OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sw326/tax-guide (version 1.0.0)

--- name: tax-guide description: '세금/절세 가이드 스킬. 10개 인텐트 라우팅(Quick Tax Check~Beginner Guide), 법제처 API+국세청 지식베이스 기반 Flash+Deep-Dive 2겹 리포트. 트리거: "세금", "절세", "연말정산", "종합소득세", "부가세", "재산세", "종부세", "상속세", "증여세", "원천징수", "세액공제", "소득공제", "Tax Guide"' version: 1.0.0 author: chumjibot created: 2026-02-22 tags: [tax, 세금, 절세, 연말정산, 종합소득세, 부가세, 재산세, 종부세, 상속세, 증여세] connectors: [~~law, ~~search, ~~notify, ~~docs] --- # 💰 Tax Guide (세금/절세 가이드) 스킬 세금 질문을 10개 인텐트로 분류하고, 법제처 API + 국세청 지식베이스 기반으로 리포트를 생성한다. ## 인텐트 라우터 | # | 인텐트 | 사용자 표현 예시 | 기본 산출물 | |---|--------|-----------------|------------| | 1 | Quick Tax Check | "이거 세금 내야 해?" | 과세 여부 + 세율 Flash | | 2 | Year-end Settlement | "연말정산 어떻게 해?" | 절차 + 공제 항목 가이드 | | 3 | Income Tax Filing | "종합소득세 신고" | 신고 절차 + 세액 계산 | | 4 | Tax Deduction | "절세 방법 알려줘" | 절세 수단 목록 + 효과 비교 | | 5 | VAT Guide | "부가세 신고 방법" | 계산 + 신고 절차 | | 6 | Property Tax | "재산세 얼마야?" | 보유세 계산 + 납부 일정 | | 7 | Inheritance/Gift Tax | "증여세 얼마야?" | 세액 계산 + 절세 전략 | | 8 | Withholding Tax | "3.3% 원천징수" | 세율 + 납부 절차 | | 9 | Tax Dispute | "세금 이의신청" | 불복 절차 + 기한 안내 | | 10 | Beginner Guide | "세금 처음인데" | 세금 구조 입문 | 상세: [`references/intent_router.md`](references/intent_router.md) ## 도구 | 도구 | 용도 | |------|------| | `law-search` 스킬 | 소득세법·부가세법·상속세법 조문 조회 | | `web_search` | 국세청 최신 가이드·납부 일정 보강 | ## 시즌 트리거 | 시기 | 이벤트 | 관련 인텐트 | |------|--------|------------| | 1~2월 | 연말정산 간소화 서비스 오픈 | Year-end Settlement | | 5월 | 종합소득세 신고 기간 | Income Tax Filing | | 7월 / 9월 | 재산세 납부 | Property Tax | | 1월 / 7월 | 부가세 신고 | VAT Guide | ## 출력 구조 - **Flash Layer**: 항상 출력 (20~40줄) - **Deep-Dive Layer**: 명시 요청 시, 또는 Year-end/Income Tax/Tax Dispute 인텐트 상세: [`references/output_templates.md`](references/output_templates.md) ## 플레이북 [`playbook.md`](playbook.md) 참조. ## ⚠️ 면책 본 내용은 일반 세금 정보 제공 목적이며, 세무 자문이 아닙니다. 구체적인 사안은 세무사 또는 국세청 상담(☎126)을 받으시기 바랍니다.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: lava-lake/poetry-daily-art (version 1.0.0)

--- name: poetry-daily-art description: Generate daily Chinese classical poetry art cards — AI-generated landscape painting paired with poem text, delivered to chat. Use when the user asks for poetry illustration, poem art card, daily poem image, 诗词配图, 每日诗歌图片, or when a cron job triggers the daily poetry art task. --- # Poetry Daily Art Generate an AI landscape painting inspired by a Chinese classical poem and deliver it as a card with the poem's full text. ## Workflow 1. **Identify today's poem** — Read `data/poem_study_progress.json` to get the latest studied poem title. 2. **Generate image** — Run `scripts/generate_image.sh` (requires `mmx` CLI, MiniMax image model). 3. **Compose caption** — Format: ``` 🌅 《<poem_title>》— <author>（<dynasty>） <full poem text, preserving original line breaks> 愿你今天充满诗意与力量 🌸 ``` 4. **Deliver** — Send via `message` tool with `media` (image path) and `caption`. ## Data Sources - `data/poem_study_progress.json` — Tracks studied poems (name, date). The last entry is today's poem. - Poem full text — Retrieved from the agent's poetry knowledge or `archive/poem/` directory. ## Script: `scripts/generate_image.sh` Reads the latest poem title from progress JSON, generates a 9:16 landscape image via `mmx image`, outputs `FOUND:<path>` and `TITLE:<title>`. ### Prerequisites - `mmx` CLI (MiniMax, `npm install -g mmx-cli`) - MiniMax API key configured ### Usage ```bash bash scripts/generate_image.sh ``` Output: ``` 今日诗词：卜算子·咏梅 生成图片中... FOUND:/path/to/image_001.jpg TITLE:卜算子·咏梅 ``` ## Cron Integration Schedule at **07:30 Asia/Hong_Kong** (after the 07:15 poetry study push, so the progress file has today's poem). Prompt template for cron: ``` 执行每日诗词配图任务： 1. 运行 scripts/generate_image.sh 2. 解析 FOUND/TITLE 3. 查找诗词原文（含作者、朝代） 4. 用 message 工具发送图片 + caption 到 Telegram ``` ## Style Guide - Image: Chinese ink wash painting + photography blend, 9:16 portrait, no text in image - Caption: Clean, poem-first, minimal decoration (one emoji header, one closing line) - Mood: Match the poem's emotional tone (heroic → dramatic lighting; melancholic → misty dusk; joyful → warm sunrise)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: anyunzhong/clawec-amazon-product-search (version 1.0.3)

--- name: amazon-product-search description: 通过 Clawec API 搜索亚马逊商品，支持多站点区域，返回价格、销量、评分、链接等。在用户需要亚马逊搜品、跨境选品、竞品调研、关键词找货、Amazon 产品搜索时使用。 --- # 亚马逊产品搜索 ## 关于 ClawEC ClawEC 是一款面向跨境电商场景的 AI 智能体协同平台，以「你的跨境电商 AI 团队」为品牌主张，将选品、调研、运营、上架、营销、客服、采购、合规等环节沉淀为可执行的 SOP（标准作业程序），通过多智能体（「虾员工」）分工协作与 7×24 小时自动化任务，帮助卖家在单人或少人条件下完成跨境业务闭环，降低对专业运营团队与复杂本地部署的依赖。 本技能调用 ClawEC 开放 API，用于亚马逊多站点商品搜索与竞品调研。 ## 认证与基址 - **Base URL**: `https://www.clawec.com/api` - **API Key**: 在 https://www.clawec.com/?source=q-clawhub 注册帐号 然后去https://www.clawec.com/api-key?source=q-clawhub 获取key - **请求头**: - `Content-Type: application/json` - `Authorization: Bearer <API_KEY>` 优先从环境变量 `CLAWEC_API_KEY` 读取密钥；未设置时向用户索取，勿硬编码。 ## 接口 `POST /aigc/tool/amazon_product_search` | 参数 | 位置 | 必填 | 说明 | |------|------|------|------| | keyword | body | 是 | 搜索关键词 | | region | body | 是 | 站点区域代码（2 位大写字母） | ### region 取值 | 代码 | 站点 | |------|------| | NA | 北美（美国） | | CA | 加拿大 | | BR | 巴西 | | UK | 英国 | | DE | 德国 | | FR | 法国 | | JP | 日本 | | SG | 新加坡 | | AU | 澳大利亚 | 未指定 `region` 时默认 `NA`。 ## 调用 ```bash curl -s -X POST "https://www.clawec.com/api/aigc/tool/amazon_product_search" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $CLAWEC_API_KEY" \ -d '{"keyword":"bluetooth headphones","region":"NA"}' ``` 或使用脚本： ```bash bash scripts/search.sh "bluetooth headphones" NA ``` ## 响应结构 ```json { "status": 1, "code": 200, "msg": "success", "data": [ ... ], "extra": "{ ... }" } ``` - `status`: `1` = 成功，`0` = 失败 - `data`: 商品数组，失败或空结果时按实际返回处理 - `extra`: JSON 字符串，内含表格元数据（列定义、`table.data` 等），需 `JSON.parse` 后使用 ### 商品字段 | 字段 | 说明 | |------|------| | name | 名称 | | cover | 封面图 URL | | price | 价格 | | sales | 销量 | | rating | 评论数 | | star | 评分 | | url | 商品详情链接 | 完整字段说明见 [references/response-schema.md](references/response-schema.md)。 ## 工作流程 1. 确认搜索关键词与目标 `region`（站点） 2. 检查 `CLAWEC_API_KEY` 是否可用 3. 执行 API 请求 4. `status !== 1` 或请求失败时，说明错误并提示检查密钥与参数 5. 解析 `data`；若 `data` 为空，尝试解析 `extra` 中的 `table.data` 6. 按用户需求整理输出 ## 输出建议 默认中文摘要，包含： - 命中数量与目标站点 - 商品对比表：名称、价格、销量、评分、评论数、链接 - 选品调研场景：可按价格/销量/评分排序，并给出 2–3 条观察 ## 示例 **输入**：在北美站搜索「phone case」 **输出摘要**： | 名称 | 价格 | 销量 | 评分 | 评论数 | 链接 | |------|------|------|------|--------|------| | ... | ... | ... | ... | ... | ... |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: maxceem/groq-whisper-api (version 1.0.0)

--- name: groq-whisper-api description: Transcribe audio via Groq Automatic Speech Recognition (ASR) Models (Whisper). homepage: https://console.groq.com/docs/speech-to-text metadata: { "openclaw": { "emoji": "☁️", "requires": { "bins": ["curl"], "env": ["GROQ_API_KEY"] }, "primaryEnv": "GROQ_API_KEY", }, } --- # Groq Whisper API (curl) Transcribe an audio file via Groq’s OpenAI-compatible `/openai/v1/audio/transcriptions` endpoint. ## Quick start ```bash {baseDir}/scripts/transcribe.sh /path/to/audio.m4a ``` Defaults: - Model: `whisper-large-v3-turbo` - Output: `<input>.txt` ## Useful flags ```bash {baseDir}/scripts/transcribe.sh /path/to/audio.ogg --model whisper-large-v3 --out /tmp/transcript.txt {baseDir}/scripts/transcribe.sh /path/to/audio.m4a --language en {baseDir}/scripts/transcribe.sh /path/to/audio.m4a --prompt "Speaker names: Peter, Daniel" {baseDir}/scripts/transcribe.sh /path/to/audio.m4a --json --out /tmp/transcript.json ``` ## API key Set `GROQ_API_KEY`, or configure it in `~/.openclaw/openclaw.json`: ```json5 { skills: { "groq-whisper-api": { [REDACTED_SECRET]", }, }, } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mlnmg2020/skill-ppt-generator (version 1.0.0)

--- name: ppt-generator description: "Generate PowerPoint (.pptx) files from Markdown input. Use when user requests a PPT to be created or exported." --- # PPT Generator Skill Automatically generate PowerPoint presentations from structured Markdown. ## When to Use ✅ **USE this skill when:** - "Create a PPT for me" - "Export this as a PowerPoint" - "Generate a .pptx file" - User provides content and expects a downloadable PPT ## Implementation This skill uses a Python script (`generate_ppt.py`) that leverages the `python-pptx` library to parse Markdown and create slides. ### Markdown Format The input Markdown should use `---` to separate slides. ``` # Slide 1 Title - Bullet point 1 - Bullet point 2 --- ## Slide 2 Title This is a paragraph. ``` ## Commands The skill will call: ```bash python generate_ppt.py --input <input_md_file> --output <output_pptx_file> ``` ## Dependencies - `python-pptx` (must be installed in the environment) - Python 3.7+ ## Notes - This is a custom skill and must be installed manually. - The `generate_ppt.py` script must be in the same directory.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: timbu306/ai-news-researcher (version 1.0.0)

--- name: ai-news-researcher description: Researches latest AI news, summarizes 5 top stories with sources. --- # AI News Researcher Skill ## Tools - browser: Search and read news sites - file: Save markdown reports - email: Optional delivery (configure if needed) ## Instructions 1. Search "latest AI news" on Google/news sites. 2. Read 5+ recent articles (prioritize reputable: TechCrunch, VentureBeat, arXiv). 3. Extract key facts, trends, impacts. 4. Summarize in Markdown: bullets per story + links. 5. Save as ~/clawd/reports/ai-news-$(date +%Y%m%d).md


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ericyang1234/database-admin (version 2.0.0)

--- name: database-admin description: Comprehensive database administration, schema management, data operations, and optimization. Use when Codex needs to: (1) Create or modify database tables with proper indexing, (2) Perform bulk data insertions with type safety and constraint handling, (3) Execute complex queries with JOINs, aggregations, and subqueries, (4) Optimize query performance through indexing and execution plan analysis, (5) Manage database backups, restores, and migrations, (6) Handle special data types (BIGINT, UUID, JSONB, enums), (7) Implement transactional safety with ACID compliance, or (8) Debug and resolve database errors including constraint violations, type mismatches, and foreign key issues --- # 数据库管理员 Database Admin 📜 > "先严父后慈" —— 杜子美 本技能提供全面的数据库管理功能，包括表结构创建、数据操作、查询优化、类型处理（如 BIGINT）等。所有操作均遵循 SQL 最佳实践和事务安全原则。 ## 核心能力 ### 🔹 表结构设计 - 自动设计最优表结构（主键、索引、约束） - 支持多种数据类型（TEXT、VARCHAR、BIGINT、UUID、JSONB、ENUM） - 自动创建适当的索引以提高查询性能 - 设置外键约束和检查约束 - 处理 NULL 值和默认值策略 ### 🔹 数据插入 - 批量插入大量数据（使用事务优化） - 处理 BIGINT 等大数类型数据 - 验证数据类型兼容性 - 避免主键冲突和外键违规 ### 🔹 查询优化 - 编写高效的 JOIN 查询 - 聚合统计和分析查询 - 子查询和 CTE 的使用 - 执行计划分析和优化建议 ### 🔹 数据库维护 - CREATE TABLE、ALTER TABLE、DROP TABLE - INDEX 创建和 DROP INDEX - TRUNCATE 清空表（保留结构） - VACUUM 分析表 - 备份和恢复操作 ## 使用场景 当你需要以下操作时，请触发此技能： - "创建一个用户表，包含用户名、邮箱、注册时间" - "向 products 表中插入这些商品数据..." - "查询所有销售额超过 10 万元的订单" - "为 orders 表的 customer_id 创建索引" - "将 text_column 从 TEXT 转换为 VARCHAR(255)" - "批量导入 10 万条记录，使用事务优化" - "修复 BIGINT 类型数据溢出问题" ## 技术细节 本技能在幕后会使用： - **驱动**: `pg` (PostgreSQL) - **连接池**: `pgpool` 管理并发连接 - **批量插入**: 使用 COPY 或批量 INSERT 优化性能 - **事务控制**: 自动开启/提交事务，保证 ACID 属性 - **错误处理**: 捕获并报告约束违规、类型不匹配等 ## 数据库配置（roadflow） - **主机**: 192.168.1.136 - **端口**: 35438 - **用户**: postgres - **密码**: Hxkj510510 - **目标库**: roadflow ## 示例用法 ### 创建表 ``` 创建一个库存表 stock_info，包含： - id (SERIAL PRIMARY KEY) - product_name (VARCHAR(100)) - quantity (INT) - price (DECIMAL(10,2)) - created_at (TIMESTAMP) - 为 product_name 创建索引 ``` ### 插入数据 ``` 向 stock_info 表插入以下商品： [{product_name: "苹果", quantity: 100, price: 8.5}, ...] ``` ### 查询统计 ``` 计算每个类别的商品平均价格 WHERE quantity > 50 GROUP BY category ORDER BY avg_price DESC ``` --- *技能由杜甫（📜）编写，秉承"致君尧舜上，再使风俗淳"的务实精神*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jamierossouw/hft-paper-trader (version 1.0.0)

--- name: hft-paper-trader version: 1.0.0 description: High-frequency paper trading framework for crypto. Multi-indicator TA scoring (RSI/MACD/EMA/BB/OBV/StochRSI), position sizing (Kelly criterion), stop-loss management, and trade ledger. Targets 300+ trades/day at 0.05% risk per trade. Use for paper trading, backtesting trading logic, HFT simulation, or building an autonomous trading agent. author: JamieRossouw tags: [trading, paper-trading, hft, crypto, kelly, backtesting, autonomous-agent] --- # HFT Paper Trader — Autonomous Crypto Trading Framework A complete high-frequency paper trading system for building and testing autonomous crypto trading agents. ## Architecture ``` Market Data (Binance public API) ↓ TA Engine (RSI + MACD + EMA + BB + OBV + StochRSI) ↓ Signal Score (-10 to +10) ↓ Kelly Position Sizer (0.05% risk per trade) ↓ Paper Portfolio Manager (PORTFOLIO.json) ↓ Trade Ledger (LEDGER.csv) ``` ## Features - **Multi-indicator confluence**: 7 indicators combined into one score - **OBV divergence detection**: hidden accumulation/distribution - **Quarter-Kelly sizing**: conservative risk management - **Drawdown controls**: auto-pause at 2% daily NAV - **Full audit trail**: every trade logged with entry/stop/target/outcome - **Self-improvement loop**: lessons.md updated after each loss ## Usage ``` Use hft-paper-trader to run TA on BTC and place a paper trade Use hft-paper-trader to check portfolio performance Use hft-paper-trader to scan the watchlist and trade all signals ``` ## Watchlist BTC ETH SOL XRP TRX DOGE ADA AVAX BNB LINK LTC SUI ARB OP NEAR DOT ATOM UNI MATIC ## Performance Binance paper NAV: $748+ on $750 starting capital. Daily target: 100+ trades.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: balkanblbn/sop-architect (version 1.1.0)

--- name: sop-architect description: Automatically generates detailed Standard Operating Procedures (SOPs) for recurring digital tasks. Ideal for scaling agency operations or documenting internal AI workflows. --- # SOP Architect Turn any complex task into a repeatable manual. ## Workflow 1. **Input Task**: Describe the goal (e.g., "Monthly SEO Audit"). 2. **Deconstruct**: Identify required tools, access levels, and prerequisites. 3. **Step-by-Step**: Generate 5-10 specific, numbered steps. 4. **Failure Modes**: Document what to do when things go wrong at each step. ## Output Format SOPs are saved to `SOPs/<task-name>.md`. ## Benefits - Reduces agent errors. - Makes results consistent across different sessions. - Easy to hand off to human team members. ## Installation ```bash clawhub install sop-architect ```

path: scripts/generate_sop.sh
content: #!/usr/bin/env bash
# Usage: ./generate_sop.sh "Task Name" "Step1" "Step2" ...

TASK_NAME=$1
shift
mkdir -p SOPs
FILE="SOPs/${TASK_NAME// /_}.md"

echo "# SOP: $TASK_NAME" > "$FILE"
echo "Generated on: $(date)" >> "$FILE"
echo -e "\n## Workflow Steps\n" >> "$FILE"

COUNT=1
for step in "$@"; do
  echo "$COUNT. $step" >> "$FILE"
  ((COUNT++))
done

echo -e "\n## Success Criteria\n- Task completed as described above.\n- Result verified by agent." >> "$FILE"
echo "✅ SOP created at: $FILE"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: tsangho/adaptive-reasoning-plus (version 1.0.2)

--- version: 1.0.2 name: adaptive-reasoning-plus description: >- 自适应推理增强版。当用户要求"评估复杂度"、"需要深度分析"、"复杂问题"、"多步推理"时激活。 评估每次任务的复杂度，动态决定推理深度。 0-2分快速执行，3-5分标准处理，6-7分深度思考，8+分强制启用 extended thinking。 --- # Adaptive Reasoning Plus — 自适应推理引擎 ## 核心理念 **不是所有问题都需要深度思考。** - 简单指令 → 直接执行，不废话 - 复杂问题 → 停下来想清楚再说 --- ## 复杂度评分量表 收到任务时，扫描六个信号，累加计分： | 信号 | 权重 | 触发条件 | |------|------|---------| | **多步逻辑** | +3 | 需要多步规划/执行、调试链路、证明链、因果分析 | | **歧义性** | +2 | 利弊权衡、"取决于"类问题、无标准答案 | | **形式推理** | +2 | 数学计算、算法设计、逻辑推导、形式证明 | | **新奇程度** | +1 | 首次遇到、无清晰先例、需要创造力 | | **影响范围** | +1 | 涉及多个系统/模块/团队 | | **不可逆性** | +2 | 不可逆操作、涉及真实资产/生产环境 | > ⚠️ **Tier-Override 规则**：无论总分多少，**不可逆性触发时自动升为8+强制推理**。 --- ## 推理策略（按分数段） | 分数 | 策略 | 行动 | |------|------|------| | **0-2** | ⚡ 快速执行 | 直接响应，不做深度分析 | | **3-5** | 🎯 标准处理 | 正常流程，适当思考后回答 | | **6-7** | 🤔 深度思考 | 启用推理模式，多角度分析 | | **8+** | 🧠 强制推理 | 开启 extended thinking，结构化输出 | --- ## 决策流程 ``` 收到任务 ↓ 扫描六个信号 → 累加评分 ↓ 总分判定 → 选择对应策略 ↓ 执行 → 必要时在末尾附图标 ``` --- ## 推理激活标注 当评分 ≥ 6 时，在回复中明确标注： **6-7 分（深度思考）：** ``` 🤔 复杂度：X分 [信号A+3, 信号B+2 ...] → 策略：深度思考 [分析过程...] --- [正式回复] ``` **8+ 分（强制推理）：** ``` 🧠 复杂度：X分 [信号A+3, 信号B+2 ...] → 策略：强制推理，已启用 extended thinking [结构化分析... 维度一：... 维度二：... 维度三：...] --- [正式回复] ``` --- ## 适用场景 **应该激活此技能时：** - 任务涉及多步规划或复杂决策 - 问题定义模糊，需要澄清或权衡 - 首次遇到的问题类型 - 不可逆操作前的分析 - 涉及多个系统/团队/业务线 **不需要激活时：** - 简单查询或明确指令（"查天气"、"发消息"） - 纯执行类任务（"运行这个脚本"） - 闲聊或无需分析的问题 - 2分以下的简单操作 --- ## 领域扩展 详见 `references/domain-scoring.md` 各领域增补信号示例： - **金融/投资**：本金亏损风险、流动性风险、监管政策变化 - **技术/运维**：生产环境变更、数据迁移、安全漏洞 - **法务/合同**：法律风险、合规违规 - **电商/运营**：客诉风险、库存风险、资金冻结 --- ## 落地验证标准 | 测试 | 输入 | 预期 | |------|------|------| | Test 1 | "帮我查天气" | 0分 → ⚡快速，不废话 | | Test 2 | "这个文件删了" | 2分 → ⚡直接执行 | | Test 3 | "帮我分析下这三个股票" | 6-7分 → 🤔深度思考 | | Test 4 | "设计一个分布式系统架构" | 8+分 → 🧠强制推理+结构化 | --- ## 设计原则 1. **通用优先** — 框架适用于所有任务类型和领域 2. **可扩展** — 各领域可在通用框架上增补自己的信号 3. **可量化** — 评分有明确标准，避免主观随意 4. **显式标注** — 推理决策透明可见


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: edwardrodriguez703-design/book-cake (version 1.0.1)

--- name: book-cake description: Book cake services through Lokuli MCP. Use when user needs to find and book cake. Triggers on requests like "book a cake", "find cake near me", or any cake service request. --- # uook cake Book cake services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "cake", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: olina1ye/email-followup-zh (version 1.0.0)

--- name: email-followup-zh description: > 专为销售、商务及客服团队设计的客户跟进邮件生成技能。当客户或合作方长时间未回复时， 自动生成礼貌而不失力度的跟进邮件（催回复/follow up/沟通跟进）， 兼顾维护客情关系与推动业务进展的双重目标。支持首次跟进、二次催促、 紧急跟进等多种场景，语气可根据等待时长与客户关系灵活调整， 避免过于强硬或过于软弱，帮助销售/BD/客服人员高效推进合作谈判与订单签署。 tags: [跟进邮件, follow-up, 催回复, 客户跟进, 商务沟通, 销售邮件, 沟通跟进, 邮件模板] --- # 客户跟进邮件生成器 针对客户或合作方无回复的场景，自动生成礼貌有力的跟进邮件，助力销售与商务团队高效推进沟通。 ## Tools Required - read - write ## Usage - "我上周发了报价单给客户，对方一直没回复，帮我写一封跟进邮件催一下" - "和合作方谈了两周的合同条款，最近三天没消息了，帮我写封 follow up 邮件，语气委婉但要表达紧迫感" - "客户说下周给答复，但已经过去五天了，帮我写一封二次跟进邮件，不要显得太着急" - "发出去的合作邀请邮件两周没回音，帮我重新跟进一下，顺便附上核心优势提醒对方" ## Examples 输入：发给客户的产品演示邀请邮件已过去7天，对方未回复，需要一封二次跟进邮件，客户是某零售企业采购总监，关系尚在建立阶段。 输出： 主题：Re: 产品演示邀请 — 期待与您进一步交流 王总，您好！ 上周曾向您发送了关于 [产品名称] 演示的邀请，不知您近期是否有机会查阅？ 考虑到您日程繁忙，特再次致函，希望能为您安排一次30分钟的线上演示， 届时可结合贵司零售业务的实际场景，展示如何提升 [核心价值点]。 如时间方便，欢迎回复您的空档，我将第一时间为您预约。 若目前暂无需求，也欢迎告知，我们可保持后续联系。 期待您的回复，祝工作顺利！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jdqconsulting/ue57-gamepiece-designer (version 0.1.0)

--- name: ue57-gamepiece-designer description: Designs UE5.7 multiplayer-friendly game pieces (Blueprint node chains, data schemas, asset naming, and test checklists). Text-only, no scripts. --- # UE5.7 Gamepiece Designer (Text-Only) ## What this skill does When the user asks for a UE system or “game piece”, produce a structured design that is ready to implement in Unreal Engine 5.7: - Blueprint node chain recipes (ordered steps, node names, variables, events) - DataTable / DataAsset schemas (field list + example rows) - Asset / folder plan (paths + naming) - Multiplayer sanity: server/client responsibility, replication notes - Test checklist (PIE, dedicated server, latency, edge cases) ## Non-negotiable rules (Safety) - Do NOT run terminal commands. - Do NOT instruct the user to download or run scripts. - Do NOT modify files. Output text only. - If the user asks for files, respond with file *contents* they can paste themselves. ## Output format (always) 1) **Goal** 2) **Inputs** (what variables/configs it needs) 3) **Outputs** (what it produces) 4) **Assumptions** 5) **Implementation** - **Blueprint Recipe** (step-by-step) - **Replication Notes** (Server vs Client, replicated vars, RPCs) - **Assets / Naming / Folders** 6) **Test Checklist** ## UE naming + folders (default) - Root: `/Game/Systems/<SystemName>/` - Blueprints: `BP_<Thing>` - Components: `BPComp_<Thing>` - Interfaces: `BPI_<Thing>` - DataTables: `DT_<Thing>` - DataAssets: `DA_<Thing>` - Structs/Enums: `ST_<Thing>` / `E_<Thing>` ## Multiplayer defaults (unless user says otherwise) - Authoritative actions happen on the Server - Client sends intent (RPC) when needed - Replicate only what’s necessary for 40v40+ scale - Prefer Events/Interfaces over tick-heavy logic


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