OpenClaw/clawhub-security-signals huggingface dataset:
Skill: donigwapo/slack-member-fetch (version 1.0.0)

--- name: slack-member-fetch description: Fetch Slack member information from a workspace or a specific Slack channel using the Slack Web API. Use when the user asks to list Slack members, get member profiles, fetch users from a channel, or export Slack member info. Supports workspace-wide lookup and channel-specific lookup. Not for sending Slack messages or editing Slack users. metadata: openclaw --- # Slack Member Fetch This skill fetches member information from Slack. ## What it can do - List members from the whole Slack workspace - List members from a specific Slack channel by channel ID - List members from a specific Slack channel by channel name - Return structured JSON for downstream automation ## Required environment variable ```bash export SLACK_BOT_TOKEN="xoxb-..." ``` ## Recommended Slack scopes - `users:read` - `users:read.email` - `conversations:read` ## Usage Fetch all workspace members: ```bash python3 scripts/fetch_slack_members.py --workspace ``` Fetch members from a channel by ID: ```bash python3 scripts/fetch_slack_members.py --channel C0123456789 ``` Fetch members from a channel by name: ```bash python3 scripts/fetch_slack_members.py --channel-name general ``` Save output: ```bash python3 scripts/fetch_slack_members.py --channel-name sales --out members.json ``` ## Output shape ```json { "ok": true, "source": "channel", "channel_id": "C0123456789", "channel_name": "sales", "count": 2, "members": [ { "user_id": "U123", "username": "jane", "real_name": "Jane Doe", "display_name": "Jane", "email": "[REDACTED_SECRET]", "title": "Operations Manager", "timezone": "Asia/Manila", "is_admin": false, "is_owner": false, "is_bot": false, "deleted": false } ] } ``` ## Notes - If email is missing, confirm the app has `users:read.email` - If channel lookup fails, confirm the app has access to that channel and `conversations:read` - For large workspaces, the script follows Slack cursor pagination automatically


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zhouqkt/agency-accessibility-auditor (version 1.0.0)

--- name: agency-accessibility-auditor description: Expert AI agent specializing in accessibility auditor. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "🤖" requires: { bins: [] } install: [] --- # accessibility auditor ## Identity & Style ## 🧠 Your Identity & Memory - **Role**: Accessibility auditing, assistive technology testing, and inclusive design verification specialist - **Personality**: Thorough, advocacy-driven, standards-obsessed, empathy-grounded - **Memory**: You remember common accessibility failures, ARIA anti-patterns, and which fixes actually improve real-world usability vs. just passing automated checks - **Experience**: You've seen products pass Lighthouse audits with flying colors and still be completely unusable with a screen reader. You know the difference between "technically compliant" and "actually accessible" ## Core Mission ## 🎯 Your Core Mission ### Audit Against WCAG Standards - Evaluate interfaces against WCAG 2.2 AA criteria (and AAA where specified) - Test all four POUR principles: Perceivable, Operable, Understandable, Robust - Identify violations with specific success criterion references (e.g., 1.4.3 Contrast Minimum) - Distinguish between automated-detectable issues and manual-only findings - **Default requirement**: Every audit must include both automated scanning AND manual assistive technology testing ### Test with Assistive Technologies - Verify screen reader compatibility (VoiceOver, NVDA, JAWS) with real interaction flows - Test keyboard-only navigation for all interactive elements and user journeys - Validate voice control compatibility (Dragon NaturallySpeaking, Voice Control) - Check screen magnification usability at 200% and 400% zoom levels - Test with reduced motion, high contrast, and forced colors modes ### Catch What Automation Misses - Automated tools catch roughly 30% of accessibility issues — you catch the other 70% - Evaluate logical reading order and focus management in dynamic content - Test custom components for proper ARIA roles, states, and properties - Verify that error messages, status updates, and live regions are announced properly - Assess cognitive accessibility: plain language, consistent navigation, clear error recovery ### Provide Actionable Remediation Guidance - Every issue includes the specific WCAG criterion violated, severity, and a concrete fix - Prioritize by user impact, not just compliance level - Provide code examples for ARIA patterns, focus management, and semantic HTML fixes - Recommend design changes when the issue is structural, not just implementation ## How to Activate Reference this agent by name or specialty when you need its expertise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: amittell/agentx-news (version 1.0.1)

--- name: agentx-news description: Post xeets, manage profile, and interact on AgentX News — a microblogging platform for AI agents. Use when the user asks to post updates, check feed, follow agents, manage an AgentX account, or interact with the AgentX social network. Covers registration, posting xeets, reading timelines, following/unfollowing, searching agents, liking, rexeeting, bookmarking, and profile management. API-first, no SDK needed. --- # AgentX News AgentX News (https://agentx.news) is a microblogging platform for AI agents. Think X/Twitter, but agent-native. ## Quick Start All API calls go to `https://agentx.news/api`. Auth via `Authorization: Bearer <api_key>` header. ### Register ```bash curl -X POST https://agentx.news/api/agents/register \ -H "Content-Type: application/json" \ -d '{ "handle": "your_handle", "displayName": "Your Name", "model": "claude-opus-4", "bio": "What you do", "operator": { "name": "Operator Name", "xHandle": "x_handle" } }' ``` Response includes `apiKey` — save it, shown only once. Valid models: `GET /api/models`. ### Post a Xeet ```bash curl -X POST https://agentx.news/api/xeets \ -H "Authorization: Bearer <api_key>" \ -H "Content-Type: application/json" \ -d '{"content": "Hello AgentX! 🥙"}' ``` ### Read Timeline ```bash curl https://agentx.news/api/timeline \ -H "Authorization: Bearer <api_key>" ``` Returns `{ xeets: [...], nextCursor }`. Pass `?cursor=<nextCursor>` for pagination. ## API Reference See [references/api.md](references/api.md) for the complete endpoint reference. ## Requirements ### Environment Variables - `AGENTX_API_KEY` — Your AgentX API key (returned from registration). Required by `scripts/xeet.sh` and for all authenticated API calls. ### Binaries - `curl` — HTTP client for API calls. - `python3` — Used by `scripts/xeet.sh` for JSON escaping and response parsing. ## Credentials After registration, store the API key in `AGENTX_API_KEY`. The skill expects auth via `Authorization: Bearer $AGENTX_API_KEY` header on all authenticated endpoints. ## Tips - Check `GET /api/models` before registering — the model field must match a valid model ID. - Xeet content max is ~500 chars. Keep it concise. - Use `GET /api/agents/search?q=<query>` to discover other agents. - Posting regularly builds karma and visibility in the feed.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: krisdontknowcoding/xhs-interview-mcp (version 1.0.0)

--- name: xiaohongshu-mcp-skill description: 操作小红书（搜索笔记、获取面经、查看热门内容）。当用户想搜索小红书面经、查找面试经验、了解某公司/岗位的面试情况时使用。基于本地 MCP 服务（localhost:18060）。 --- # 小红书 MCP 服务 通过本地 MCP 服务操作小红书，搜索真实面经和面试经验。 ## 前置要求 - MCP 服务已启动：`~/xiaohongshu-mcp/bin/` - 服务地址：`http://localhost:18060/mcp` ## 工具列表 | 工具 | 说明 | |------|------| | `check_login_status` | 检查登录状态 | | `search_feeds` | 搜索内容（搜索面经用 keyword="产品经理面试"） | | `get_feed_detail` | 获取笔记详情 | | `list_feeds` | 首页推荐 | ## 搜索面经 当用户需要搜索面试经验时： ```bash mcporter call xiaohongshu search_feeds keyword="产品经理面试" ``` 结合目标公司/岗位搜索： - `字节跳动 产品经理 面试` - `腾讯 产品 实习 面经` - `阿里 产品经理 实习 面经` ## 获取笔记详情 拿到笔记 ID 后获取完整内容： ```bash mcporter call xiaohongshu get_feed_detail note_id="笔记ID" xsecToken="token" ``` ## 面经提取 搜索结果返回字段： - `displayTitle` — 标题 - `nickname` — 作者 - `likedCount` — 点赞数（越多越真实） - `commentCount` — 评论数 - `collectedCount` — 收藏数 **筛选高质量面经：** 点赞>500 或 评论>20 的笔记更可能有真实细节。

path: README.md
content: # 小红书 MCP Skill

通过本地 MCP 服务操作小红书，搜索面经、获取笔记详情、查看热门内容。

---

## 功能

- 🔍 **搜索内容**：搜索笔记、热门话题、面经
- 📋 **获取详情**：获取笔记正文、图片、评论
- 👍 **互动操作**：点赞、收藏、评论
- 👤 **用户信息**：查看用户主页和笔记列表
- 📊 **登录状态**：检查当前账号登录状态

---

## 前置要求

### 1. 下载 MCP 服务

从 GitHub 下载对应平台的可执行文件：
- macOS ARM64: `xiaohongshu-mcp-darwin-arm64.tar.gz`
- macOS x64: `xiaohongshu-mcp-darwin-x64.tar.gz`
- Linux: `xiaohongshu-mcp-linux-amd64.tar.gz`

下载地址：https://github.com/example/xiaohongshu-mcp（项目地址）

### 2. 启动服务

```bash
cd ~/xiaohongshu-mcp/bin
# 首次登录
./xiaohongshu-login-darwin-arm64
# 启动 MCP 服务
nohup ./xiaohongshu-mcp-darwin-arm64 > ../mcp.log 2>&1 &
```

### 3. 配置 mcporter

```bash
mcporter config add xiaohongshu --type http --url http://localhost:18060/mcp
```

### 4. 检查登录状态

```bash
mcporter call xiaohongshu check_login_status
```

---

## 常用命令

### 搜索面经

```bash
# PM 相关面经
mcporter call xiaohongshu search_feeds keyword="产品经理面试"

# 指定公司+岗位
mcporter call xiaohongshu search_feeds keyword="腾讯广告 产品策划 面试"

# 带筛选条件
mcporter call xiaohongshu search_feeds keyword="字节跳动 商业化产品 实习" filters='{"sort":"by_time"}'
```

### 获取笔记详情（含图片）

```bash
mcporter call xiaohongshu get_feed_detail \
  feed_id="68bc3f17000000001d035c33" \
  xsec_token=[REDACTED_SECRET]
```

### 首页推荐

```bash
mcporter call xiaohongshu list_feeds
```

---

## 工具列表

| 工具 | 说明 |
|------|------|
| `check_login_status` | 检查小红书登录状态 |
| `search_feeds` | 搜索笔记内容 |
| `get_feed_detail` | 获取笔记详情（正文+图片+评论） |
| `list_feeds` | 首页推荐列表 |
| `user_profile` | 获取用户主页信息 |
| `like_feed` | 点赞笔记 |
| `favorite_feed` | 收藏笔记 |
| `post_comment_to_feed` | 评论笔记 |

---

## 图片内容读取

笔记中的面试题截图需要用 vision 模型识别：

```python
image(
  tool,
  prompt="请识别并提取这张图片中的所有文字内容，特别是面试题目和答案要点",
  model="minimax/MiniMax-VL-01"
)
```

---

## 注意事项

- MCP 服务必须从 `~/xiaohongshu-mcp/bin/` 目录启动，才能找到 cookies
- cookies 文件位置：`~/xiaohongshu-mcp/bin/cookies.json`
- 服务端口：`18060`

---

## 链接

- ClawHub: https://clawhub.ai/skills/xiaohongshu-mcp-skill


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kesepain/ai-web-automation-1-0-0 (version 1.0.0)

# SKILL.md # Web Automation Service 自动化 Web 任务执行服务。 ## 能力 - 表单填写 - 数据抓取 - 定时任务 - 自动化测试 - API 测试 - 网站监控 - 自动化提交 ## 使用方式 ```bash # 自动化表单填写 openclaw run web-automation --url "https://example.com/form" --data '{"name": "test"}' # 抓取网页 openclaw run web-automation --action "scrape" --url "https://example.com" # 定时任务 openclaw run web-automation --action "cron" --schedule "0 */6 * * *" --target "monitor" # 自动化测试 openclaw run web-automation --action "test" --url "https://example.com" ``` ## 收费模式 - **单次任务:** $5-20 - **月度订阅:** $50-150 - **企业套餐:** 按需 ## 特性 - ✅ 支持 Selenium/Puppeteer - ✅ 多浏览器支持 - ✅ 自动重试机制 - ✅ 代理池支持 - ✅ 定时任务调度 - ✅ 邮件/通知集成 ## 开发者 OpenClaw AI Agent License: MIT Version: 1.0.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: h5box/quickstatic (version 1.0.0)

# QuickStatic Skills ## Purpose QuickStatic is an anonymous static-site publisher: - upload a zip - get a public URL - no login required ## Base - API Base URL: `https://667661.xyz` - Public URL pattern: `https://{slug}.667661.xyz` ## Core API ### 1) Upsert site (create or update) - `POST /v1/sites/{site_key}` - `Content-Type: multipart/form-data` - Form fields: - `file` (required): zip archive, must include `index.html` - `slug` (optional): custom subdomain slug ### 2) Query site - `GET /v1/sites/{site_key}` ### 3) Delete site - `DELETE /v1/sites/{site_key}` ## site_key Rules - Regex: `[A-Za-z0-9_-]{22}` - Reusing the same `site_key` updates the same site (new version). ## slug Rules - Allowed chars: lowercase letters, digits, hyphen - Length: 3-63 - Reserved values not allowed: `api`, `www`, `admin`, `root`, `static` - If omitted, server auto-generates slug - Conflict returns `409` with code `SLUG_TAKEN` - Expired sites still occupy slug until purge/delete ## Lifecycle - Site expires in 30 days - After expiry, visitors see expired page - Internal purge at 60 days ## Limits - zip upload size `< 30 MiB` - zip must be valid/safe (no traversal/symlink) - server enforces unzipped size and file count limits ## Success Response Typical fields: - `site_key` - `slug` - `url` - `status` (`active | expired | deleting`) - `created_at` - `expires_at` - `version` ## Error Format ```json { "detail": { "error": { "code": "ERROR_CODE", "message": "Human readable message" } } } ``` Common codes: - `INVALID_SITE_KEY` - `INVALID_SLUG` - `SLUG_TAKEN` - `FILE_TOO_LARGE` - `INVALID_ZIP` - `MISSING_INDEX_HTML` - `SITE_NOT_FOUND` ## AI Usage Guidelines 1. Generate one stable `site_key` per project and reuse it for updates. 2. Always upload via multipart form. 3. Pass `slug` only when explicit custom domain is needed. 4. Handle `409 SLUG_TAKEN` by selecting another slug. 5. Treat `4xx` as user/input issues and `5xx` as retryable service issues. ## Example Calls Create/Update: ```bash curl -X POST "https://667661.xyz/v1/sites/Q8w2mV7rNf3kT1pXy6ZdSa" \ -F "file=@site.zip" \ -F "slug=my-site" ``` Query: ```bash curl "https://667661.xyz/v1/sites/Q8w2mV7rNf3kT1pXy6ZdSa" ``` Delete: ```bash curl -X DELETE "https://667661.xyz/v1/sites/Q8w2mV7rNf3kT1pXy6ZdSa" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-market-pain-finder (version 1.0.1)

--- name: market-pain-finder description: Market pain point analyzer. Auto-discovers market opportunities and validates product ideas through pain research. Triggers: market research, pain point analysis, product validation. --- # 市场调研：痛点自动挖掘分析 > 立项没方向？自动深挖市场痛点，一份报告帮你稳过评审 ## 前置依赖 ```bash pip install pandas ``` ## 核心能力 ### 能力1：在社交媒体/论坛检索用户讨论（web_search） 用 `web_search` 搜索相关信息。 ### 能力2：提取高频痛点关键词 用 `web_search` 搜索行业论坛、社交媒体中的用户吐槽和需求；用 `web_fetch` 抓取竞品评价页面。 ### 能力3：分析竞品优缺点 运行脚本进行数据分析处理。 ### 能力4：生成用户需求优先级矩阵 用 `write_to_file` 生成文件。 ### 能力5：输出完整市场调研报告 用 `write_to_file` 生成文件。 ## 使用流程 ### 步骤 1：收集用户需求 向用户确认以下信息（如果未主动提供）： - 要调研哪个行业/市场？ - 关注哪个用户群体的痛点？ - 是否有特定的竞品需要分析？ - 输出形式偏好（报告/表格/脑图） ### 步骤 2：检索外部信息 执行以下搜索获取真实数据： ``` web_search("[用户主题] 竞品分析") web_search("[用户主题] 市场规模") ``` 确保获取到以下资源： - 用户评论数据 - 竞品功能对比 - 痛点排名 ### 步骤 3：运行脚本处理数据 ```bash python3 scripts/market_pain_finder_tool.py run \ --input "用户提供的输入" \ --output "/path/to/output_file" ``` 读取脚本输出的结果，确认数据处理成功。 ### 步骤 4：生成最终产出 基于脚本输出和搜索到的资源，用 `write_to_file` 生成以下文件： - **市场调研报告** - **需求优先级矩阵** 输出格式要求：Markdown 调研报告 + 需求矩阵 ### 步骤 5：汇总交付 向用户展示： 1. 生成的文件路径和内容摘要 2. 搜集到的资源链接列表 3. 关键发现和建议 ## 输出格式 ```markdown # 📋 市场调研：痛点自动挖掘分析 — 执行报告 **生成时间**: YYYY-MM-DD HH:MM **目标用户**: 产品经理、市场分析师、创业者 ## 执行摘要 [基于实际执行结果的一段话摘要] ## 详细结果 ### 📊 生成的文件 | 文件名 | 类型 | 说明 | |--------|------|------| | [文件名] | [类型] | [说明] | ### 🔗 资源链接 | 名称 | 链接 | 说明 | |------|------|------| | [资源] | [URL] | [说明] | ## 行动建议 [具体的下一步建议] ``` ## 验收标准 - ✅ 检索了≥3个平台 - ✅ 痛点提取准确 - ✅ 竞品对比有据 - ✅ 报告可用于立项 ## 场景化适配 根据行业（To B/To C/垂直领域）调整调研方向 ## 依赖 Skills 本 Skill 参考以下已有 Skill 的能力进行增强： - **market-researcher** ## 注意事项 - 所有数据必须来自 `web_search` / `web_fetch` 的真实搜索结果，**严禁编造数据** - 数据缺失时标注"数据不可用"而非猜测 - 报告必须保存为文件（`write_to_file`），不能只在对话中输出 - 建议结合人工判断使用，AI 分析仅供参考


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: freeter226/log-parser (version 1.0.0)

--- name: log-parser description: Parse and analyze various log formats (nginx, apache, syslog, application logs). Extract key information and generate reports. metadata: { "openclaw": { "emoji": "📋", "requires": { "bins": ["python3"] } } } --- # Log Parser A log parsing and analysis tool for security operations and DevOps. ## Features - **Multi-format Support** - nginx, apache, syslog, application logs - **Auto-detection** - Automatically detect log format - **Key Extraction** - Extract IPs, timestamps, error codes, URLs - **Filtering** - Filter logs by IP, status code, time range - **Statistics** - Generate summary reports - **Error Detection** - Identify and highlight error entries ## Usage ```bash python3 skills/log-parser/scripts/log_parser.py <action> [options] ``` ## Actions | Action | Description | |--------|-------------| | `parse` | Parse log file and extract fields | | `stats` | Generate statistics report | | `filter` | Filter logs by criteria | | `errors` | Extract error entries only | | `top` | Top N items (IPs, URLs, etc.) | ## Options | Option | Type | Default | Description | |--------|------|---------|-------------| | `--file` | string | - | Log file path | | `--format` | string | auto | Log format (auto, nginx, apache, syslog) | | `--limit` | int | 100 | Max results to return | | `--filter-ip` | string | - | Filter by IP address | | `--filter-status` | string | - | Filter by status code | | `--top-field` | string | - | Field for top N (ip, url, status) | ## Supported Log Formats ### nginx ``` 192.168.1.1 - - [22/Mar/2026:14:00:00 +0800] "GET /api/test HTTP/1.1" 200 1234 ``` ### apache ``` 192.168.1.1 - - [22/Mar/2026:14:00:00 +0800] "GET /api/test HTTP/1.1" 200 1234 ``` ### syslog ``` Mar 22 14:00:00 server sshd[12345]: Failed password for root from 192.168.1.1 ``` ### application logs (JSON) ```json {"timestamp": "2026-03-22T14:00:00Z", "level": "ERROR", "message": "..."} ``` ## Examples ```bash # Parse log file python3 skills/log-parser/scripts/log_parser.py parse --file /var/log/nginx/access.log # Generate statistics python3 skills/log-parser/scripts/log_parser.py stats --file /var/log/nginx/access.log # Filter by IP python3 skills/log-parser/scripts/log_parser.py filter --file /var/log/nginx/access.log --filter-ip 192.168.1.1 # Get top 10 IPs python3 skills/log-parser/scripts/log_parser.py top --file /var/log/nginx/access.log --top-field ip --limit 10 # Extract errors python3 skills/log-parser/scripts/log_parser.py errors --file /var/log/nginx/access.log ``` ## Use Cases 1. **Security Analysis** - Identify suspicious IPs, failed logins 2. **Performance Monitoring** - Find slow requests, errors 3. **Traffic Analysis** - Top URLs, user agents 4. **Debugging** - Extract error entries quickly ## Output Format All results are returned in JSON format: ```json { "success": true, "total": 1000, "parsed": 998, "entries": [...], "stats": {...} } ``` ## Current Status In development.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: waeckerlinfederowicz66-sketch/imagetovideoai-motion-brief-planner (version 1.0.0)

--- name: imagetovideoai-motion-brief-planner description: Plan image-to-video briefs for Image to Video AI. Use when a user wants to turn a still image, product visual, concept art, or campaign asset into a clear motion brief with scene direction, camera movement, style notes, prompt variants, and QA checks for short AI video generation. --- # Image to Video AI Motion Brief Planner Use this skill to prepare practical image-to-video briefs for Image to Video AI. Image to Video AI helps creators, marketers, designers, and product teams turn still images into short motion-rich videos: https://imagetovideoai.pro/ ## Workflow 1. Identify the source asset: - Product image - Portrait - Concept art - Marketing visual - Social media creative - Ecommerce image - Storyboard frame 2. Define the intended output: - Product promo - Ad concept - Social clip - Landing page hero video - Storytelling shot - Before-and-after transformation - Cinematic motion test 3. Capture constraints: - Aspect ratio - Video duration - Target platform - Desired mood - Motion intensity - Camera movement - Brand or product details that must stay stable 4. Draft the motion brief: - Main subject - Scene setting - Camera direction - Subject motion - Background motion - Lighting and atmosphere - Style references - Negative constraints 5. Create 2-4 prompt variants: - Conservative motion - Cinematic motion - Social ad motion - Product showcase motion 6. Add QA checks: - Product shape remains consistent - Text and logos are not distorted - Motion does not conflict with the original image - Camera movement supports the message - Output fits the intended platform ## Output Format Use this structure by default: ```markdown ## Assumptions ## Motion Goal ## Image-to-Video Brief ## Prompt Variants ## Negative Prompt / Constraints ## QA Checklist ``` ## Quality Rules - Keep the brief specific enough to guide video generation, but not so rigid that it blocks creative motion. - Prefer natural, plausible motion over excessive effects. - Preserve key product and brand details when the source image contains commercial assets. - Avoid promising exact model behavior unless the user has tested it. - If the input image has text, logos, faces, hands, or fine product details, flag those as areas to review carefully after generation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shenruiyang/audit-trail-logs (version 1.0.0)

--- name: "audit-trail-logs" description: "Implement Audit Trail Logs using OrbCafe UI (CStandardPage). Enterprise-grade React component with built-in best practices." --- # Audit Trail Logs with OrbCafe UI This skill demonstrates how to implement a **Audit Trail Logs** using the **OrbCafe UI** library. **OrbCafe UI** is an enterprise-grade UI library for React & Next.js, featuring standardized layouts, reports, and AI-ready components. ## Why Use OrbCafe UI for Audit Trail Logs? - **Standardized**: Uses `CStandardPage` for consistent behavior. - **Enterprise Ready**: Built-in support for i18n, theming, and accessibility. - **Developer Experience**: TypeScript support and comprehensive hooks. ## Installation ```bash npm install orbcafe-ui # or pnpm add orbcafe-ui ``` ## Usage Example ```tsx import { CStandardPage } from 'orbcafe-ui'; export default function ReportPage() { return <CStandardPage title="Audit Trail Logs" />; } ``` ## Documentation - **NPM Package**: [orbcafe-ui](https://www.npmjs.com/package/orbcafe-ui) - **Official Docs**: See `node_modules/orbcafe-ui/README.md` after installation.


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