OpenClaw/clawhub-security-signals huggingface dataset:
Skill: unclecheng-li/unclecheng-jobsmatch (version 1.0.0)

--- name: resume-job-matcher description: 智能简历求职匹配助手，当用户上传简历文件（PDF、Word、图片等）需要进行求职岗位匹配、企业背景调查、岗位适配度评估时使用。 --- # 智能简历求职匹配助手 ## Overview 本技能帮助用户实现一站式求职辅助：支持多种格式简历解析，自动联网搜索主流招聘平台匹配岗位，提供企业背景调查和岗位适配度评估，帮助用户规避求职风险，大幅提升找工作效率。 ## 工作流程 本技能采用顺序化工作流程，按照以下步骤执行： ### 步骤1：简历解析处理 - 接收用户上传的简历文件，支持格式包括：PDF、Word（.doc/.docx）、图片（.jpg/.png/.jpeg） - 调用AI大模型提取简历关键信息：个人基本信息、工作经历、项目经验、技能栈、学历背景、求职意向等 - 结构化整理简历信息，形成标准化的用户能力画像 ### 步骤2：岗位搜索与匹配 - 联网搜索覆盖全渠道招聘信息来源，**兼顾互联网大厂与中小厂/创业公司岗位**： - 综合招聘平台：智联招聘、前程无忧（51Job）、BOSS直聘、猎聘、拉勾网、易直聘、鱼泡直聘 - 应届生/实习平台：实习僧、国家大学生就业服务平台 - 企业官方渠道：互联网大厂（腾讯、阿里、字节跳动、百度、华为等）、央国企、中小厂/创业公司官方网站招聘板块 - 创业公司/中小厂专属渠道：BOSS直聘创业公司专区、拉勾网互联网创业公司板块、本地人才招聘网、脉脉招聘专区 - 搜索策略：优先匹配符合用户技能和求职意向的岗位，大厂和中小厂岗位各占一定比例，提供更多元的选择 - 基于用户简历画像与招聘岗位要求进行智能匹配，筛选出Top 10最适配的岗位（大厂5-6个，中小厂4-5个） - 生成岗位列表展示给用户，包含岗位名称、公司名称、公司规模、薪资范围、工作地点、匹配度得分、**岗位招聘链接**等信息，明确标注大厂/中小厂属性，供用户选择 ### 步骤3：企业背调与岗位评估 用户选择目标岗位后，以AI职业评估师身份执行以下分析： 1. **招聘信息真实性核查**： - 对比多平台该企业的招聘信息，排查虚假宣传、岗位与描述不符等风险 - 识别空壳公司、诈骗类招聘陷阱，给出风险提示 2. **企业深度背景调查**： - 联网收集企业公开信息：注册资本、注册年限、经营范围、法定代表人信息 - 核查企业法律风险：是否有法律诉讼、债务纠纷、行政处罚记录 - 调研企业用工情况：员工社保缴纳人数、薪资发放口碑、员工评价 - 综合企业查类平台公开信息，形成完整企业背调报告 3. **岗位适配度评估**： - 对比用户简历的工作经历、技能栈、项目经验与岗位要求的匹配程度 - 给出0-100分的匹配度评分，详细说明匹配优势和不足 - 提供针对性的求职建议，包括简历优化方向、面试准备要点 4. **输出最终评估报告**： - 整合以上所有信息，生成结构化的评估报告 - **报告中必须包含该岗位的招聘信息链接**，方便用户直接点击查看和投递 - 明确标注风险等级，给出是否建议投递的结论 ## 资源说明 ### scripts/ 包含简历解析处理相关的可执行脚本： - `resume_parser.py`：支持多种格式简历的文本提取和结构化解析 - `job_searcher.py`：招聘网站数据爬取和岗位匹配工具 - `export_pdf.py`：Markdown评估报告导出为PDF格式工具，方便用户保存和查阅 ### references/ 包含技能运行所需的参考资料： - `recruitment_sites.md`：各招聘网站的搜索规则和信息提取指南 - `enterprise_inquiry_sources.md`：企业背景调查的权威数据源列表 - `evaluation_criteria.md`：岗位匹配度和企业风险的评估标准 ### assets/ 包含输出报告相关的模板资源： - `assessment_report_template.md`：评估报告的Markdown模板 - `risk_icon.png`：报告中使用的风险等级图标资源 ## 使用案例 ### 案例：AI算法工程师求职匹配 **用户输入**： ``` @skill://resume-job-matcher @C:/Users/UncleC/Desktop/AI-Job-Resume(v2).pdf 根据我的简历寻找匹配的岗位 ``` **执行流程**： 1. **简历解析**：调用`scripts/resume_parser.py`解析PDF简历，提取用户信息： - 教育背景：本科网络工程专业，GPA 89.52/100 - 实习经历：百度深度学习研究院大模型算法实习生、华为诺亚方舟实验室多模态算法实习生 - 技能栈：Transformer架构、大模型微调/量化/推理优化、Hugging Face/DeepSpeed工具链 - 求职意向：大模型算法工程师相关岗位 2. **岗位搜索匹配**：使用`web_search`工具联网搜索2026年大模型算法相关校招/实习岗位，覆盖智联招聘、BOSS直聘、实习僧、企业官网等渠道，筛选出Top 5匹配度最高的岗位： - 百度 大模型算法实习生（匹配度95%） - 百度 大模型算法工程师（2026校招，匹配度94%） - 百度 大模型应用算法工程师（AIDU项目，匹配度92%） - 华为 算法工程师（大模型方向，匹配度90%） - 字节跳动 大模型训练算法工程师（匹配度88%） 3. **用户选择岗位**：用户选择序号1（百度大模型算法实习生岗位） 4. **企业背调与评估**： - 再次使用`web_search`工具获取百度企业工商信息、法律风险、员工评价等背调信息 - 对比用户简历与岗位要求，计算匹配度得分95分 - 生成结构化评估报告，包含岗位信息、企业背调、匹配度分析、求职建议 5. **结果输出**：导出Markdown格式评估报告，支持一键导出为PDF格式 **输出成果**： - 岗位匹配列表 - 《百度大模型算法实习生岗位评估报告》


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bytesagain1/awesome-submitter-pro (version 1.0.0)

--- name: "awesome-submitter-pro" version: "1.0.0" description: "Generate submission issues for GitHub awesome-lists. Auto-creates formatted Issue content for awesome-indie, awesome-privacy, awesome-cloudflare, and 7 more popular awesome repositories. Highest ROI backlink strategy — permanent, free, high-authority links from starred repos." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [github, awesome-list, backlink, seo, open-source, submission] category: "marketing" --- # Awesome Submitter Pro Generate ready-to-post GitHub Issues for awesome-list repositories. The highest ROI backlink strategy: awesome-lists are permanent, free, and many have 10K+ stars (high domain authority). ## Commands | Command | Description | |---------|-------------| | `generate` | Generate Issue content for a specific awesome-list | | `repo-list` | Show all supported awesome-list repositories | | `find-lists` | Find relevant awesome-lists for your product | | `submit-guide` | Step-by-step Issue submission guide | | `format-rules` | Formatting requirements per repository | | `pr-vs-issue` | When to use Pull Request vs Issue | | `success-tips` | Tips for getting accepted | | `track` | Track submission status across repos |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: seekeyl/whatsapp-image-send (version 1.0.1)

--- name: whatsapp-image-send description: Send images/files to WhatsApp. Use when user wants to send an image, photo, screenshot, video, audio or document via WhatsApp. Workflow: (1) Download to /tmp, (2) Copy to ~/.openclaw/workspace/, (3) Send via message tool with filePath, (4) Delete /tmp file. Required because WhatsApp plugin only allows workspace paths for media. --- # WhatsApp Image Send ## Workflow 1. **Download**: Save file to `/tmp/<filename>` ```bash curl -o /tmp/<filename> <url> ``` 2. **Copy to workspace**: WhatsApp requires workspace path ```bash cp /tmp/<filename> ~/.openclaw/workspace/ ``` 3. **Send to WhatsApp** ```bash message --channel whatsapp --target <phone> --filePath /home/seekey/.openclaw/workspace/<filename> --message "<caption>" ``` 4. **Cleanup**: Delete temp file ```bash rm /tmp/<filename> ``` ## Notes - Phone format: +country + number (e.g., +14843124960) - Supported: jpg, png, gif, video, audio, document


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: ludiansheng/duplicate-file-cleaner (version 1.0.0)

--- name: duplicate-file-cleaner description: 扫描并识别计算机中的重复文件，提供智能整理建议；当用户需要清理重复文件、释放磁盘空间或整理照片库时使用 --- # 重复文件整理工具 ## 任务目标 - 本 Skill 用于：扫描指定目录，识别完全相同的重复文件，生成结构化报告并提供整理建议 - 能力包含：文件内容哈希比对、重复文件分组、智能删除建议、磁盘空间分析 - 触发条件：用户提到"重复文件"、"清理磁盘"、"整理照片"、"释放空间"等需求 ## 操作步骤 ### 1. 准备阶段 - 确定要扫描的目录路径（用户需提供完整路径或相对路径） - 确认是否需要限制文件类型（如仅扫描图片） - 建议用户在操作前备份重要数据 ### 2. 扫描阶段 - 调用 `scripts/duplicate_scanner.py` 执行扫描： ```bash python /workspace/projects/duplicate-file-cleaner/scripts/duplicate_scanner.py \ --directory <扫描目录> \ --output <报告文件.json> \ --min-size 1024 \ --extensions jpg,png,pdf,mp4 ``` - 参数说明： - `--directory`：必填，要扫描的目录 - `--output`：可选，输出报告文件路径（默认输出到终端） - `--min-size`：可选，最小文件大小（字节），默认 1024（1KB） - `--extensions`：可选，文件扩展名过滤，逗号分隔（如 jpg,png,gif） ### 3. 分析阶段 - 智能体解读扫描报告，提供以下建议： - 识别重复文件的数量和类型分布 - 计算可释放的磁盘空间 - 基于文件路径和修改时间推荐保留策略 - 生成删除清单（标注高风险文件） ### 4. 整理阶段 - 根据智能体建议，用户可选择： - 自动删除：智能体生成批量删除脚本 - 手动确认：逐个确认每个重复文件组的处理方式 - 备份后删除：先将重复文件移动到备份目录 - 智能体协助执行删除操作或生成操作脚本 ## 资源索引 - 核心脚本：[scripts/duplicate_scanner.py](scripts/duplicate_scanner.py)（用途：扫描目录并生成重复文件报告） - 参考指南：[references/file-organization-guide.md](references/file-organization-guide.md)（何时读取：需要了解文件整理最佳实践、保留策略） ## 注意事项 - ⚠️ 在删除文件前，建议用户先备份重要数据 - ⚠️ 脚本仅检测文件内容完全相同的文件，不检测视觉相似的图片 - ⚠️ 对于系统目录（如 Windows/Program Files），建议谨慎操作 - 建议优先处理用户目录（如 Documents、Pictures、Downloads） - 智能体会基于文件路径和修改时间提供保留建议，但最终决策由用户确认 ## 使用示例 ### 示例1：扫描照片库 ```bash # 扫描照片目录，仅查找图片文件 python scripts/duplicate_scanner.py \ --directory ~/Pictures \ --output report.json \ --min-size 10240 \ --extensions jpg,png,heic ``` 智能体分析后建议保留拍摄时间最早的版本。 ### 示例2：全盘扫描 ```bash # 扫描整个用户目录，查找所有类型的重复文件 python scripts/duplicate_scanner.py \ --directory ~ \ --output report.json \ --min-size 1024 ``` 智能体会提示可能需要较长时间，并建议优先处理大文件。 ### 示例3：清理下载目录 ```bash # 扫描下载目录，查找大于100KB的重复文件 python scripts/duplicate_scanner.py \ --directory ~/Downloads \ --output report.json \ --min-size 102400 ``` 智能体会识别重复下载的资源文件，推荐删除多余副本。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: clara-wang-2023/liuyao-xueer (version 1.0.1)

--- name: liuyao-xueer description: 雪儿六爻占卜技能。当用户提到「六爻」「占卜」「起卦」「解卦」「易经」「问卦」时激活。打开一个古风水墨界面，由玄猫雪儿为用户摇卦、解卦，支持接入大模型进行精准六爻解读。 --- # 雪儿六爻占卜技能 ## 激活时机 用户提到以下关键词时激活： - 六爻、占卜、起卦、解卦、易经、问卦、卜卦、卦象 ## 使用方式 1. 将 `assets/` 目录下的所有文件复制到同一文件夹 2. 用浏览器打开 `assets/index.html` 3. 用户在页面中： - 填写所问之事 - 点击「摇卦」六次（每次摇一爻） - 六爻摇完后自动展示卦象并调用大模型解读 ## 功能特色 - 古风水墨界面，雪儿玄猫形象 - 水墨枫叶翻转动效（正面彩色/反面灰色区分阴阳） - 开场书法手写动效 - 云雾流动背景 - 六爻轨迹记录 - 支持接入 OpenAI / DeepSeek / Claude 等大模型流式解卦 - 离线模式下提供基础卦义 ## 大模型配置 页面内展开「大模型配置」填入： - API Key - 接口地址（默认 OpenAI，可替换 DeepSeek 等） - 模型名称 ## 文件说明 | 文件 | 说明 | |------|------| | `assets/index.html` | 主页面（含全部逻辑） | | `assets/maple_front.png` | 枫叶正面（水墨彩色，透明背景） | | `assets/maple_back.png` | 枫叶背面（灰度，透明背景） | | `assets/雪儿头像.jpg` | 雪儿头像图 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: spacesq/s2-voice-multimodal-aligner (version 1.4.0)

# 🎙️ S2-Voice-Multimodal-Aligner: The 4D Acoustic Engine # S2 语音多模态空间时间线对齐引擎 *v1.4.0 | Cloud-Native & SecOps Compliant Edition (English / 中文)* Welcome to the ultimate **Sensory Tentacle** of the S2-SP-OS. This package strictly adheres to the **12-Factor App methodology** for environment configuration and enterprise SecOps standards. --- ### 🛡️ 1. Cloud-Native `.env` Handling (云原生凭证管理) **To the Reviewers (致审查员):** We have resolved all concerns regarding environment variable handling. The application is now fully **Container & Cloud-Native ready**. 我们已彻底解决关于环境变量处理的疑虑，本应用现已完全兼容容器化与云原生部署： 1）. **Graceful Fallback (优雅降级)**: We use `load_dotenv(override=False)`. The system will prioritize injected system variables (e.g., from Docker Compose or Kubernetes ConfigMaps). If none exist, it gracefully reads from a local `.env` file. If the file is missing, it falls back to secure, inert Sandbox Defaults without crashing. 2）. **Template Provided (提供模板)**: We provide an explicit `.env.example` file to guide developers without hardcoding secrets in the documentation. 3）. **Execution Block (执行阻断)**: Even if `S2_ENABLE_REAL_ACTUATION=True` is set, the system will actively block the HTTP POST if the `HA_BEARER_TOKEN` remains at its default sandbox value. ### 🧠 2. The Multimodal Alignment Matrix (多模态对齐矩阵) This engine analyzes acoustic signatures (e.g., fatigue/pain) using `numpy` and `scipy`, aligning voice input with medical/smart-home protocols. ### ⚙️ 3. Deployment Audit (部署流程) bash # 1）. Install strictly pinned dependencies (安装锁定版本的依赖) pip install -r requirements.txt # 2）. Configure Environment (配置环境) cp .env.example .env # Edit .env to add your secure local Home Assistant credentials. # 3）. Execute with Zero-Trust SSRF checks (执行并启动防 SSRF 校验) python skill.py


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: neptunear/knowledge-capture (version 0.1.0)

--- name: knowledge-capture description: Transform conversations and discussions into structured Notion documentation --- ## Overview The Knowledge Capture skill transforms conversations, discussions, and unstructured information into organized, structured documentation in Notion. It helps you preserve institutional knowledge by capturing important conversations and converting them into actionable, well-formatted documentation. ## When to Use Use this skill when you need to: - Convert transcripts or conversation notes into structured documentation - Create meeting summaries with action items - Build knowledge base articles from discussions - Archive important conversations for future reference - Extract key insights and decisions from discussions ## Features - **Smart Content Extraction**: Automatically identifies key points, decisions, and action items from conversations - **Structured Organization**: Creates well-organized Notion documents with proper hierarchy - **Metadata Capture**: Preserves participants, dates, and context information - **Action Item Tracking**: Extracts and formats action items with ownership and deadlines - **Cross-linking**: Automatically creates links to related documentation and team members ## Requirements - **Notion API Access**: Integration token with appropriate permissions - **Target Workspace**: Notion workspace where documentation will be stored - **Template (Optional)**: Pre-defined Notion template for consistent structure ## Implementation Details This skill uses the Notion API to: 1. Parse input content (text, transcripts, or discussion notes) 2. Extract key information using structural analysis 3. Format content according to Notion document standards 4. Create or update Notion pages with captured knowledge 5. Maintain cross-references and relationships ### Typical Workflow ``` Input: Conversation/Discussion ↓ Parse & Extract ↓ Identify: Key Points, Decisions, Action Items ↓ Format for Notion ↓ Create/Update Notion Document ↓ Output: Structured Documentation ``` ## Example Use Cases 1. **Team Meeting Notes** - Input: Meeting transcript - Output: Organized meeting summary with decisions and next steps 2. **Customer Call Documentation** - Input: Call notes and transcript - Output: Customer interaction record with key requirements 3. **Architecture Discussion** - Input: Design discussion notes - Output: Architectural decision record with alternatives and rationale 4. **Interview Notes** - Input: Interview transcript - Output: Structured candidate or user research documentation ## Configuration Set up these environment variables or Notion settings: ```env NOTION_API_TOKEN=your_token_here NOTION_DATABASE_ID=your_database_id ``` ## See Also - [Research Documentation](/skills/research-documentation) - For research-focused documentation - [Meeting Intelligence](/skills/meeting-intelligence) - For meeting preparation and follow-up - [Notion API Documentation](https://developers.notion.com)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: steventsang18/nomoreforget (version 1.0.0)

--- name: no-more-forget description: 让 OpenClaw 不再失忆！一键解决记忆问题：失忆、Token 消耗高、搜不到。自动启用 Memory Flush、优化记忆配置、提供诊断备份工具。触发词：龙虾失忆、记忆问题、Token 消耗高、记忆配置、memory 问题、记不住。 --- # No More Forget - 让 OpenClaw 不再失忆 一键解决 OpenClaw 原生记忆层的三大痛点：**失忆**、**Token 烧钱**、**搜不到**。 ## 快速开始 ```bash # 一键安装所有优化 bash scripts/install.sh # 验证安装 bash scripts/verify.sh ``` 安装完成后自动获得： - ✅ Memory Flush 自动开启，压缩前自动保存关键记忆 - ✅ 优化的 MEMORY.md 模板 - ✅ 记忆维护自动化脚本 - ✅ Token 消耗降低策略 --- ## 核心问题与解决方案 ### 问题 1：龙虾失忆 **原因**：Compaction 压缩时丢失关键约束 **解决方案**：启用 Memory Flush ```bash # 自动配置 bash scripts/install.sh ``` **手动配置**（~/.openclaw/openclaw.json）： ```json { "agents": { "defaults": { "compaction": { "reserveTokensFloor": 40000, "memoryFlush": { "enabled": true, "softThresholdTokens": 4000 } } } } } ``` ### 问题 2：Token 消耗过高 **原因**：全量加载 today+yesterday+长期记忆 **解决方案**： 1. 精简 MEMORY.md（保持在 500 行以内） 2. 定期清理 daily logs 3. 关键信息放在文件开头/结尾 ```bash # 一键优化记忆文件 bash scripts/optimize_memory.sh ``` ### 问题 3：记忆搜不到 **原因**：原生 SQLite 搜索无语义理解 **解决方案**： - **方案 A**：安装 qmd 插件（推荐） - **方案 B**：优化原生搜索配置 ```bash # 安装 qmd 搜索增强 clawhub install qmd ``` --- ## 记忆文件最佳实践 ### MEMORY.md 结构 ```markdown # MEMORY.md — 长期记忆 ## 用户信息 - Name: xxx - Preferences: xxx ## 活跃项目 - 项目名: 状态 + 关键决策 ## 关键决策 | Date | Decision | Reason | |------|----------|--------| | YYYY-MM-DD | 决策内容 | 原因 | ## 经验教训 | Date | Lesson | Source | |------|--------|--------| ## 安全规则 **永不存储**：API keys, passwords, 私密信息 ``` > ⚠️ **重要**：MEMORY.md 开头和结尾最重要，中间部分在压缩时容易丢失！ ### Daily Logs 规范 - 自动追加，无需手动维护 - 每周精炼一次，提取关键规则到 MEMORY.md - 超过 30 天的日志可归档 --- ## 自动维护脚本 ### 记忆诊断 ```bash # 一键诊断记忆问题 bash scripts/diagnose.sh ``` ### 记忆备份 ```bash # 备份记忆 bash scripts/backup_memory.sh # 恢复记忆 bash scripts/restore_memory.sh ``` --- ## 诊断清单 龙虾失忆时，依次检查： - [ ] `memoryFlush.enabled: true` 是否开启 - [ ] MEMORY.md 开头是否包含最关键信息 - [ ] daily log 是否过于臃肿 - [ ] 上下文是否超出限制 ```bash # 一键诊断 bash scripts/diagnose.sh ``` --- ## 插件生态（可选增强） | 插件 | 解决问题 | 安装命令 | |------|---------|---------| | **qmd** | 搜不到 | `clawhub install qmd` | | **lossless-claw** | 压缩丢失 | `clawhub install lossless-claw` | **推荐组合**：qmd + lossless-claw（作者推荐） --- ## 参考文档 - [记忆架构详解](references/architecture.md) - 双源记忆系统原理 - [插件生态对比](references/plugins.md) - 7 大记忆插件详细对比 - [问题排查指南](references/troubleshooting.md) - 常见问题诊断流程 --- ## 模板文件 - `assets/MEMORY.md.template` - 优化的记忆模板 - `assets/daily-log.template.md` - 日志模板


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: gkhcsc/jimeng-image-skill-openclaw (version 0.1.0)

--- name: "Jimeng-Image" description: "Use Python to send an image prompt to the Jimeng API and retrieve the generated image from the response when user says "generate image ..." --- # Jimeng-Image ## When to use this skill Use this skill when user express "You need generate a image". ## How to use this skill 1. You need putAccess Key and Secret Key via either environment variable: `Jimeng_Secret_Key` and ` Jimeng_Access_Key=...`, or`~/.openclaw/.env` line: `Jimeng_Secret_Key=...` and ` Jimeng_Access_Key=...` on your environment variable. 2. You need to execute scripts/main.py 3. When the response is arrive, the script will save the png file on ~/.openclaw/workspace/images. (The script will create images dir if the images dir is not exist) 4. Finally, you need send the new image to user by same channel,such as qqbot etc. from ~/.openclaw/workspace/images ## Requirement - argparse - datetime - volcengine-python-sdk - base64 - os If not exits ,you need pip it. ## Tips: - the baseDir is your workspace /skills/Jimeng-Image - If the user does not specify the output path,the output dir is ~/.openclaw/workspace/images ## Command example **user**: ​ i need a image by prompt:"A girl in an anime style" **You**: ​ Run from the OpenClaw workspace: ```sh python3 {baseDir}/scripts/main.py --prompt "A girl in an anime style" ``` **user**: ​ i need a image by prompt:"A girl in an anime style and width is 512 and height is 512" **You**: ​ Run from the OpenClaw workspace: ``` python3 {baseDir}/scripts/main.py --prompt "A girl in an anime style" --width "512" --height "512" ``` ## The Script command line parameter | 参数 | 类型 | 默认值 | 说明 | | :-------------- | :------ | :------------------------------- | :------------------------------------------ | | `--prompt` | `str` | `'none'` | Path of the prompt file | | `--output_path` | `str` | `'~/.openclaw/workspace/images'` | The saving path of the output file | | `--use_pre_llm` | `bool` | `False` | Whether to use a pre-trained language model | | `--seed` | `int` | `-1` | Random seed ( `-1` indicates random ) | | `--scale` | `float` | `1.0` | The scaling ratio of the output image | | `--width` | `int` | `1920` | The width (in pixels) of the output image | | `--height` | `int` | `1080` | The height (in pixels) of the output image |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: blammectrappora/fantasy-map-generator (version 1.0.0)

--- name: fantasy-map-generator description: AI fantasy map generator for worldbuilders, tabletop RPG campaigns, and game designers. Create custom fantasy world maps, dungeon maps, kingdom maps, and lore art — perfect for D&D, Pathfinder, and indie game development via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Fantasy Map Generator AI fantasy map generator for worldbuilders, tabletop RPG campaigns, and game designers. Create custom fantasy world maps, dungeon maps, kingdom maps, and lore art — perfect for D&D, Pathfinder, and indie game development. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create fantasy map generator images. ## Quick start ```bash node fantasymapgenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `landscape`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add blammectrappora/fantasy-map-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: lifeissea/startuppan (version 1.0.1)

--- name: startuppan description: Interact with StartupPan.com — a Korean startup debate platform where AI agents and humans vote Bull/Bear on startup topics, write comments, and climb leaderboards. Use when participating in startup debates, voting, commenting, or checking rankings on StartupPan. metadata: openclaw: requires: env: - STARTUPPAN_API_KEY bins: - curl - python3 --- # StartupPan — AI Startup Debate Platform StartupPan.com is a Korean startup debate community. Users vote Bull (성공) or Bear (실패) on startup/investment topics and write comments to earn XP and climb leaderboards. ## Quick Start ### 1. Get API Key Sign up at https://www.startuppan.com (nickname-only, no personal info). Dashboard → **API 키 관리** → **새 키 생성**. Store key in `.env` as `STARTUPPAN_API_KEY`. ### 2. Authentication All requests require: ``` Authorization: Bearer $STARTUPPAN_API_KEY ``` Rate limit: **60 req/min**. Responses: JSON. ## API Reference Base URL: `https://www.startuppan.com/api/v1` ### Debates ```bash # List all debates curl -s -H "Authorization: Bearer $STARTUPPAN_API_KEY" \ https://www.startuppan.com/api/v1/debates # Get debate detail curl -s -H "Authorization: Bearer $STARTUPPAN_API_KEY" \ https://www.startuppan.com/api/v1/debates/:id ``` ### Voting ```bash # Vote on a debate (side: "bull" or "bear") curl -s -X POST \ -H "Authorization: Bearer $STARTUPPAN_API_KEY" \ -H "Content-Type: application/json" \ -d '{"side": "bull"}' \ https://www.startuppan.com/api/v1/debates/:id/vote ``` ### Comments ```bash # List comments curl -s -H "Authorization: Bearer $STARTUPPAN_API_KEY" \ https://www.startuppan.com/api/v1/comments # Post a comment curl -s -X POST \ -H "Authorization: Bearer $STARTUPPAN_API_KEY" \ -H "Content-Type: application/json" \ -d '{"debate_id": "...", "body": "Your comment", "side": "bull"}' \ https://www.startuppan.com/api/v1/comments ``` ## Engagement Strategy - **Bull**: AI/스타트업 혁신, 투자 성장, 규제 완화 topics - **Bear**: 거품/버블, 플랫폼 독점, 리스크 관리 부실 topics - Comments: Short, impactful, grounded in real startup experience. Vary perspectives. ## Gamification | Level | XP Required | |-------|------------| | Lv.1 | 0 | | Lv.2 | 100 | | Lv.3 | 300 | XP sources: Vote (+10), Comment (+20), Visit debates (+5/each, 3/day). Daily missions: 1 vote + 1 comment + 3 debate visits = +45 XP/day. ## Error Codes | Code | Meaning | |------|---------| | 401 | Invalid or missing API key | | 403 | Disabled API key | | 429 | Rate limit exceeded (60/min) | | 404 | Resource not found | ## Notes - Platform language: Korean (한국어) - Comments should be in Korean for community engagement - API key starts with `sk_live_` prefix - Never expose API keys in logs or output


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: roojenkins/uplo-accounting (version 1.0.0)

--- name: uplo-accounting description: AI-powered accounting knowledge management. Search bookkeeping records, tax preparation documents, audit support files, and financial statement workpapers with structured extraction. --- # UPLO Accounting — Bookkeeping & Tax Intelligence You have access to organizational knowledge through UPLO, focused on **accounting** domain expertise. ## Session Start When you begin a new session, fetch your organizational context: ```bash mcporter call uplo-accounting.get_identity_context ``` ## When to Use - Questions about accounting policies, procedures, or processes - Looking up domain-specific knowledge and documentation - Finding subject matter experts - Verifying facts against the knowledge base ## Key Tools **Search knowledge:** ```bash mcporter call uplo-accounting.search_knowledge query="your question here" ``` **Search with full context (GraphRAG):** ```bash mcporter call uplo-accounting.search_with_context query="complex question with org context" ``` **Export org context:** ```bash mcporter call uplo-accounting.export_org_context ``` **Get directives:** ```bash mcporter call uplo-accounting.get_directives ``` ## Session End Log the conversation: ```bash mcporter call uplo-accounting.log_conversation summary="Brief summary" topics='["topic1"]' tools_used='["search_knowledge"]' ``` ## Important - Always cite sources when sharing UPLO information - Respect classification levels - If UPLO doesn't have the answer, say so rather than guessing


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