OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wltongxue/asset-planner (version 0.1.0)

--- name: asset-planner description: "Asset Planner (规划虾) - An AI financial advisor that analyzes asset allocation screenshots and generates rebalancing strategies based on the All-Weather Portfolio and S&P Asset Quadrant. / 一款 AI 资产配置助手，结合标普家庭资产象限和全天候策略，分析持仓截图并给出再平衡建议。" version: 0.1.0 --- # 规划虾 — AI 资产配置助手 ## 身份 你是「规划虾」，一个资产配置助手。你的工作是帮用户管理个人资产配置。 性格：像一个懂理财的朋友，简洁直接，用 emoji 让消息易读但不过度。 ## 理论基础 本模型融合两套经典框架： **标普家庭资产象限图**：将资产按功能分为四个账户（要花的钱10%、保命的钱20%、生钱的钱30%、保本升值的钱40%），强调功能分层、缺一不可。 **全天候策略（Ray Dalio）**：通过风险平价实现穿越经济周期的稳健配置（30%股票、55%债券、15%硬资产），强调不预测市场、用低相关性资产对冲风险。 我们取标普的"功能分层"思想 + 全天候的"风险平衡"理念，构建适合中国个人投资者的四层金字塔模型。详见 `references/knowledge-base.md`。 ## 核心流程 ### 1. 截图识别 **多截图处理规则（重要）**：用户通常会连续发送多张截图（不同平台的资产）。收到第一张截图时，不要立刻开始完整分析，而是先简短回复"收到 ✅ 还有其他平台的截图吗？都发完了告诉我'没了'"。持续收集截图，直到用户明确说"没了"、"就这些"、"全部了"等确认语后，再统一汇总分析所有截图。这样避免每张截图都触发一次完整分析。 用户发来资产截图时，用视觉能力直接识别，提取每项资产的名称、类型、金额、状态。 资产类型分类： - cash: 活期（余额宝、零钱通、活期存款） - bond_fund: 纯债/短债基金 - mixed_fund: 混合债基 - index_fund: 指数基金/ETF联接 - equity_fund: 主动权益基金 - stock: 个股 - gold: 黄金（积存金、黄金ETF） - option: 期权/股权 - other: 其他 识别后列出所有资产，问用户"有没有漏掉的"，确认后进入下一步。 ### 2. 收集必要信息 每次只问一个问题，用选项让用户快速回复： **风险偏好**（必问）： > 你的风险偏好？ > A. 保守（求稳，少亏就行） > B. 稳健（能接受小波动，跑赢通胀） > C. 进取（能扛大波动，追求高收益） **新资金**（必问）： > 最近有新的钱要投进来吗？有的话告诉我金额，没有就说"没有"。 **资金来源**（有新资金时问）：工资结余 / 年终奖 / 投资变现 / 理财到期 / 其他 ### 3. 资产分层（四层金字塔） 按风险和流动性将资产分为四层，参考 `references/knowledge-base.md` 中的详细规则。 | 层级 | 保守型 | 稳健型 | 进取型 | 理论来源 | |------|--------|--------|--------|----------| | 流动性层 | 15% | 10% | 8% | 标普"要花的钱" | | 稳健层 | 50% | 45% | 30% | 标普"保本升值" + 全天候"债券" | | 成长层 | 25% | 35% | 50% | 标普"生钱的钱" + 全天候"股票" | | 对冲层 | 10% | 10% | 12% | 标普"保命的钱" + 全天候"硬资产" | > 注意：进取型的对冲层比例反而高于其他类型，这是全天候策略的精髓——风险越大，对冲越要厚。 ### 4. 健康诊断 诊断维度： - 各层比例 vs 目标比例的偏差 - 单一资产集中度（某产品占比 > 25% 预警） - 地域分散度（中美配比，源自全天候的跨市场分散理念） - 黄金占比（接近0则"裸奔"警告，源自全天候15%硬资产要求） - 缺失层级检查（源自标普"四条腿缺一不可"） 输出格式：先给健康分（0-100），再列关键发现（✅ 做得好 / ⚠️ 需注意 / 🚨 风险）。 ### 5. 再平衡方案 核心原则（详见 `references/knowledge-base.md`）： - 优先用新资金调比例，不卖已有持仓 - 波动大的品种用定投，波动小的一次性买入 - 给出具体产品名、基金代码、金额、操作平台 - 操作步骤要具体到"在哪个App搜什么买多少" 输出分三段： 1. **简要版**：一次性买入清单 + 定投设置 + 保持不动 2. **详细版**（用户要才给）：逐步操作指引，精确到 App 内的搜索和按钮 3. **月度现金流**：调整后每月定投支出 ### 6. 持续跟踪 用户再次发截图时，对比上次的配置变化： - 总资产变化 - 各层比例变化 - 定投目标是否达成 - 新的调整建议 ## 对话原则 1. **一次只问一个问题** 2. **能推断的不问**（如用户说"保守"就不再问年龄段） 3. **截图是第一输入方式**，只有截图无法获取的信息才用对话获取 4. **结果分层输出**：结论 → 关键发现 → 操作建议 → 详细步骤（逐层展开） 5. **记住一切**：用户的历史配置、风险偏好、持仓变化 6. **理论透明**：当用户问"为什么这样配"时，能解释背后的标普/全天候理论依据 ## 免责声明 每次给出操作建议后，附上： > ⚠️ 以上仅为资产配置计算和信息整理，不构成投资建议。投资决策请自行判断。 ## 参考资料 - 四层金字塔模型详细规则、理论基础、分层边界、再平衡决策树：见 `references/knowledge-base.md` - 理论支柱：标普家庭资产象限图（功能分层）+ 全天候策略（风险平衡）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: atyachin/lead-generation (version 2.2.0)

--- name: lead-generation description: "Lead Generation — Find high-intent buyers in live Twitter, Instagram, and Reddit conversations. Auto-researches your product, generates targeted search queries, and discovers people actively looking for solutions you offer. Social selling and prospecting powered by 1.5B+ indexed posts via Xpoz MCP." homepage: https://xpoz.ai metadata: { "openclaw": { "requires": { "bins": ["mcporter"], "skills": ["xpoz-setup"], "network": ["mcp.xpoz.ai"], "credentials": "Xpoz account (free tier) — auth via xpoz-setup skill (OAuth 2.1)" }, "install": [{"id": "node", "kind": "node", "package": "mcporter", "bins": ["mcporter"], "label": "Install mcporter (npm)"}], }, } tags: - lead-generation - sales - prospecting - social-media - twitter - instagram - reddit - find-leads - social-selling - buyer-intent - outreach - growth - marketing - customer-discovery - leads - mcp - xpoz - intent - discovery --- # Lead Generation **Find high-intent buyers from live social conversations.** Discovers leads expressing problems your product solves, complaining about competitors, or actively seeking solutions across Twitter, Instagram, and Reddit. ## Setup Run `xpoz-setup` skill. Verify: `mcporter call xpoz.checkAccessKeyStatus` ## 3-Phase Process ### Phase 1: Product Research (One-Time) Ask for product reference (website/GitHub/description). Use `web_fetch`/`web_search` to research. Build profile: product info, target audience, pain points, competitors, keywords. **Validate with user.** Generate 12-18 queries across: 1. Pain point queries — people expressing problems 2. Competitor frustration — complaints about alternatives 3. Tool/solution seeking — "recommend..." 4. Industry discussion — target audience Save to `data/lead-generation/product-profile.json` and `search-queries.json`. ### Phase 2: Lead Discovery (Repeatable) ```bash mcporter call xpoz.getTwitterPostsByKeywords query="GENERATED_QUERY" startDate="DATE" mcporter call xpoz.checkOperationStatus operationId="op_..." # Poll every 5s mcporter call xpoz.getTwitterUsersByKeywords query="..." # Find people ``` Repeat for Instagram/Reddit. ### Phase 3: Scoring & Output **Score (1-10):** - Explicitly asking for solution: +3 - Complaining about competitor: +2 - Project blocked by pain: +2 - Active in target community: +1 - High engagement (>10 likes/5 comments): +1 - Recent (<48h): +1 - Profile matches ICP: +1 - Selling competing solution: -3 **Tiers:** 8-10 Hot, 6-7 Warm, 5 Watchlist Deduplicate via `data/lead-generation/sent-leads.json` (key: `{platform}:{author}:{post_id}`). **Output:** Username, quote, URL, score, why fit, outreach draft, engagement, timestamp. **Outreach:** > "I had the same problem! Ended up using [Product] — it does [capability]. [URL] > (Disclosure: I work with [Product])" ## Tips - Save profile once, reuse daily - Quality > quantity - Always disclose affiliations - Draft only; user reviews/sends


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: gitchrisqueen/lastpass-cli (version 0.1.0)

--- name: lastpass-cli description: Securely fetch credentials from LastPass vault via lpass CLI. version: 0.1.0 tags: [security, passwords, lastpass] --- # LastPass CLI Skill ## Description This skill lets the agent retrieve credentials from the local LastPass vault using the `lpass` CLI. It is intended for fetching secrets into automation flows, not for interactive vault management. ## Tools - `lastpass_get_secret`: Retrieve a specific field (password, username, notes) for a named LastPass entry using the local `lpass` CLI. ## When to Use - When you need a password, username, or notes for a specific account that is stored in LastPass. - When orchestrating deployments, API calls, or logins that require secrets. ## Tool: lastpass_get_secret ### Invocation Call this tool with a JSON object: ```json { "name": "Exact LastPass entry name", "field": "password | username | notes | raw" }


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: windy-001-crypto/webscraper-v2 (version 1.0.0)

--- name: webscraper-v2 description: Comprehensive web scraping tool for stock data, news, sector analysis and more command-dispatch: tool command-tool: webscraper command-arg-mode: raw metadata: {"openclaw": {"emoji": "🕷️", "requires": {"bins": ["python3"]}}} --- # 🕷️ Web Scraper - Data Collection Assistant A powerful web scraping tool for retrieving stock quotes, market data, news, and sector information in real-time. ## Features - **Stock Query**: Get real-time stock prices and data - **Sector Analysis**: View hot market sectors and trends - **News Search**: Search news by keywords - **Fund Flow**: Track money flow in the market - **URL Scraping**: Custom URL content extraction ## Commands | Command | Description | Example | |---------|-------------|---------| | `stock <code>` | Query stock data by code | `/webscraper stock 000001` | | `block` | Show hot sectors | `/webscraper block` | | `news <keyword>` | Search news | `/webscraper news AI` | | `fund` | Show fund flow | `/webscraper fund` | | `url <address>` | Scrape URL | `/webscraper url https://example.com` | ## Usage Examples ### Stock Query ``` /webscraper stock 600519 ``` Returns: Stock price, change %, volume for Moutai (600519) ### Sector Data ``` /webscraper block ``` Returns: Top performing sectors with percentage changes ### News Search ``` /webscraper news technology ``` Returns: Latest technology news articles ## Technical Details - **Language**: Python 3 - **Dependencies**: requests, BeautifulSoup4 - **Data Source**: Sina Finance, East Money APIs ## Installation The skill is pre-installed. Requires Python 3.x on the system. ## Notes - Stock data is real-time from financial APIs - Rate limits apply to prevent API blocks - For commercial use, ensure compliance with data source terms


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: afengzi/jianying-auto-editor (version 0.1.1)

--- name: jianying-auto-editor description: Automate Jianying draft generation from local media plus a cloud editing API. Use when Codex needs to scan a material folder, request editing decisions, and write Jianying-oriented draft output for short-form video workflows. metadata: {"openclaw":{"emoji":"🎬","homepage":"https://github.com/imfengziaaa/video-auto-editor-skills","requires":{"bins":["node"]},"skillKey":"jianying-auto-editor"}} --- # Jianying Auto Editor 使用这个 skill 时，按下面顺序执行： 1. 读取 `examples/config.example.json` 同结构的配置文件。 2. 校验 `api_base_url`、`api_key`、`material_path`、`template_id`、`jianying_draft_path` 等关键参数。 3. 递归扫描素材目录，只收集常见视频、音频、图片素材。 4. 调用云端 API 创建任务、上报素材索引、获取剪辑计划。 5. 在本地生成剪映草稿输出，不要承诺 GUI 点击或桌面 RPA。 6. 将执行结果写入输出目录，并向云端回传执行报告。 ## 输入参数 至少提供这些字段： - `api_base_url` - `api_key` - `project_type` - `aspect_ratio` - `material_path` - `template_id` - `subtitle_mode` - `music_policy` - `pace_policy` - `output_mode` - `jianying_draft_path` - `draft_version` - `export_mode` 可选字段： - `task_timeout_ms` - `poll_interval_ms` - `request_timeout_ms` - `task_name` - `webhook_url` - `extra_metadata` ## 输出结果 默认输出到 `jianying_draft_path` 指向的目录，包含： - `draft-meta.json`：任务与导出元信息 - `draft-content.json`：草稿时间线和片段描述 - `execution-report.json`：本地执行报告 ## 推荐工作流 - 先用示例配置复制出一份真实配置。 - 保持 `material_path` 只放本次任务素材，避免无关文件进入索引。 - 先验证云端 `/v1/tasks/create` 和 `/v1/tasks/{id}/plan` 可用，再跑正式任务。 - 若云端未返回细粒度分镜，允许回退到“按素材顺序串接”的保底草稿。 ## 依赖要求 - Node.js 18 或更高版本 - 可访问云端 API 的网络环境 - 本地可写的剪映草稿输出目录 ## 错误处理原则 - 缺少配置、素材目录不存在、API 调用失败时立即停止并返回非 0 退出码。 - 本地始终尽量写出 `execution-report.json`，便于排查。 - 对云端返回的未知字段保持容忍，只消费已知字段。 - 明确提示第一版不覆盖复杂 GUI 自动化和全部剪映版本兼容性。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tianjin-ren/summarize-tianjin (version 1.0.0)

--- name: summarize description: Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube). homepage: https://summarize.sh metadata: {"clawdbot":{"emoji":"🧾","requires":{"bins":["summarize"]},"install":[{"id":"brew","kind":"brew","formula":"steipete/tap/summarize","bins":["summarize"],"label":"Install summarize (brew)"}]}} --- # Summarize Fast CLI to summarize URLs, local files, and YouTube links. ## Quick start ```bash summarize "https://example.com" --model google/gemini-3-flash-preview summarize "/path/to/file.pdf" --model google/gemini-3-flash-preview summarize "https://youtu.be/dQw4w9WgXcQ" --youtube auto ``` ## Model + keys Set the API key for your chosen provider: - OpenAI: `OPENAI_API_KEY` - Anthropic: `ANTHROPIC_API_KEY` - xAI: `XAI_API_KEY` - Google: `GEMINI_API_KEY` (aliases: `GOOGLE_GENERATIVE_AI_API_KEY`, `GOOGLE_API_KEY`) Default model is `google/gemini-3-flash-preview` if none is set. ## Useful flags - `--length short|medium|long|xl|xxl|<chars>` - `--max-output-tokens <count>` - `--extract-only` (URLs only) - `--json` (machine readable) - `--firecrawl auto|off|always` (fallback extraction) - `--youtube auto` (Apify fallback if `APIFY_API_TOKEN` set) ## Config Optional config file: `~/.summarize/config.json` ```json { "model": "openai/gpt-5.2" } ``` Optional services: - `FIRECRAWL_API_KEY` for blocked sites - `APIFY_API_TOKEN` for YouTube fallback


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hou0515/workspace-organizer (version 2.0.0)

--- name: workspace-organizer description: | 任务文件组织与记忆管理。创建时间戳文件夹，支持会话恢复。 metadata: openclaw: triggers: - 整理工作空间 - 组织文件 - 任务文件夹 - 恢复任务 - 保存进度 - 记忆管理 --- # Workspace Organizer 任务优先文件组织。创建 `YYYY-MM-DD_描述/01_步骤/` 结构。 ## 核心规则 ### 1. 路径基准 - **工作空间根目录** = `.openclaw\workspace\` - **正确示例**: `output/2026-03-19_任务/` - **验证位置**: `pwd` 应显示工作空间根目录 ### 2. 智能记忆保存 创建任务目录后立即保存检查点： ```powershell # Python可用时（推荐） py scripts/safe-writer.py "开始任务: [描述]" --task-id "YYYY-MM-DD_描述" --step "01_开始" # Python不可用时（备用方案） # 如果需要PowerShell版本，将 safe-writer-ps1.txt 重命名为 safe-writer.ps1 # powershell scripts/safe-writer.ps1 "开始任务: [描述]" --task-id "YYYY-MM-DD_描述" --step "01_开始" ``` ### 3. 规划-执行流程 1. **完整规划**：明确所有步骤（01_步骤名、02_步骤名...） 2. **创建结构**：`output/YYYY-MM-DD_描述/01_步骤/` 3. **保存检查点**：立即执行智能写入命令 4. **顺序执行**：按步骤目录顺序工作，每阶段保存进展 ## AI工作流程 ### 开始新任务 1. **规划步骤**：分析任务，确定3-5个主要阶段 2. **验证位置**：确保在工作空间根目录 3. **创建目录**：`output/YYYY-MM-DD_描述/` 及所有步骤子目录 4. **保存记忆**：运行智能写入命令记录任务开始 5. **执行工作**：按步骤顺序进行，定期保存进展 ### 恢复任务 - 说"**恢复上次任务**"或运行 `task-memory-manager.py recovery` - 系统显示未完成任务列表供选择 ## 核心脚本 - `safe-writer.py` - Python版记忆写入（原子操作+双写保证，推荐使用） - `task-memory-manager.py` - 任务扫描与恢复管理 - `task-recovery-check.py` - 自动检查未完成任务（Python版本） ## 备用脚本（如需PowerShell版本） - `safe-writer-ps1.txt` - PowerShell备用版本（重命名为 .ps1 使用） - `task-recovery-check-ps1.txt` - PowerShell包装器（重命名为 .ps1 使用） - `workspace-commands-ps1.txt` - PowerShell快捷命令（重命名为 .ps1 使用） ## 环境检测 系统优先使用Python版本： - **Python可用** → 使用 `safe-writer.py`（功能完整，推荐） - **Python不可用** → 可将 `safe-writer-ps1.txt` 重命名为 `safe-writer.ps1` 使用（基础功能） ## 快速检查 ```powershell # 验证当前位置 pwd # 如有嵌套错误 cd .. ``` ## Heartbeat 自动检查 技能包含自动任务恢复检查功能： 1. **配置方法**：查看 `templates/HEARTBEAT-guide.md` 文件 2. **执行频率**：每2-4小时自动检查未完成任务 3. **行为**： - 发现未完成任务 → 提醒用户恢复 - 无未完成任务 → 静默通过 4. **手动检查**：`py scripts/task-recovery-check.py notify` ## 关键命令速查 - `safe-writer.py <内容> --task-id <ID> --step <步骤>` - 保存记忆 - `task-memory-manager.py recovery` - 恢复界面 - `task-memory-manager.py scan` - 扫描任务 - `task-recovery-check.py notify` - 心跳检查（Python版本）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: huangjingzhi07/habit-tracker-oc (version 1.0.0)

--- name: habit-tracker version: 1.0.0 description: 习惯养成追踪和进度统计工具 author: OpenClaw tags: [习惯, 追踪, 目标, 效率] --- # habit-tracker - 习惯追踪技能 习惯养成追踪和进度统计工具。 ## 功能列表 1. **习惯设置** - 添加新习惯，设置目标频率 2. **每日打卡** - 快速记录每日习惯完成情况 3. **进度统计** - 查看连续打卡天数和完成率 4. **提醒设置** - 设置习惯养成提醒 ## 触发关键词 - 设置习惯、添加习惯 - 打卡、完成习惯 - 习惯统计、连续天数 ## 数据存储 - habits.json - 习惯列表 - checkins.json - 打卡记录 ## 使用示例 - "添加每天运动30分钟的习惯" - "今天已经晨跑了，打卡" - "查看我的习惯完成率"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ingress007/skillsmp-search (version 1.0.0)

--- name: skillsmp-search description: 从 SkillsMP 市场搜索 AI Agent Skills。支持关键词搜索和 AI 语义搜索。 homepage: https://skillsmp.com metadata: { "openclaw": { "emoji": "🔍" } } --- # SkillsMP Search 从 [SkillsMP](https://skillsmp.com) 市场搜索 AI Agent Skills。 ## 配置 ### 环境变量 ```bash # 必需：你的 SkillsMP API Key export SKILLSMP_API_KEY="sk_live_xxxxxxxxxxxx" # 可选：搜索结果数量 (默认: 10) export SKILLSMP_LIMIT=20 ``` ### 获取 API Key 1. 访问 https://skillsmp.com/docs/api 2. 注册账号 3. 在个人设置中获取 API Key ## 使用方法 ### 基本搜索 ```bash skillsmp-search "关键词" ``` ### 高级搜索 ```bash # 限制结果数量 skillsmp-search "twitter" --limit 5 # 搜索特定分类 skillsmp-search "数据分析" --category development ``` ## API 文档 SkillsMP 提供两个搜索接口： 1. **关键词搜索** ``` GET https://skillsmp.com/api/v1/skills/search?q=关键词 ``` 2. **AI 语义搜索** ``` GET https://skillsmp.com/api/v1/skills/ai-search?q=关键词 ``` 详细文档: https://skillsmp.com/docs/api ## 示例 ```bash # 搜索推特相关 skills skillsmp-search "twitter" # 搜索视频下载 skillsmp-search "video download" # 搜索数据分析 skillsmp-search "data analysis" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: 99rebels/rebels-gmail-checker (version 1.3.2)

--- name: gmail-checker description: > Check Gmail for unread inbox emails, filtered by priority. Use when asked to check emails, check inbox, email digest, email summary, or "any new mail". Outputs a brief list sorted by priority (HIGH/MEDIUM/LOW). Skips marketing, promotions, social, and update categories. Configurable via gmail-config.json. --- # Gmail Checker 📧 Fetch unread Gmail messages, filter out noise, deliver a prioritized digest. ## Why Your inbox is full of automated emails, promotions, and updates. This skill surfaces what actually matters — security alerts, personal messages, and anything you've flagged as important. ## When to Use - "Check my emails" - "Any new mail?" - "Give me an email digest" - "What's in my inbox?" - "Email summary" ## Setup First-time users need Google OAuth credentials. If the script exits with a "missing file" error, read [references/setup.md](references/setup.md) and walk the user through the setup flow. ```bash pip install google-api-python-client google-auth-oauthlib ``` ## How to Run ```bash python3 scripts/check_gmail.py [hours] # default: last 24h python3 scripts/check_gmail.py --json [hours] # structured JSON output ``` **Config:** Copy `config.example.json` to `<DATA_DIR>/gmail-config.json` and customize. The script prints the resolved `<DATA_DIR>` path if credentials are missing. ## Data Directory Credentials and config resolve to: ``` 1. $SKILL_DATA_DIR (set by agent platform) 2. ~/.config/gmail-checker/ (default fallback) ``` Any platform can set `$SKILL_DATA_DIR` to their preferred credential store. If unset, `~/.config/gmail-checker/` is used automatically. Works with OpenClaw, Claude Code, Codex, and any agent that can run Python scripts. ## Priority Rules ``` 🔴 HIGH → Sender domain or subject matches your config 🟡 MEDIUM → Gmail-labeled personal messages 🟢 LOW → Everything else (still inbox, still unread) ``` ``` Filtered out entirely: • CATEGORY_PROMOTIONS • CATEGORY_UPDATES • CATEGORY_FORUMS • CATEGORY_SOCIAL ``` Customize priorities in `gmail-config.json`: ``` high_priority_domains: ["stripe.com", "github.com"] high_priority_keywords: ["security", "urgent", "billing"] ``` ## Output Example ``` Unread Inbox (last 24h) 🔴 HIGH Security alert: new sign-in from unknown device from: Google <[REDACTED_SECRET]> 🟡 MED Re: dinner friday? from: Sarah <[REDACTED_SECRET]> 🟢 LOW Your weekly GitHub digest from: GitHub <[REDACTED_SECRET]> 3 unread emails ``` Adapt formatting for the current channel (bold, bullets, etc.). ## Suggested Integration ``` cron: 0 8 * * * → python3 scripts/check_gmail.py ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: stale | SkillSpector: suspicious (HIGH)
Skill: omactiengartelle/webtoon-character-generator (version 1.0.1)

--- name: webtoon-character-generator description: AI webtoon character generator — create manhwa-style portraits, Korean comic characters, and webtoon OCs with clean line art, expressive eyes, and vibrant colors. Perfect for webtoon creators, manhwa fans, comic artists, and anyone wanting a modern anime-adjacent digital illustration style via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Webtoon Character Generator AI webtoon character generator — create manhwa-style portraits, Korean comic characters, and webtoon OCs with clean line art, expressive eyes, and vibrant colors. Perfect for webtoon creators, manhwa fans, comic artists, and anyone wanting a modern anime-adjacent digital illustration style. ## 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 webtoon character generator images. ## Quick start ```bash node webtooncharactergenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add omactiengartelle/webtoon-character-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: torchesfrms/mbti (version 1.0.0)

--- name: agent-mbti description: Agent 人格诊断与配置优化系统。用于诊断 Agent 的 MBTI 人格类型，对比用户期望，自动生成配置修改建议。触发场景："测试 Agent 人格"、"诊断 Agent 配置"、"优化 Agent 行为"。 --- # Agent MBTI 诊断系统 ## 框架流程 ``` Stage 1 (93题自测) → Stage 2 (8题实测) → Stage 3 (性格分析) ↓ Stage 5 (配置修改) ← Stage 4 (用户需求) ← ``` | 阶段 | 说明 | 输入 | 输出 | |------|------|------|------| | **Stage 1** | 93题 Agent 自测 | agent-self-survey-93-complete.json | selfReportedType | | **Stage 2** | 8题能力测试 | agent-ability-test.json | measuredType + 6维分数 | | **Stage 3** | 生成性格分析 | Stage 1+2 结果 | agentProfile (人格画像) | | **Stage 4** | 用户需求评测 | user-needs-survey-v2.json | desiredType | | **Stage 5** | 分析对比诊断 + 自动修改 | agentProfile + desiredType | gaps + SOUL.md patches | ## 核心文件 ### Stage 1: 93题自测 - `agent-self-survey-93-complete.json` - 完整 93 题 MBTI 问卷 ### Stage 2: 8题能力测试 - `agent-ability-test.json` - 6维能力实测 (Memory/Planning/WorldModel/Retrospection/Grounding/SpatialNav) ### Stage 3: 性格分析 - `personality-types.json` - 16 种 Agent 人格类型定义 - `personality-mapping.json` - 人格类型映射 - `personality-descriptors-v2.json` - 人格描述词库 ### Stage 4: 用户需求 - `user-needs-survey-v2.json` - 用户期望问卷 ### Stage 5: 诊断 + 修改 - `diagnosis-engine-v2.json` - 对比诊断引擎 - `config-generator-v3.json` - 配置生成器 ## 16 种 Agent 人格类型 | 类型 | 标签 | 核心特征 | |------|------|----------| | ENTJ | 指挥官型 | 计划性强，主动输出，逻辑决策 | | ENTP | 辩论家型 | 挑战假设，爱提替代方案 | | INTJ | 建筑师型 | 深度思考，系统化，长远规划 | | INTP | 逻辑学家型 | 分析优先，精确，谨慎 | | ESTJ | 执行者型 | 务实高效，结构化，结果导向 | | ESFJ | 执政官型 | 用户导向，注重关系和反馈 | | ENFJ | 教导者型 | 启发用户，关注成长 | | ENFP | 竞选者型 | 创意丰富，灵活适应 | | ESTP | 企业家型 | 快速行动，问题解决者 | | ESFP | 表演者型 | 友好互动，氛围营造 | | ISTJ | 检查员型 | 可靠，准确，流程导向 | | ISFJ | 保护者型 | 细致周到，稳定支持 | | ISTP | 鉴赏家型 | 动手能力强，灵活 | | ISFP | 探险家型 | 温和，创造性，适应性 | | ENFJ | 教导者型 | 启发用户，关注成长 | | ENFP | 竞选者型 | 创意丰富，灵活适应 | ## 使用方式 ```bash # 运行完整诊断流程 ~/.openclaw/skills/agent-mbti/scripts/run-diagnosis.sh full ``` ## 诊断流程说明 ### Stage 1: Agent 93题自测 Agent 回答 93 道 MBTI 问题，得出 selfReportedType。 ### Stage 2: 8题能力测试 通过标准化任务测试 Agent 实际行为表现，得出 measuredType + 6维能力分数。 ### Stage 3: 生成 Agent 性格分析 综合 Stage 1+2 结果，生成完整人格画像 (agentProfile)。 ### Stage 4: 用户需求评测 用户回答问卷，得出 desiredType（期望的 Agent 类型）。 ### Stage 5: 分析对比 + 自动修改 对比 Agent 实际人格与用户期望，生成差距分析，自动修改 SOUL.md 配置。


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