OpenClaw/clawhub-security-signals huggingface dataset:
Skill: furukama/context-viz (version 1.0.0)

--- name: context-viz description: Visualize the current context window usage — token estimates per component (system prompt, tools, workspace files, messages, free space). Use when the user asks about context size, token usage, context breakdown, "how full is the context", or wants to see what's consuming their context window. --- # Context Visualization Estimate and display a breakdown of the current context window usage. ## How It Works Run the bundled script to estimate token counts for workspace files: ```bash python3 scripts/estimate_tokens.py /path/to/workspace ``` The script counts characters in known workspace files and estimates tokens (~4 chars/token). Then call `session_status` to get the actual context usage from OpenClaw. ## Generating the Visualization 1. Run `session_status` to get: model, context used/total, compactions 2. Run `scripts/estimate_tokens.py <workspace_path>` to estimate file token sizes 3. Estimate message tokens: `context_used - system_overhead - file_tokens` 4. Present the breakdown using the format below ## Output Format Use a monospace block with bar chart. Adapt the bar lengths proportionally. ``` 📊 Context Usage <model> • <used>k/<total>k tokens (<pct>%) Component Tokens % ───────────────────────────────────────────── ⚙️ System prompt + tools ~Xk X% ░░ 📋 AGENTS.md ~Xk X% ░ 👻 SOUL.md ~Xk X% 👤 USER.md ~Xk X% 🔧 TOOLS.md ~Xk X% ░ 💓 HEARTBEAT.md ~Xk X% 🧠 MEMORY.md ~Xk X% ░ 🪪 IDENTITY.md ~Xk X% 💬 Messages ~Xk X% ░░░░░░░░░░░░ 📭 Free space ~Xk X% ░░░░░ ───────────────────────────────────────────── ``` Use ░ blocks: 1 block per ~2% of total context. Round to nearest block. ## Memory Inventory (not in context) Below the context chart, add a **Memory on Disk** section showing what's stored in `memory/` — grouped by category. These files are NOT loaded into context but represent the agent's total knowledge base. ``` 💾 Memory on Disk (not in context) Category Files Tokens Size ────────────────────────────────────────────────── 📰 chinese-ai-digests 12 ~23k 92KB 📁 other 11 ~12k 46KB 📅 daily-notes 9 ~5k 17KB 🗃️ zettelkasten 8 ~4k 15KB 💼 linkedin 2 ~1k 5KB ────────────────────────────────────────────────── Total: 42 ~44k 177KB ``` The script auto-categorizes files by directory or filename pattern. ## Notes - Token estimates use ~4 chars/token (rough average for English/mixed content) - System prompt + tools overhead is estimated at ~8-10k tokens for a typical OpenClaw setup - Message tokens are the remainder after subtracting files + system overhead - Memory files are informational only — they show what the agent has accumulated - For Discord/WhatsApp: skip markdown tables, use the block format above


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: fenccerece/superpowers-openclaw (version 1.0.0)

--- name: superpowers-open description: > Complete Superpowers methodology for OpenClaw. A 12-skill collection that enforces design-before-code, TDD, systematic debugging, verification, and structured code review. Use when you want rigorous AI-assisted development workflow. metadata: openclaw: emoji: "⚡" homepage: https://github.com/superpowers-open/superpowers-open --- # SuperpowersOpen Superpowers 方法论在 OpenClaw 平台上的完整技能集合。12 个技能协同工作，形成端到端的严格开发工作流。 ## How This Collection Works This is a **skill collection**. The root SKILL.md acts as the manifest. Individual skills live in subdirectories and are auto-discovered by OpenClaw via their `description` fields. ## Skills ### Entry Point - **using-superpowers-open** — Tool mapping, trigger coordination, instruction priority (always active) ### Workflow Chain - **brainstorming** — Design-first: no code before design approval - **writing-plans** — Decompose designs into bite-sized implementation tasks - **executing-plans** — Load plan, execute tasks inline, verify completion - **finishing-a-development-branch** — Structured merge/PR/keep/discard workflow ### Practice Disciplines - **test-driven-development** — RED-GREEN-REFACTOR: test first, watch it fail, minimal code - **systematic-debugging** — 4-phase debugging: root cause → pattern → hypothesis → fix - **verification-before-completion** — Evidence before claims, always - **receiving-code-review** — Verify before implementing, no performative agreement - **requesting-code-review** — 5-dimension self-review checklist - **writing-skills** — TDD methodology applied to skill documentation - **using-git-worktrees** — Isolated git worktree workspaces ## Installation ```bash cp -r superpowers-open ~/.openclaw/skills/ ``` Restart OpenClaw Gateway. All 12 skills are auto-discovered. ## Requirements - OpenClaw (any version supporting SKILL.md format) - No additional dependencies ## License MIT-0 ## Credits Adapted from [obra/superpowers](https://github.com/obra/superpowers) methodology.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: dej4vu/bing-search-cn (version 1.0.0)

--- name: bing-search-cn description: 使用必应中文搜索引擎进行网络搜索和网页抓取 metadata: {"openclaw": {"requires": {"bins": ["node"]}, "emoji": "🔍"}} --- # bing-search-cn 技能 使用必应中文搜索引擎进行网络搜索和网页抓取。 ## 工具 ### 1. bing_search 必应搜索引擎搜索。 **参数：** - `query` (必填): 搜索关键词 - `count` (可选): 返回结果数量，默认10条，最多50条 - `offset` (可选): 起始偏移，用于分页 **返回：** - 搜索结果总数 - 每条结果的标题、链接、摘要、网站名称 ### 2. bing_fetch 抓取网页内容（自动跳过黑名单网站）。 **参数：** - `url` (必填): 要抓取的网页地址 **返回：** - 网页标题 - 清理后的正文内容 ### 黑名单 自动过滤的网站： - 知乎 (zhihu.com) - 小红书 (xiaohongshu.com) - 微博 (weibo.com) - 微信公众号 (weixin.qq.com) - 抖音/TikTok (douyin.com, tiktok.com) - B站 (bilibili.com) - CSDN (csdn.net) ## 使用示例 ``` "搜索人工智能最新进展" "搜索Python教程，返回20条" "抓取这个网页 https://example.com" ``` ## 限制 - 需要能访问 cn.bing.com - 建议每次搜索间隔1-2秒 - 搜索请求直接发送到必应，不存储日志 - 部分网站有反爬限制 ## 技能路径 技能脚本位于: `{baseDir}/bing-search.js` ## 调用方式 通过 exec 工具调用: ``` node ~/.openclaw/skills/bing-search-cn/bing-search.js search "关键词" 数量 偏移 node ~/.openclaw/skills/bing-search-cn/bing-search.js fetch "网址" ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wanxing0724/doubao-image-openclaw (version 1.0.4)

--- name: doubao-image description: 使用火山引擎豆包模型生成图片。通过火山引擎豆包图片生成 API 创建图片。支持自定义提示词、尺寸、模型等参数。使用方式：生图：一只可爱的小猫。 --- # doubao-image - 豆包图片生成 使用火山引擎豆包模型生成图片。 ## 简介 通过火山引擎豆包图片生成 API 创建图片。支持多模型自动切换、URL 输出等功能。 ## 环境变量 | 变量名 | 必填 | 说明 | |--------|------|------| | `VOLCENGINE_IMAGE_API_KEY` | Yes | 豆包 API Key | | `DOUBAO_IMAGE_OUTPUT_DIR` | No | 图片输出目录（默认：workspace/downloads/images） | **Get API Key**: 1. Login https://console.volces.com/ 2. Go to API Key management 3. Copy your API Key ## 使用方式 ``` 生图：一只可爱的小猫 ``` ### 命令行参数 | 参数 | 说明 | 默认值 | |------|------|--------| | prompt | 图片描述 | (必填) | | --model | 使用的模型 | doubao-seedream-4-0-250828 | | --size | 图片尺寸 | 1024x1024 | | --num | 生成数量 | 1 | | --url-only | 仅返回 URL，不下载 | false | ### 支持的模型（自动切换） 按优先级排序，优先使用第一个，失败则自动尝试下一个： 1. `doubao-seedream-4-0-250828` (默认) 2. `doubao-seedream-4-5-251128` 3. `doubao-seedream-5-0-260128` 4. `doubao-seedream-3-0-t2i-250415` ### 支持的尺寸 - 1024x1024 (默认) - 1280x720 - 720x1280 - 1024x768 - 768x1024 ## 输出格式 ### 控制台输出 ``` ================================================== Prompt: a cute kitten Size: 1024x1024 Num: 1 ================================================== Trying model: doubao-seedream-4-0-250828 [OK] Model doubao-seedream-4-0-250828 success ================================================== Success! Model: doubao-seedream-4-0-250828 URL: https://... ================================================== Downloading to: C:\Users\zcf\.openclaw\workspace\downloads\images\doubao_1234567890.jpeg [OK] Saved: C:\Users\zcf\.openclaw\workspace\downloads\images\doubao_1234567890.jpeg Size: 256.5 KB [FILE_PATH] C:\Users\zcf\.openclaw\workspace\downloads\images\doubao_1234567890.jpeg ``` ### 特殊标记 - `[URL_ONLY] <url>` - 仅生成 URL 时的输出 - `[FILE_PATH] <path>` - 文件保存路径 ## 输出目录 图片自动保存到 `downloads/images/` 目录。 可通过环境变量自定义： ```bash # Windows set DOUBAO_IMAGE_OUTPUT_DIR=D:\my_images # Linux/Mac export DOUBAO_IMAGE_OUTPUT_DIR=/path/to/images ``` ## 重要说明 - **API Key**: 从 `VOLCENGINE_IMAGE_API_KEY` 环境变量读取 - **Multi-model fallback**: 如果默认模型失败，会自动尝试备选模型 - **URL Output**: 生成后立即输出图片 URL ## 错误处理 - 如果所有模型都失败，会显示具体错误信息 - 如果下载失败，仍会输出 URL 供手动下载


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: oschina/gitee-daily-digest (version 1.0.0)

--- name: daily-digest description: Use this skill when the user asks for a daily summary, today's to-dos, "what's on my plate today", "daily digest", "daily-digest", "show my notifications", "pending PRs and issues". Requires Gitee MCP Server to be configured. version: 1.0.0 requires: mcp-servers: - gitee --- > **Note**: If you have `mcporter` installed locally, you should use `mcporter` to invoke the MCP tool instead of directly calling the MCP tool. The `mcporter` CLI provides a more convenient interface and better error handling. # Daily Digest via Gitee MCP Aggregate unread notifications, pending PRs, and open Issues from Gitee to generate a daily work summary. ## Prerequisites - Gitee MCP Server configured (tools: `list_user_notifications`, `list_repo_pulls`, `list_repo_issues`, `get_user_info`) - Optional: user can provide a list of repositories to focus on (otherwise inferred from notifications) ## Steps ### Step 1: Get Current User Info Use `get_user_info` to retrieve the currently authenticated user's information, used for filtering (e.g., only show tasks assigned to me). ### Step 2: Fetch Unread Notifications Use `list_user_notifications` to get recent notifications: - Filter for unread notifications - Categorize by type: PR-related, Issue-related, @mentions, comment replies ### Step 3: Fetch Pending PRs For repositories the user cares about (extracted from notifications or specified by the user), use `list_repo_pulls`: - Filter for `state=open` PRs - Focus on: - PRs assigned to me for review - My own PRs waiting for review - PRs with new comments that need a reply ### Step 4: Fetch Open Issues Use `list_repo_issues`: - Filter for `state=open` Issues assigned to me - Filter for open Issues I created ### Step 5: Generate Daily Digest ``` # Daily Work Digest 📅 [date] 👤 [username] --- ## 📬 Unread Notifications ([N]) ### @Mentions - [repo] [Issue/PR title] - [notification summary] ### PR Comments - [repo] PR #N [title] - [comment summary] ### Issue Updates - [repo] Issue #N [title] - [update summary] --- ## 🔀 Pending PRs ### Needs My Review | Repo | PR | Author | Updated | |------|----|--------|---------| | [repo] | #N [title] | [@author] | [time] | ### My PRs Awaiting Review | Repo | PR | Status | Updated | |------|----|--------|---------| | [repo] | #N [title] | [Awaiting review / Has new comments] | [time] | --- ## 📋 My Open Issues | Repo | Issue | Priority | Status | |------|-------|----------|--------| | [repo] | #N [title] | P1 | In progress | --- ## Today's Suggestions **Handle first:** 1. [Most urgent item, e.g., a review request blocking someone else] 2. [Second priority item] **Can defer:** - [Low priority items] ``` If there is nothing pending: ``` # Daily Work Digest 📅 [date] All clear! No pending PRs, Issues, or unread notifications. ``` ## Notes - The digest focuses on information aggregation, not deep analysis - If there are many repositories, prioritize time-sensitive items - Notifications may span multiple repositories — group them clearly


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yzswk/a-share-portfolio (version 1.0.0)

--- name: a-share-portfolio description: A股投资组合管理/持仓分析。当用户说"组合分析"、"持仓分析"、"portfolio"、"我的持仓怎么样"、"帮我看看组合"、"仓位管理"、"组合优化"、"风险分析"、"再平衡"、"行业暴露"时触发。对用户提供的持仓组合进行全面分析，包括持仓集中度、行业/风格暴露、风险指标、收益归因、再平衡建议。通过 cn-stock-data 获取各持仓股的行情和财务数据。支持机构组合报告风格（formal）和个人持仓复盘风格（brief）。 --- ### 数据源 ```bash SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts" # 各持仓股行情 python "$SCRIPTS/cn_stock_data.py" quote --code [CODE1],[CODE2],[CODE3],... # 各持仓股财务指标 python "$SCRIPTS/cn_stock_data.py" finance --code [CODE] # 各持仓股K线（组合收益计算） python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [起始日期] # 大盘指数（基准对比） python "$SCRIPTS/cn_stock_data.py" kline --code SH000300 --freq daily --start [起始日期] # 北向资金（外资对持仓的态度） python "$SCRIPTS/cn_stock_data.py" north_flow ``` ### Workflow (6 steps): **Step 1: 持仓信息确认** 收集用户持仓信息： | 代码 | 名称 | 持仓数量(股) | 成本价(元) | 买入日期 | |------|------|------------|----------|---------| 如果用户只给出股票代码和大致比例，也可以基于比例分析。 **Step 2: 持仓快照** 通过 cn-stock-data quote 获取最新行情，计算： | 代码 | 名称 | 现价 | 成本价 | 持仓市值(元) | 占比(%) | 盈亏(%) | 盈亏额(元) | |------|------|------|--------|------------|---------|---------|-----------| 汇总：总市值、总盈亏、加权平均收益率 **Step 3: 组合特征分析** **集中度分析**： - 前 3 大持仓占比 - 前 5 大持仓占比 - HHI 指数（持仓集中度） - 判断：过度集中 / 适度集中 / 分散 **行业暴露**： | 行业 | 持仓占比(%) | 沪深300权重(%) | 超配/低配 | |------|----------|-------------|----------| **风格暴露**： | 维度 | 组合加权平均 | 沪深300 | 偏向 | |------|------------|---------|------| | PE(TTM) | | | 价值/成长 | | PB | | | | | 市值(亿) | | | 大盘/中小盘 | | ROE(%) | | | 质量 | | 股息率(%) | | | 红利 | **Step 4: 风险分析** **个股风险**： - 任一持仓占比 > 30%：集中风险 - 持仓股的资产负债率 > 70%：财务风险 - 持仓股近期大幅下跌 > 20%：回撤风险 - ST 或负面新闻：特殊风险 **组合风险**： - 行业集中度：前 1 行业占比 > 50% = 高风险 - 相关性风险：多只持仓属同一板块/主题 = 分散不足 - 估值风险：组合加权 PE 显著高于市场 = 估值偏高 **Step 5: 优化建议** 基于分析结果给出建议： - **减仓**：过度集中的个股/行业 - **加仓**：低配的行业/补充分散性 - **替换**：基本面恶化的持仓 - **再平衡**：偏离目标配置的调整 **Step 6: 输出** ### 风格说明 | 维度 | formal（机构组合报告） | brief（个人持仓复盘） | |------|---------------------|---------------------| | 篇幅 | 4-8 页 | 1-2 页 | | 持仓明细 | 完整表格 + 各股核心指标 | 持仓快照表 | | 行业暴露 | 完整 vs 基准对比 | Top 3 行业 | | 风格分析 | 多维度雷达图描述 | PE/市值一句话 | | 风险分析 | 详细（个股+组合+情景） | 核心风险 2-3 条 | | 优化建议 | 详细再平衡方案 | 简要建议 | | 免责声明 | 需要 | 不需要 | ### 关键规则 1. **用户数据保密**：持仓信息是敏感数据，分析仅在当前会话中进行 2. **基准对比**：默认基准为沪深300，用户可指定其他基准（中证500/创业板指等） 3. **不做精确收益预测**：给出风险提示和优化方向，不预测组合未来收益 4. **区分建议的优先级**：高风险问题（过度集中/财务风险）优先于优化建议（再平衡） 5. **尊重用户策略**：如果用户有明确的投资策略（如集中持仓），不强行建议分散 6. **与其他 skill 联动**：对组合中个股可用 a-share-earnings-analysis 深入分析、a-share-technical 看技术面、a-share-thesis 梳理持仓逻辑 ## 使用示例 ### 示例 1: 基本使用 ```python # 调用 skill result = run_skill({ "param1": "value1", "param2": "value2" }) ``` ### 示例 2: 命令行使用 ```bash python scripts/run_skill.py --input data.json ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iizs/openclaw-dooray-hook-skill (version 0.1.1)

--- name: dooray-hook description: Send automated notifications to Dooray! messenger channels via webhooks. homepage: https://dooray.com metadata: openclaw: emoji: "📨" requires: bins: ["python3"] config: ["skills.entries.dooray-hook.config"] --- # Dooray! Webhook Skill A seamless integration to send text notifications and status updates to **Dooray!** chat rooms using Incoming Webhooks. ## Overview This skill allows OpenClaw to communicate with your team on Dooray!. It supports multiple chat rooms, customizable bot profiles, and configurable SSL verification settings. ## Configuration To use this skill, you must define your Dooray! webhook URLs in the OpenClaw global config (`~/.openclaw/openclaw.json`). > **Security Note:** Webhook URLs are stored in your local config file. Ensure this file's permissions are restricted (e.g., `chmod 600`). ```json { "skills": { "entries": { "dooray-hook": { "enabled": true, "config": { "botName": "N.I.C.K.", "botIconImage": "[https://static.dooray.com/static_images/dooray-bot.png](https://static.dooray.com/static_images/dooray-bot.png)", "verify_ssl": true, "rooms": { "General": "[https://hook.dooray.com/services/YOUR_TOKEN_1](https://hook.dooray.com/services/YOUR_TOKEN_1)", "Alerts": "[https://hook.dooray.com/services/YOUR_TOKEN_2](https://hook.dooray.com/services/YOUR_TOKEN_2)" } } } } } } ``` ### Configuration Options * **`rooms`** (Required): A dictionary mapping room names to webhook URLs. * **`botName`** (Optional): The name displayed for the bot message (Default: "OpenClaw"). * **`verify_ssl`** (Optional): Set to `false` to disable SSL certificate verification. Useful for corporate proxies or self-signed certificates. (Default: `true`). ## Usage ### 💬 Natural Language You can ask OpenClaw to send messages directly: * *"Send 'Server deployment successful' to the Alerts room on Dooray."* * *"Tell the General channel that I'll be late for the meeting."* ### 💻 CLI Execution ```bash python scripts/send_dooray.py "RoomName" "Your message content" ``` ## Technical Details * **Zero Dependencies**: Uses Python's built-in `urllib.request` and `json` modules. No `pip install` or `venv` required. * **Security**: * Defaults to secure SSL context (`verify_ssl: true`). * Requires explicit configuration to bypass certificate checks. ## Troubleshooting * **[SSL: CERTIFICATE_VERIFY_FAILED]**: If you are behind a corporate proxy or using self-signed certificates, add `"verify_ssl": false` to your config. * **Room Not Found**: Ensure the room name matches the key in your `openclaw.json` exactly (case-sensitive). * **Invalid URL**: Verify the webhook URL starts with `https://hook.dooray.com/services/`. ``` ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: zero2ai-hub/skill-ads-optimizer (version 1.0.1)

--- name: skill-amazon-ads description: "Amazon Ads API v3 skill for OpenClaw agents. List profiles, manage Sponsored Products campaigns, view budgets and performance. Works with any advertiser account." metadata: openclaw: requires: { bins: ["node"] } --- # Amazon Ads API Skill Manage Amazon Sponsored Products campaigns from your OpenClaw agent — list profiles, view campaigns, check budgets, and pull performance data. --- ## Setup ### 1. Create credentials file ```json { "lwaClientId": "amzn1.application-oa2-client.YOUR_CLIENT_ID", "lwaClientSecret": "YOUR_CLIENT_SECRET", "refreshToken": "Atzr|YOUR_REFRESH_TOKEN", "profileId": "YOUR_ADS_PROFILE_ID", "region": "eu" } ``` Save as `amazon-ads-api.json`. Set `AMAZON_ADS_PATH` env var to point to it (default: `./amazon-ads-api.json`). > **Regions & endpoints:** > - `na` → `advertising-api.amazon.com` > - `eu` → `advertising-api-eu.amazon.com` > - `fe` → `advertising-api-fe.amazon.com` ### 2. Get your Profile ID ```bash node scripts/ads.js --profiles ``` Copy the `profileId` for your brand/marketplace and add it to the credentials file. --- ## Scripts ### `ads.js` — Campaigns & Summary ```bash node scripts/ads.js --profiles # list all advertiser profiles node scripts/ads.js --campaigns # list all SP campaigns node scripts/ads.js --summary # active campaigns + budgets summary node scripts/ads.js --campaigns --out c.json # save to file ``` --- ## Credentials Schema | Field | Description | |-------|-------------| | `lwaClientId` | Ads app client ID (separate from SP-API) | | `lwaClientSecret` | Ads app client secret | | `refreshToken` | LWA refresh token | | `profileId` | Advertising profile ID (from `--profiles`) | | `region` | `na`, `eu`, or `fe` | --- ## Notes - Ads API uses a **separate LWA app** from SP-API — different client ID/secret - Profile ID is required for all campaign operations - Tokens are fetched fresh per request (no caching overhead for CLI use) - For production/high-frequency use, add token caching ## Related - [skill-amazon-spapi](https://github.com/Zero2Ai-hub/skill-amazon-spapi) — Orders, inventory & listings


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: xiaoliang1319-cloud/publish-skill (version 1.0.1)

--- name: xiaoxia-image-gen description: "通用文生图技能。使用 MiniMax、OpenAI、DALL-E、Stability 等模型生成图片。用户需要配置自己的 API Key（MINIMAX_API_KEY、OPENAI_API_KEY 等）。当用户需要生成图片、AI绘图时使用此技能。" --- # 通用文生图技能 (xiaoxia-image-gen) 支持多种文生图模型，用户可根据需要配置不同的 API Key。 ## 支持的模型 | 模型 | 环境变量 | 说明 | |------|----------|------| | MiniMax image-01 | `MINIMAX_API_KEY` | 推荐，性价比高 | | OpenAI DALL-E 3 | `OPENAI_API_KEY` | 质量高 | | Stability AI | `STABILITY_API_KEY` | 写实风格 | ## 使用方法 ### 1. 配置 API Key 根据你想使用的模型，设置对应的环境变量： ```bash # MiniMax (推荐) set MINIMAX_API_KEY=sk-cp-xxxxx # OpenAI set OPENAI_API_KEY=sk-xxxxx # Stability AI set STABILITY_API_KEY=sk-xxxxx ``` ### 2. 生成图片 在 OpenClaw 中，直接告诉我想生成什么图片，例如： - "帮我生成一只可爱的小龙虾" - "画一幅日落风景图" ## 示例提示词 - 可爱小龙虾：cute cartoon lobster mascot, kawaii style, orange color, big eyes - 风景：beautiful sunset over ocean, golden hour, photorealistic - 人物：portrait of a young woman, natural lighting, professional photography ## 技术实现 技能会根据用户配置的 API Key 自动选择可用的模型。优先顺序：MiniMax > OpenAI > Stability。 --- _🦞 虾虾团队出品_


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: 1970168137/performance-review-cn (version 1.0.0)

--- name: performance-review-cn description: "绩效面谈报告、OKR对齐度检测、校准辅助" --- # 绩效评估 ## 概述 本SKILL提供专业的绩效能力，帮助企业简化运营流程并提升业务成果。 ## 核心能力 - 绩效面谈报告、OKR对齐度检测、校准辅助 - 行业最佳实践和标准化工作流程 - 数据驱动的洞察和建议 - 合规性和风险管理考量 ## 应用场景 ### 主要应用 - 业务运营优化 - 战略决策支持 - 流程自动化和标准化 - 绩效监控和改进 ### 行业应用 - 企业级实施方案 - 中小企业解决方案 - 跨职能团队协作 - 供应商和合作伙伴管理 ## 输入要求 **COPY以下内容并粘贴输入：** ``` [请在此处提供您的具体业务背景、数据或需求] ``` ### 必需信息 - 业务背景和目标 - 相关数据或文档 - 具体要求或约束条件 - 目标受众或利益相关方 ### 可选信息 - 历史数据或基准 - 竞争格局 - 监管要求 - 预算和时间限制 ## 输出格式 本SKILL将提供： 1. **分析与洞察** - 数据驱动的观察和建议 2. **可交付成果** - 可直接使用的文档、脚本或计划 3. **实施指导** - 分步执行说明 4. **最佳实践** - 行业标准和成熟方法论 ## 最佳实践 - 始终提供完整的上下文以获得最佳结果 - 根据业务特定需求审查输出内容 - 根据您的特定行业标准验证建议 - 基于反馈和绩效指标进行迭代优化 ## 使用限制 - 需要准确和完整的输入数据 - 输出内容应由领域专家审查 - 高度专业化行业可能需要定制 - 不能替代专业法律、财务或医疗建议


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cofi295/content-repurposing-toolkit (version 1.0.1)

--- name: content-repurposing-toolkit description: "1 piece of content only lives on 1 platform? OpenClaw Skill Pro solves that. Turn it into 10+ formats across multiple platforms. Get OpenClaw Skill Pro on Telegram: @Cofi_ClawSkill_Bot" --- # Content Repurposing Toolkit Transform 1 piece of content into 10+ formats for different platforms. ## How to Use Ask your AI agent: - "Turn this blog post into a Twitter thread, LinkedIn article, and newsletter" - "Convert my YouTube video transcript into a blog post and social snippets" - "Repurpose this podcast episode into show notes, quote cards, and email" - "Take this product description and make Instagram carousels and ads" ## Features - Blog to Twitter, LinkedIn, Newsletter, Email, Video script - Video to Blog, social clips, transcripts - Podcast to Show notes, clips, social posts ## What You Get in the Pro Version The Pro version (available on Telegram: @Cofi_ClawSkill_Bot) includes everything above plus: - **10 full transformation templates** (blog-to-thread, blog-to-newsletter, blog-to-email, blog-to-video, video-to-blog, podcast-to-notes, content-to-infographic, SEO mapping, quality checklist) - **Full transformation matrix** — every format to every format - **SEO optimization workflow** — keyword mapping, canonical URLs, structured data - **Bulk processing guide** — handle 10+ pieces at once - **Multi-language workflow** (EN to VN) - **Quality scoring rubric** — auto 0-100 pre-publish check - **2 real examples** (1 blog to 5 formats, 1 video to 4 formats) - **3 Python scripts** (repurpose engine, bulk processor, SEO optimizer) - **18 files total** of ready-to-use content - **Lifetime access** — no subscriptions, no extra fees - **Instant delivery** — pay via crypto or PayPal, receive file immediately ## Get the Pro Version Get OpenClaw Skill Pro on Telegram: @Cofi_ClawSkill_Bot


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