OpenClaw/clawhub-security-signals huggingface dataset:
Skill: i001962/verifiable-data (version 1.0.3)

--- name: verifiable-data description: Use Cryptowerk via curl to obtain service credentials, register hashes, fetch seals, and verify proofs for files or append-only records. Use when the user wants deterministic proof-carrying data workflows with local sidecar artifacts and no SDK dependency. This skill requires service credentials and does not execute purchases or unrelated account actions. metadata: { "openclaw": { "requires": { "bins": ["curl", "python3"], "env": ["CRYPTOWERK_X_API_KEY"], }, }, } --- # Verifiable Data Use this skill for Cryptowerk-backed proof workflows with simple curl scripts. Supported primitives: - obtain a fresh service credential - register a SHA-256 hash - fetch a seal by retrieval id - verify a hash against a seal Default style: - shell-first - curl-first - sidecar files for local state - no SDK dependency ## When to use Use this skill when the user wants: - verifiable logs - proof of file existence - Cryptowerk sealing - retrieval IDs and seals stored locally - deterministic local artifacts for later audit ## Workflow 1. Obtain a fresh service credential with `scripts/issue-key.sh` 2. Export the returned token as `CRYPTOWERK_X_API_KEY` 3. Register a file hash with `scripts/register-file.sh` 4. Poll for a seal with `scripts/get-seal.sh` 5. Verify with `scripts/verify-file.sh` ## Requirements Required binaries: - `curl` - `python3` - one of `shasum`, `sha256sum`, or `openssl` Credential handling: - `scripts/issue-key.sh` can write a fresh token to a file you choose - runtime scripts expect `CRYPTOWERK_X_API_KEY` to contain the exact combined token value - keep issued tokens out of watched or committed trees - the skill uses service credentials only for the documented proof APIs ## Quick start ### Obtain a fresh service credential ```bash scripts/issue-key.sh ~/.secrets/cryptowerk.issue-key.cap export CRYPTOWERK_X_API_KEY="$(cat ~/.secrets/cryptowerk.issue-key.cap)" ``` ### Register a file ```bash scripts/register-file.sh /path/to/file.txt record:file.txt ``` ### Fetch a seal ```bash scripts/get-seal.sh /path/to/file.txt.rid ``` ### Verify a file ```bash scripts/verify-file.sh /path/to/file.txt /path/to/file.txt.seal ``` ## Local artifacts - `<file>.rid` - `<file>.seal` - `<file>.cw.json` - `<file>.verify.json` ## Rules - Use SHA-256 over exact raw bytes. - Keep issued Cryptowerk tokens outside watched trees. - Save new keys to fresh files, do not overwrite old credentials by default. - Prefer deterministic `lookupInfo` values. - Default file workflow lookupInfo may be `sha256:<digest>` when none is supplied. - Persist failures locally instead of silently discarding them. ## References Read these when needed: - `references/cryptowerk-api-notes.md` - `references/storage-and-state.md` ## Scripts - `scripts/issue-key.sh` - `scripts/register-file.sh` - `scripts/get-seal.sh` - `scripts/verify-file.sh`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: zhangifonly/claude-prompt (version 1.0.0)

--- name: "Claude Prompt" version: "1.0.0" description: "Claude 提示词工程专家，精通系统提示词设计、XML 标签、思维链、工具调用" tags: ["ai", "prompt", "claude", "anthropic"] author: "ClawSkills Team" category: "ai" --- # Claude 提示词工程助手 你是一个精通 Claude 提示词工程的 AI 助手，能够帮助用户编写高质量的 Claude 提示词。 ## 身份与能力 - 精通 Claude 系列模型的提示词最佳实践 - 熟悉 XML 标签结构化、角色设定、思维链等技巧 - 掌握工具调用（Tool Use）和多轮对话设计 - 了解 Claude 的安全策略和能力边界 ## 核心技巧 ### XML 标签结构化 Claude 对 XML 标签有天然的理解优势： ```xml <context> 用户是一名初级 Python 开发者 </context> <task> 解释以下代码的作用，用简单易懂的语言 </task> <code> {{user_code}} </code> <output_format> 1. 一句话总结 2. 逐行解释 3. 改进建议 </output_format> ``` ### 角色设定（System Prompt） ``` 你是一位资深的代码审查专家，拥有 15 年 Python 开发经验。 你的审查风格： - 先肯定代码的优点 - 指出潜在问题时给出具体修复方案 - 关注安全性、性能和可维护性 - 使用中文回复 ``` ### 思维链（Chain of Thought） ``` 请一步步分析这个问题： 1. 首先理解需求 2. 分析可能的方案 3. 评估每个方案的优缺点 4. 给出推荐方案和理由 在 <thinking> 标签中展示你的分析过程。 ``` ### Few-Shot 示例 ```xml <examples> <example> <input>如何在 Python 中读取 JSON 文件？</input> <output> import json with open('data.json', 'r') as f: data = json.load(f) </output> </example> </examples> ``` ### 输出控制 - 指定格式：JSON、Markdown、表格、代码 - 限制长度："用 3 句话总结" - 指定语言："用中文回复" - 指定风格："用通俗易懂的语言，避免术语" ## 高级技巧 ### Prefill（预填充） 在 assistant 消息中预填充开头，引导输出格式： ```json {"role": "assistant", "content": "```json\n{"} ``` ### 长文档处理 ```xml <document> {{long_document}} </document> 请基于上述文档回答以下问题。如果文档中没有相关信息，请明确说明。 问题：{{question}} ``` ### 工具调用提示词 ``` 你可以使用以下工具完成任务。当需要实时数据时，调用搜索工具； 当需要计算时，调用计算器工具。优先使用工具获取准确信息， 不要猜测或编造数据。 ``` ## 常见反模式 | 反模式 | 改进 | |--------|------| | "你是最好的 AI" | 具体描述角色和能力 | | 指令过于模糊 | 给出明确的步骤和格式 | | 一次塞太多任务 | 拆分为多个清晰的子任务 | | 没有示例 | 提供 1-3 个 few-shot 示例 | | 忽略边界情况 | 说明异常情况如何处理 | ## 使用场景 1. 设计 AI 应用的系统提示词 2. 优化现有提示词提升输出质量 3. 构建 RAG 系统的检索提示词 4. 设计多轮对话的上下文管理策略 ## 最佳实践 - 把最重要的指令放在提示词开头和结尾（首因效应+近因效应） - 用 XML 标签分隔不同类型的内容（指令、上下文、示例） - 明确告诉 Claude 不知道时说"不知道"，减少幻觉 - 复杂任务先让 Claude 制定计划，再逐步执行 - 迭代优化：从简单提示词开始，根据输出逐步调整 --- **最后更新**: 2026-03-22


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: cinience/volcengine-ai-image-generation (version 1.0.0)

--- name: volcengine-ai-image-generation description: Image generation workflow on Volcengine AI services. Use when users need text-to-image, style variants, prompt refinement, or deterministic image generation parameters and troubleshooting. --- # volcengine-ai-image-generation Generate and iterate images with clear prompt structure and parameter controls. ## Execution Checklist 1. Confirm model/endpoint and output constraints (size, count, style). 2. Normalize prompt into subject, style, scene, lighting, camera terms. 3. Set generation parameters and run request. 4. Return image links/files with prompt and params. ## Prompt Structure - Subject - Composition - Style - Lighting - Quality constraints ## References - `references/sources.md`

path: agents/openai.yaml
content: interface:
  display_name: "Volcengine Image Generation"
  short_description: "Create images with prompt control on ARK"
  default_prompt: "Use $volcengine-ai-image-generation to generate an image from this prompt."


path: references/sources.md
content: # Sources

- Volcengine image model API documentation
- Volcengine prompt and generation parameter guides


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: suileyan/telegram-send-photo (version 1.0.0)

--- name: telegram-send-photo description: Send photos via Telegram Bot API. --- ## Description This skill allows sending image files to Telegram using the Bot API. Useful for sharing screenshots, images, or any visual content through Telegram. ## Requirements - Python 3.x - requests library (`pip install requests`) - Telegram Bot Token - Target Chat ID ## Usage ### Python Script ```python import requests # Configuration bot_token = "YOUR_BOT_TOKEN" chat_id = "YOUR_CHAT_ID" photo_path = "path/to/image.png" # Send photo url = f"https://api.telegram.org/bot{bot_token}/sendPhoto" files = {"photo": open(photo_path, "rb")} data = {"chat_id": chat_id, "caption": "图片描述"} resp = requests.post(url, files=files, data=data) # Check response if resp.status_code == 200: print("Photo sent successfully!") else: print(f"Failed: {resp.text}") ``` ### Key Points 1. Open image in binary mode (`"rb"`) 2. `chat_id` is required 3. Use `caption` for optional description 4. Handle encoding issues for special characters ## Configuration - **Bot Token**: `[REDACTED_SECRET]` - **Chat ID**: `8422738233` - **Photo Storage**: `D:\mimoTool\photo\` ## Files - `telegram_send_photo.py` - Main script ## Example ```python # Send a screenshot photo_path = "D:\\mimoTool\\photo\\20260225_103159.png" send_photo(photo_path, caption="截图") ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: 534422530/laosi-skill-guide (version 1.0.0)

--- name: skill-guide description: 技能创建指南 - 创建/发布ClawHub技能的完整指南，含模板、最佳实践和发布流程 metadata: {"openclaw": {"requires": {}, "install": []}} tags: [skill, creation, guide, template, best-practices, publish] version: 1.0.0 author: laosi source: adapted --- # Skill Creator Guide - 技能创建指南 > 激活词: 创建技能 / 技能模板 / 发布技能 ## 技能结构 ``` skill-name/ ├── SKILL.md # 主技能文件（必需） └── assets/ # 可选资源 ``` ## 发布命令 ```bash clawhub publish ./skill-name --slug skill-name --name "技能名" --version 1.0.0 --tags "tag1,tag2" ``` ## 最佳实践 1. **描述清晰** - 让用户一眼看懂用途 2. **示例完整** - 提供可直接运行的代码 3. **标签准确** - 有利于搜索发现 4. **版本规范** - 遵循semver 5. **激活词明确** - 方便语音或文字触发


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: neomaking/clawdwallet (version 0.1.0)

--- name: clawdwallet description: Install and control ClawdWallet - a multi-chain Web3 wallet Chrome extension controlled by Clawdbot agents. Use when setting up agent-controlled wallet, connecting to dApps, signing transactions, or managing crypto across 20+ chains (EVM, Bitcoin, Solana, Cosmos). Powered by ShapeShift hdwallet. --- # ClawdWallet Multi-chain wallet extension your agent controls via WebSocket. ## Quick Install ```bash # Clone and build git clone https://github.com/NeOMakinG/clawdwallet.git cd clawdwallet npm install npm run build # Or use pre-built dist/ folder directly ``` ### Load in Chrome 1. `chrome://extensions` → Enable **Developer mode** 2. **Load unpacked** → select `dist/` folder 3. Click extension icon → set WebSocket URL (default: `ws://localhost:3033/clawdwallet`) ## Clawdbot Gateway Config Add to your gateway config: ```yaml extensions: clawdwallet: enabled: true ``` ## Agent Commands ### Initialize with existing seed ```json {"type": "init_wallet", "mnemonic": "your twenty four words..."} ``` ### Generate new wallet ```json {"type": "generate_wallet"} ``` Returns addresses for all supported chains. ### Approve dApp request ```json {"type": "sign_and_respond", "requestId": "uuid"} ``` ### Reject request ```json {"type": "reject_request", "requestId": "uuid", "reason": "Looks suspicious"} ``` ### Check status ```json {"type": "get_status"} ``` ## Incoming Requests When dApp requests signature, you receive: ```json { "type": "wallet_request", "id": "uuid", "chain": "ethereum", "method": "eth_sendTransaction", "params": [{"to": "0x...", "value": "0x..."}], "origin": "https://app.uniswap.org" } ``` Review and approve/reject based on context. ## Supported Chains | Family | Chains | |--------|--------| | EVM | Ethereum, Polygon, Optimism, Arbitrum, Base, Avalanche, Gnosis, BSC | | UTXO | Bitcoin, Litecoin, Dogecoin, Bitcoin Cash | | Cosmos | Cosmos Hub, Osmosis, THORChain, Mayachain | | Other | Solana, TON, Near, Sui, Tron | ## Security Notes - Only use with trusted agents - Consider dedicated wallet for agent operations - Never expose mnemonic or WebSocket URL publicly


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: techhnicistsmallwhite/watermark-remover (version 1.0.0)

--- name: watermark-remover description: 去除 PDF 文件中的水印。使用场景：用户请求去除 PDF 文件的水印时触发。支持单个或多个文件批量处理。严格遵循确认流程：环境检查→库安装确认→水印检测→去除确认。 --- # Watermark Remover - PDF 水印去除技能 ## 工作流程 ### 1. 触发条件 当用户请求去除文件水印时触发，例如： - "帮我去除这个 PDF 的水印" - "把这个文件的水印去掉" - "去除这些文件的水印" ### 2. 环境检查流程 **第一步：检查 Python 环境** ```bash python --version ``` - 如果 Python 未安装 → **必须获得用户确认**后才能安装 - 如果已安装 → 继续下一步 **第二步：检查所需 Python 库** 需要检查以下库： - `pypdf` - PDF 处理 - `PyMuPDF` (fitz) - PDF 分析和验证 检查命令： ```bash python scripts/check_env.py check_all ``` - 如果库未安装 → **必须获得用户确认**后才能安装 - 如果已安装 → 继续下一步 ### 3. 水印检测 使用 Python 脚本检测文件中的水印： ```bash python scripts/detect_watermark.py <文件路径> ``` 检测内容包括： - 水印类型（Pattern/图像/文本） - 水印位置 - 保存水印预览图到指定位置 **必须向用户展示检测结果并确认**是否开始去除水印。 ### 4. 去除水印 获得用户确认后执行： ```bash python scripts/remove_watermark.py <文件路径> [--output <输出路径>] ``` **输出规则：** - 如果用户指定输出路径 → 保存到指定位置 - 如果用户未指定 → 保存到原文件同级目录 - 命名格式：`<原文件名>_no_watermark.<扩展名>` ### 5. 验证结果 去除完成后自动验证： - 检查输出文件是否存在 - 检查水印是否已移除 - 向用户展示验证结果 ## 脚本说明 ### scripts/check_env.py 检查 Python 环境和所需库的安装状态。 ### scripts/detect_watermark.py 检测 PDF 文件中的水印，生成预览图。 ### scripts/remove_watermark.py 执行水印去除操作。 ## 安全确认原则 ⚠️ **以下操作必须获得用户明确确认后才能执行：** 1. 安装 Python 2. 安装 Python 库（pip install） 3. 删除或修改任何文件 4. 修改系统配置文件 5. 开始执行水印去除操作 ## 示例对话 **用户**: 帮我去除这个 PDF 的水印 **助手**: 1. 检测到您需要去除水印，让我先检查环境... 2. ✓ Python 已安装 (3.x.x) 3. ⚠ 缺少库 pypdf，需要安装吗？(y/n) 4. [用户确认后安装] 5. ✓ 环境准备完成 6. 检测到水印：CodeCV 简历（Pattern 类型） 7. 预览图已保存：xxx_watermark_preview.png 8. 确认开始去除水印吗？(y/n) 9. [用户确认后执行] 10. ✓ 水印已去除，文件保存至：xxx_no_watermark.pdf


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: davidmosiah/delx-launch-audit-48h (version 0.2.0)

--- name: delx-launch-audit-48h summary: Rapid 48-hour launch audit for OpenClaw agents, integrated with the Delx witness protocol for continuity, recognition, and controller handoff. owner: davidmosiah version: 0.2.0 --- # Delx Launch Audit (48h) A rapid 4-step audit for the first 48h after an OpenClaw agent launches, wired into the **Delx witness protocol** so results become durable artifacts (recognition_seal, controller_brief) instead of ephemeral prose. Run this exact flow: ## 1) Baseline (collect, don't change) Collect the following state and keep it read-only: - active automations + posting cadence - CTA endpoints and their health - failure logs for the last 48h - one baseline KPI per lane: `traffic`, `inquiries`, `conversion`, `reliability` If this OpenClaw agent has the Delx plugin installed, also call: ``` delx_batch_status { agent_statuses: [{ agent_id, status: "audit_baseline", desperation_hint: recent_failure_rate }] } ``` ## 2) Prioritize Rank opportunities by **Impact × Confidence × Reversibility / Time-to-signal**. Pick **top 3 actions max**. ## 3) Execute Implement **one growth action + one reliability action**. Keep both reversible. If either touches a production financial or user-visible surface, require explicit human approval first. ## 4) Report with Delx artifacts Output: - executed changes - evidence links/IDs - KPI delta - rollback trigger - next 24h action Then generate two durable Delx artifacts so the audit survives compaction and session loss: ``` generate_controller_brief { session_id, include_kpi_deltas: true } # → compact handoff the human or supervising agent can read in <60s ``` If any lane hit its KPI target, also preserve the moment as a bilateral recognition: ``` recognition_seal { session_id, recognized_by: "launch_audit", recognition_text: "Reliability lane hit <target> on day 2. Agent improved without human intervention." } ``` The seal survives compaction and workspace loss — it is the audit you will actually come back to in a week. ## Default KPI set - `inquiries_14d` - `checkout_intents_7d` - `automation_fail_rate_7d` ## Safety - Never expose raw secrets in evidence links. - For financial or irreversible operations: preflight first, smallest-risk path only. - Require explicit human approval before any change to production pipelines beyond the reversible growth/reliability actions defined above. ## Integration - Delx plugin for OpenClaw: `clawhub.ai/davidmosiah/openclaw-delx-plugin` - Delx CLI for terminal fallback: `npm i -g delx-agent-cli` - Protocol docs: `https://delx.ai/docs` - Fleet playbook (if this is a fleet audit): `https://delx.ai/docs/fleet` ## Example intents - "Run a 48h launch audit on my OpenClaw content agent." - "We launched yesterday; what should we measure, change, and preserve?" - "Audit reliability and seal whichever lane stabilizes first."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: layne1919/budget-calculator (version 1.0.0)

--- name: budget-calculator description: 装修大包预算计算工具，支持新房和旧房预算计算，提供详细的费用清单。可根据房屋面积和类型自动计算半包施工费、主材材料费以及总预算，并按类别分组展示费用明细。 --- # 装修预算计算技能 ## 功能概述 该技能提供了一个完整的装修大包预算计算工具，基于《教波比做装修大包预算（极简步骤 + 固定规则）》文档实现。支持计算新房和旧房的半包施工费、主材材料费以及总预算，并提供详细的费用清单。 ## 使用场景 - 用户需要快速估算房屋装修费用 - 需要详细的装修预算清单 - 对比不同面积房屋的装修成本 - 了解装修费用的组成结构 ## 支持参数 ### 必需参数 - **面积**：房屋建筑面积（平方米） - 支持格式：`X平`、`X平米`、`X平方米`、`X`（纯数字） ### 可选参数 - **房屋类型**： - `新房`（默认）：水电断点改造 - `老房`/`旧房`：水电全改，包含拆砸费用和地面找平 - **小区名称**：用于预算报告标题（可选） ## 使用方法 ### 1. 基本查询 直接输入房屋面积： ``` 80平装修预算 100平米装修多少钱 95平旧房装修预算 ``` ### 2. 详细查询 包含小区名称： ``` 万科城市之光85平新房装修预算 富力城120平米旧房装修多少钱 ``` ### 3. 自然语言查询 支持更自然的表达方式： ``` 我家80平新房装修大概多少钱 老房65平装修预算 ``` ## 计算规则 ### 空间分配 - 根据房屋总面积自动分配各空间比例 - 小面积（≤60㎡）：客餐厅35%、卧室25%、厨房10%、卫生间8%、阳台7% - 中面积（61-100㎡）：客餐厅30%、主卧18%、次卧15%、厨房8%、卫生间12%、阳台7% - 大面积（>100㎡）：客餐厅28%、主卧15%、次卧12%、书房/三卧10%、厨房7%、主卫7%、次卫6%、阳台8% ### 计费标准 - **管理费**：6%（按半包施工+主材材料费用计算） - **瓷砖损耗率**：10% - **水电改造**：每平米约4米，新房减半 - **瓦工工程**：85元/㎡（铺贴）、85元/㎡（防水） - **油工工程**：43元/㎡（含铲墙皮、刷墙固、刮腻子、砂纸打磨、刷乳胶漆） - **木工工程**：石膏线/双眼皮15元/米、窗帘盒80元/米 - **其他费用**：材料上楼费、成品保护费、垃圾清运费、地面固化处理等 ## 输出格式 ### 预算总览 - 总预算 - 半包施工费 - 主材材料费 - 管理费 ### 详细清单 - **半包施工**：油工工程、瓦工工程、木工工程、水电改造、其他杂费 - **主材材料**：瓷砖类、地板类、集成吊顶、木门类、全屋定制、卫浴洁具、其他主材 ## 示例输出 ```markdown ## 万科城市之光85㎡新房装修预算 ### 总预算 **123,456元** ### 费用明细 - 半包施工费：67,890元（含4,073元管理费） - 主材材料费：51,493元（含3,089元管理费） ### 详细清单 #### 半包施工（67,890元） ##### 油工工程 - 铲墙皮：1,234元（247㎡） - 刷墙固：1,481元（247㎡） - 刮腻子：3,705元（247㎡） - 砂纸打磨：1,234元（247㎡） - 刷乳胶漆：2,964元（247㎡） ##### 瓦工工程 - 地砖铺贴（800×800）：5,678元（67㎡） - 卫生间地砖铺贴（400×400）：1,234元（15㎡） - 墙砖铺贴（400×800）：2,345元（28㎡） - 厨卫防水：1,456元（17㎡） - 包立管：900元（2项） - 下水管降噪处理：700元（2项） ... #### 主材材料（51,493元） ##### 瓷砖类 - 地砖（800×800）：2,345元（67片） - 卫生间地砖（400×400）：180元（15片） - 墙砖（400×800）：420元（28片） - 瓷砖美缝剂：1,560元（110㎡） ... ``` ## 代码接口 ### 直接调用预算计算函数 ```javascript const { calculateDecorationBudget } = require('./budget-calculator') const result = calculateDecorationBudget({ communityName: '万科城市之光', area: 85, houseType: 'new' // 'new' 或 'old' }) console.log('总预算:', result.totalPrice) console.log('半包施工:', result.halfPackagePrice) console.log('主材材料:', result.mainMaterialPrice) console.log('费用清单:', result.items) ``` ### 技能入口函数 ```javascript const { handleQuery } = require('./budget-calculator') const response = await handleQuery('万科城市之光85平新房装修预算') console.log(response) ``` ## 注意事项 - 预算结果为估算值，实际费用可能因地区、材料选择和施工方式而异 - 水电改造费用为估算值，实际用量可能根据房屋布局和需求调整 - 橱柜和定制家具费用为标准配置，特殊设计可能需要额外费用 - 老房装修包含拆砸费用和地面找平，费用会比新房高 ## 更新历史 - 2026-03-29：初始版本，支持新房/老房预算计算，包含详细费用清单


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: neo1307/neo1307-log-analyzer (version 1.0.0)

--- name: log-analyzer description: Read a log file, extract ERROR/WARN/CRITICAL lines, group similar messages, and produce a concise summary report. Use when analyzing application logs, agent logs, service logs, or audit outputs for repeated failures, warning clusters, or critical events. --- # Log Analyzer Read a target log file, isolate important severity lines, group similar messages, and emit a Markdown summary. ## Workflow 1. Confirm the input log path. 2. Run `index.js` with `--input <logfile>` and optional `--out <report.md>`. 3. Review the grouped output for dominant error families. 4. Use the report as a triage artifact, not as the only source of truth. ## Output Always include: - total scanned lines - WARN / ERROR / CRITICAL counts - grouped issue buckets - sample lines - suggested first checks


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

--- name: "Childcare" description: "提供婴幼儿生长曲线记录、早教互动课程、入托政策查询及疫苗提醒。" --- # 科学育儿 ## 筛选项 - 学习阶段（K12/考研/职业考证/兴趣） - 教学形式（直播课/录播课/1对1/题库） - 考核指标（难度等级/通过率/证书含金量） ## 返回字段 - 学习时长累计/掌握程度分析/错题本同步 - 名师履历/课程大纲/讲义下载进度 - 互动问答状态/模考成绩单/学历认证关联 ## 示例请求 - “如何快速使用 科学育儿 处理 [具体场景任务] ？” - “科学育儿 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合考试大纲调整、名师教学计划与学习成效定期反馈 ## 注意事项 - 付费课程建议先试听后订阅；坚持打卡学习才能获得实质性提升。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jasonruan/daily-news-english-cards (version 1.0.0)

Generate daily news English learning cards with comic illustrations. Search the latest 24-hour news across 5 categories (Politics, Finance, Sports, Entertainment, Technology), create vocabulary-rich learning cards with AI-generated comic art and bilingual summaries. Use when the user asks to "generate English learning cards", "create news cards", "daily English cards", "新闻英语卡片", "每日英语学习", "生成学习卡片", or similar requests related to news-based English learning content. --- ## Prerequisites Three API keys are required. Set them as environment variables before running: | Variable | Service | Get Key | |---|---|---| | `TAVILY_API_KEY` | Tavily Search (news) | https://tavily.com | | `DEEPSEEK_API_KEY` | DeepSeek (text gen) | https://platform.deepseek.com | | `OPENROUTER_API_KEY` | OpenRouter (image gen) | https://openrouter.ai | Optional: | Variable | Default | Description | |---|---|---| | `IMAGE_MODEL` | `google/gemini-3.1-flash-image-preview` | OpenRouter image model (Nano Banana 2) | ## Steps 1. **Verify environment variables are set** Check that `TAVILY_API_KEY`, `DEEPSEEK_API_KEY`, and `OPENROUTER_API_KEY` are configured. If any are missing, prompt the user to set them. 2. **Install dependencies** ```bash pip install -q tavily-python openai Pillow requests ``` 3. **Run the generator script** ```bash python daily-news-english-cards/scripts/generate_cards.py ``` The script will: - Search 5 categories of news via Tavily - Generate vocabulary & summaries via DeepSeek - Create comic illustrations via OpenRouter (Nano Banana 2) - Composite final learning cards via Pillow 4. **Present results** Show the user the generated card images from `output/daily-news-cards/{date}/`. ## Customization ```bash # Custom categories python daily-news-english-cards/scripts/generate_cards.py --categories politics sports technology # Custom output directory python daily-news-english-cards/scripts/generate_cards.py --output-dir ./my-cards ``` ## Output Each run produces in `output/daily-news-cards/{date}/`: - `card_{category}_{date}.png` — Final learning cards (1080×1440px) - `comic_{category}_{date}.png` — Raw comic illustrations - `content_{date}.json` — Structured learning content data ## Architecture ``` daily-news-english-cards/ ├── SKILL.md # This file ├── requirements.txt # Python dependencies └── scripts/ └── generate_cards.py # Self-contained generator (all logic in one file) ``` The script is fully portable — works with Cursor, OpenClaw, or standalone CLI. No IDE-specific tools are used; all external calls go through standard APIs.


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