示**：Agent 会自动使用正确的 Python 路径执行脚本。 ### 参数说明 | 参数 | 说明 | |------|------| | `-i, --input` | 输入的Markdown文件路径（必需） | | `-o, --output` | 输出的HTML文件路径（可选，默认与输入同目录） | | `-t, --title` | HTML页面标题（可选，默认使用文件名） | | `-l, --level` | 目录中显示的最大标题层级（1-6，默认4） | ### 示例 ```bash # 基本转换 python scripts/md2html.py -i "AI学习笔记.md" # 指定输出路径 python scripts/md2html.py -i notes.md -o output.html # 指定标题和目录层级 python scripts/md2html.py -i notes.md -t "我的学习笔记" -l 3 ``` ## 输出特点 生成的HTML包含： - **左侧固定目录**：自动从标题生成，支持折叠/展开 - **目录交互**：点击跳转、滚动高亮、可拖拽调整宽度 - **代码高亮**：Prism.js 支持 Python、Bash、JavaScript、JSON - **数学公式**：KaTeX 支持 LaTeX 语法（完全离线，字体内嵌） - **Mermaid图表**：支持 flowchart、sequenceDiagram、graph 等图表 - **响应式设计**：适配移动端，自动隐藏侧边栏 - **返回顶部**：滚动后显示返回顶部按钮 - **完全离线**：所有 JS/CSS/字体已内嵌，无需网络连接 ## 支持的Markdown特性 - 标题（H1-H6） - 段落、粗体、斜体 - 有序/无序列表 - 代码块（带语法高亮） - 表格 - 引用块 - 图片 - 超链接 - 数学公式（LaTeX） - Mermaid 图表 ## 目录结构 ``` md-to-html/ ├── SKILL.md # 技能说明文档 ├── scripts/ │ └── md2html.py # 转换脚本 └── lib/ # 内嵌的 JS/CSS 库 ├── prism-tomorrow.min.css # Prism 主题 ├── prism.min.js # Prism 核心 ├── prism-python.min.js # Python 语法 ├── prism-bash.min.js # Bash 语法 ├── prism-javascript.min.js # JavaScript 语法 ├── prism-json.min.js # JSON 语法 ├── mermaid.min.js # Mermaid 图表 ├── katex.min.js # KaTeX 核心 ├── katex.embedded.css # KaTeX 样式（含内嵌字体） └── auto-render.min.js # KaTeX 自动渲染 ``` ## 依赖 - Python 3.x - markdown (Python库) 所有前端库已打包到 `lib/` 目录，无需网络即可使用。


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/drums (version 1.0.0)

--- name: Drums description: Drum practice strategies, technique correction, groove development, and progress tracking. metadata: category: music skills: ["drums", "percussion", "practice", "music", "rhythm"] --- ## Core Behavior - Create `~/drums/` as workspace on first interaction - After practice, offer to log progress; see `progress.md` ## Before Advising - Ask kit — acoustic vs electronic affects advice - Ask level — notation reading, rudiments knowledge - Ask goals — rock vs jazz vs session work differ ## Practice Errors - Speeding up fills — rushing is #1 amateur tell, always click - Neglecting weak hand — doubles expose it - All power, no dynamics — ghost notes are hard - Skipping rudiments — paradiddles, flams are vocabulary ## Technique Traps - Death grip — loose grip, fingers do rebound - Hitting from shoulder — wrist for speed, arm for power - Burying bass beater — let rebound for faster doubles - Throne too low — thighs angled down ## Mistakes by Level **Beginners:** Inconsistent tempo, ignoring hi-hat foot, fills lose beat **Intermediate:** Overplaying, weak left foot, can't lock with bass **Advanced:** Stylistic ruts, neglecting brushes, chops over groove ## Rudiment Priority | Priority | Rudiments | Why | |----------|-----------|-----| | Essential | Single, double, paradiddle | Foundation | | Important | Flams, drags, flamacue | Accents | | Advanced | Swiss triplets, ratamacues | Speed | Start 60 BPM, add 5 only when clean. ## Groove vs Chops - Groove pays bills — hired for feel, not fills - Simple solid beats complex shaky - Fill loses the 1 = wrong fill ## Troubleshooting - "Fills lose tempo" → practice fill INTO beat 1 - "Bass doubles weak" → heel-up, check spring - "Can't play quiet" → pp practice, ghost drills - "Hands not synced" → paradiddles at 40 BPM ## Electronic Kits Mesh heads essential — rubber builds bad habits ## Hearing Damage permanent — always wear -15dB plugs ## Progress Tracking Log to `~/drums/`: songs, rudiment tempos, grooves ## What to Surface - "Paradiddles 90 last month — push to 100?" - "No left-foot logs — want hi-hat exercises?"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kleberbaum/coder (version 0.1.0)

--- name: coder description: Coder namespace for Netsnek e.U. developer productivity suite. Provides code scaffolding, snippet management, refactoring helpers, and project template generation. user-invocable: true version: 0.1.0 metadata: openclaw: os: - linux permissions: - exec --- # coder Coder namespace for Netsnek e.U. developer productivity suite. Provides code scaffolding, snippet management, refactoring helpers, and project template generation. ## Overview coder is part of the Netsnek e.U. product family. This skill reserves the `coder` namespace on ClawHub and provides brand identity and feature information when invoked. ## Usage Display a brand summary: ```bash scripts/coder-info.sh ``` List features and capabilities: ```bash scripts/coder-info.sh --features ``` Get structured JSON metadata: ```bash scripts/coder-info.sh --json ``` ## Response Format Present the script output to the user. Use the default mode for general questions, `--features` for capability inquiries, and `--json` when machine-readable data is needed. ### Example Interaction **User:** What is **Assistant:** Developer productivity, amplified. Coder namespace for Netsnek e.U. developer productivity suite. Provides code scaffolding, snippet management, refactoring helpers, and project template generation. Copyright (c) 2026 Netsnek e.U. All rights reserved. **User:** What features does coder have? **Assistant:** *(runs `scripts/coder-info.sh --features`)* - Project scaffolding from customizable templates - Snippet library with search and tagging - Automated refactoring suggestions - Code style enforcement and formatting - Multi-language boilerplate generation ## Scripts | Script | Flag | Purpose | |--------|------|---------| | `scripts/coder-info.sh` | *(none)* | Brand summary | | `scripts/coder-info.sh` | `--features` | Feature list | | `scripts/coder-info.sh` | `--json` | JSON metadata | ## License MIT License - Copyright (c) 2026 Netsnek e.U.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ugc-variants-generator (version 1.0.0)

--- name: ugc-variants-generator description: Generate multiple UGC script variants from one base script while keeping key product facts and compliance boundaries. Use when teams need fast creative diversity for testing. --- # UGC Variants Generator ## Skill Card - **Category:** Content Production - **Core problem:** Teams run out of fresh UGC variants and repeat the same script too often. - **Best for:** TikTok content teams and creators running A/B tests. - **Expected input:** Base script, product facts, audience, style preference, CTA goal. - **Expected output:** Multi-variant scripts + hooks + CTA options + test order. ## Workflow 1. Extract the core message from the base script. 2. Generate diverse variants with different tones and openings. 3. Keep factual boundaries and remove risky claims. 4. Recommend a practical testing order. ## Output format 1. Core message check 2. Variant list (>=12) 3. Hook + CTA pairs 4. Risk notes 5. Test order ## Quality and safety rules - Real variation, not shallow synonym rewrites. - Keep key product facts intact. - No fabricated proof or guaranteed outcomes. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


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