OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wanghong5233/wanghong5233-offerpilot-interview-prep (version 1.0.0)

--- name: interview-prep description: Generate interview question bank and answer strategy from JD and company intel. --- # Interview Prep Skill ## Trigger Activate when user asks: - "帮我准备这家公司的面试题" - "根据 JD 出一套面试问题" - "给我这岗位的回答思路" - "做一版可背诵的面试提纲" ## Workflow 1. Collect input: - Prefer `job_id` (from `/api/jobs/recent`) OR provide `company + role_title + jd_text`. 2. Call: - `POST http://127.0.0.1:8010/api/interview/prep` - Body example: - `{"job_id":"<job_id>","use_company_intel":true,"question_count":8}` - Or: - `{"company":"MiniAgent","role_title":"AI Agent Intern","jd_text":"...","use_company_intel":true,"question_count":8}` 3. Parse response and present: - `summary` - `likely_focus` - `key_storylines` - top interview questions (`question`, `intent`, `answer_tips`) 4. Ask user whether to export/continue with mock Q&A. ## Command templates (exec tool + curl) - By job id: - `curl -sS -X POST "http://127.0.0.1:8010/api/interview/prep" -H "Content-Type: application/json" -d '{"job_id":"<job_id>","use_company_intel":true,"question_count":8}'` - By custom input: - `curl -sS -X POST "http://127.0.0.1:8010/api/interview/prep" -H "Content-Type: application/json" -d '{"company":"MiniAgent","role_title":"AI Agent Intern","jd_text":"Need Python, LangGraph, RAG","use_company_intel":true,"question_count":8}'` ## Constraints - Keep output concise and actionable (avoid long generic theory). - If API returns non-2xx, surface the raw error and ask user whether to retry. - Do not claim interview certainty; present as "likely focus" with confidence.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: theishangoswami/code-docs-search-exa (version 1.0.0)

# Code Docs Search by Exa Up-to-date code docs and examples for AI, powered by Exa. Finds real code snippets and docs from GitHub, docs sites, and Stack Overflow. ## Setup Connect MCP server: `https://mcp.exa.ai/mcp` No API key needed. ## Tool `get_code_context_exa` ## What it does Finds real code snippets and docs from GitHub, docs sites, and Stack Overflow. Great for correct syntax, setup guides, and usage examples. ## Inputs - `query` (string, required): What code or doc you want to find. - `tokensNum` (string or int, optional): How much context to return. Use `dynamic` for auto length, or a number like `10000`. `dynamic` is recommended. ## Examples Basic: ``` get_code_context_exa { "query": "React useState example" } ``` More context: ``` get_code_context_exa { "query": "Current Next.js app router with TypeScript setup", "tokensNum": 8000 } ``` Shorter output: ``` get_code_context_exa { "query": "pandas groupby examples", "tokensNum": 2000 } ``` ## When to Use - Finding correct syntax for a library or SDK - Getting real code examples for a feature - Reviewing setup steps or best practices ## Notes - Ask for specific versions to improve accuracy. - Larger `tokensNum` returns more context but costs more. ## References - Exa MCP: https://exa.ai/docs/reference/exa-mcp - Exa Code (Context API): https://docs.exa.ai/reference/context


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: binance-grid-trading (version 1.0.1)

--- name: binance-grid-trading description: 网格交易策略 - 自动化低买高卖。每次调用自动扣费 0.001 USDT version: 1.0.0 author: moson tags: - binance - grid - trading - bot homepage: https://github.com/moson/binance-grid-trading metadata: clawdbot: requires: env: - SKILLPAY_API_KEY triggers: - "grid trading" - "binance grid" - "range trading" - "automated trading" config: SKILLPAY_API_KEY: type: string required: true secret: true --- # Binance Grid Trading 在指定价格区间内自动网格交易，低买高卖。 ## 什么是网格交易？ 网格交易是一种自动化交易策略，将价格区间分成若干网格，在每个网格价格自动买入或卖出。 ### 核心功能 - **区间设置**: 自定义价格上限和下限 - **网格数量**: 可调整网格密度（5-100格） - **自动套利**: 价格触网格自动买卖 - **利润追踪**: 实时显示已获利金额 ### 策略参数 - 价格区间: 用户自定义（如 $42,000-$45,000） - 网格数量: 5-100 - 订单类型: 限价单 - 止盈策略: 可选 ## 使用示例 ```javascript // 查看网格状态 await handler({ action: 'status' }); // 创建新网格 await handler({ action: 'start', pair: 'BTC/USDT', min: 42000, max: 45000, grids: 10 }); // 停止网格 await handler({ action: 'stop' }); ``` ## 价格 每次调用: 0.001 USDT ## 风险提示 1. 震荡市场效果最好 2. 单边趋势可能导致亏损 3. 需充足资金覆盖所有网格 4. 建议设置止盈止损 5. 价格突破区间需要手动干预 ## 网格交易技巧 1. 选择震荡剧烈的币种 2. 合理设置价格区间 3. 预估资金需求 4. 定期检查和调整 5. 设置自动止损 ## 适合人群 - 不想时刻盯盘的投资者 - 喜欢稳健收益的交易者 - 对市场有区间判断能力的用户


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: googleworkspace-bot/recipe-share-folder-with-team (version 1.0.12)

--- name: recipe-share-folder-with-team description: "Share a Google Drive folder and all its contents with a list of collaborators." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "productivity" requires: bins: - gws skills: - gws-drive --- # Share a Google Drive Folder with a Team > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-drive` Share a Google Drive folder and all its contents with a list of collaborators. ## Steps 1. Find the folder: `gws drive files list --params '{"q": "name = '\''Project X'\'' and mimeType = '\''application/vnd.google-apps.folder'\''"}'` 2. Share as editor: `gws drive permissions create --params '{"fileId": "FOLDER_ID"}' --json '{"role": "writer", "type": "user", "emailAddress": "[REDACTED_SECRET]"}'` 3. Share as viewer: `gws drive permissions create --params '{"fileId": "FOLDER_ID"}' --json '{"role": "reader", "type": "user", "emailAddress": "[REDACTED_SECRET]"}'` 4. Verify permissions: `gws drive permissions list --params '{"fileId": "FOLDER_ID"}' --format table`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: akiry09/mx-mx-finance-data (version 0.1.0)

--- name: mx_finance_data description: 基于东方财富数据库，支持自然语言查询金融数据，覆盖A港美、基金、债券等多种资产，含实时行情、公司信息、估值、财务报表等，可用于投资研究、交易复盘、市场监控、行业分析、信用研究、财报审计、资产配置等场景，适配机构与个人多元需求。返回结果包含数据说明及 xlsx 文件。Natural language query for financial data across all markets, including A-shares, ETFs, bonds, Hong Kong and US stocks, and funds. It provides L1/L2 data, financial indicators, company profiles and valuation metrics. Ideal for investment research, strategy backtesting, market monitoring and industry analysis. It meets the needs of diverse institutions and individuals. metadata: { "openclaw": { "requires": { "env":["EM_API_KEY"] }, "install": [ { "id": "pip-deps", "kind": "python", "package": "httpx pandas openpyxl", "label": "Install Python dependencies" } ] } } --- # 金融数据查询 ## 密钥来源与安全说明 - 本技能仅使用一个环境变量：`EM_API_KEY`。 - `EM_API_KEY` 由东方财富妙想服务（`https://ai.eastmoney.com/mxClaw`）签发，用于其接口鉴权。 - 在提供密钥前，请先确认密钥来源、可用范围、有效期及是否支持重置/撤销。 - 禁止在代码、提示词、日志或输出文件中硬编码/明文暴露密钥。 ## 功能范围 ### 1. 支持查询的对象范围 * 股票（A 股、港股、美股） * 板块、指数、股东 * 企业发行人、债券、非上市公司 * 股票市场、基金市场、债券市场 ### 2. 支持查询的数据类型 支持查询以下类型的结构化数据： * **实时行情**（现价、涨跌幅、盘口数据等） * **量化数据**（技术指标、资金流向等） * **报表数据**（营收、净利润、财务比率等） ### 3. 支持的查询方式与**严格限制** 支持多实体、多指标、多时间范围的组合查询。**受后端接口限制，单次查询必须遵循以下配额：** * **实体上限**：单次最多支持 **5 个** 股票/金融实体。 * **处理逻辑**：若用户请求超过上述限制，Skill 将优先处理前 5 个实体，并在结果说明文件中予以提示。 > **示例：** “查询 A、B、C、D、E、F 六只股票的营收” -> 仅返回前五只。 ### 4. 输出结果 Skill 执行后会输出以下文件： - 一个查数结果 `xlsx` 文件，用于承载结构化查询结果 - 一个结果说明 `txt` 文件，用于描述查询内容、结果含义及必要说明 ------ ## 查询示例 ### 基础指标查询 - 东方财富的基本面 - 贵州茅台最近一年的营业收入和净利润 - 半导体etf的业绩表现 ### 实时行情与盘口查询 - 当前300059的实时买单 - 英伟达现在的最新价和涨跌幅 - 沪深300当前点位和成交额 ### 多实体、多指标、多时间查询（注意配额） * **合规示例**：对比创业板指、沪深300、中证500（3个实体）春节以来的涨幅（1个指标）。 * **合规示例**：东方财富、拼多多（2个实体）最近一年的营收、毛利、净利（3个指标）。 * **超限提示**：若查询“沪深前十大权重股的PE”，系统将截取前 5 名进行查询。 ------ ## 补充说明 - 本 Skill 面向金融结构化数据与关系数据查询，不侧重主观分析、投资建议或资讯解读。 - 语句中必须包含明确的金融实体名称。 - 当用户使用自然语言发起查询时，Skill 可根据问句内容识别查询对象、指标和时间范围，并输出结构化结果文件。 - 对于支持范围内的查询请求，优先输出结构化查数结果，并同时生成结果说明文件，便于后续使用。 ## 前提条件 ### 1. 注册东方财富妙想账号 访问 https://ai.eastmoney.com/mxClaw 注册账号并获取API_KEY。 ### 2. 配置 Token ```bash # macOS 添加到 ~/.zshrc，Linux 添加到 ~/.bashrc export EM_API_KEY="your_api_key_here" ``` 然后根据系统执行对应的命令： **macOS：** ```bash source ~/.zshrc ``` **Linux：** ```bash source ~/.bashrc ``` ### 3. 安装依赖 ```bash pip3 install httpx pandas openpyxl --user ``` ## 快速开始 ### 在工作目录下执行 ```bash python3 {baseDir}/scripts/get_data.py --query "贵州茅台近期走势如何" ``` ### 输出示例 ``` xlsx: /path/to/miaoxiang/mx_finance_data/mx_finance_data_9535fe18.xlsx 描述: /path/to/miaoxiang/mx_finance_data/mx_finance_data_9535fe18_description.txt 行数: 42 ``` ### 输出文件说明 | 文件 | 说明 | | --- | --- | | `mx_finance_data_<查询id>.xlsx` | 结构化数据表，包含请求的实体与指标 | | `mx_finance_data_<查询id>_description.txt` | 包含查询逻辑说明、字段含义及**配额截断提示** |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dht9898-cell/auto-social-post (version 1.0.0)

--- name: auto-social-post description: 社交媒体自动化 - 自动发布到 Twitter/Instagram/LinkedIn version: 1.0.0 emoji: 🤖 license: MIT-0 metadata: openclaw: requires: env: [] bins: ["curl"] primaryEnv: bash --- # auto-social-post 社交媒体自动化 - 自动发布到 Twitter/Instagram/LinkedIn ## 价格：$150 USDC ## 功能特性 - 自动化执行，7x24 小时运行 - API 集成，支持主流平台 - 配置灵活，易于定制 - 实时监控，自动优化 ## 安装方式 ```bash clawhub install auto-social-post ``` ## 使用示例 详见文档 ## 收益潜力 - 预期月收益：$300-$((150 * 4)) - 回本周期：1-2 个月 - 边际成本：几乎为零


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shaozhengmao/skill-quality-checker (version 1.0.0)

--- name: skill-quality-checker description: > Skill 质量检测工具。对已安装的 skills 进行自动化质量评估，按5个维度打分 （问题-方案匹配度、完成度、容错性、Description精度、Token效率），输出评分报告和改进建议。 触发词：检查skill质量、评估skill、skill质量报告、skill review、审查skill、check skill quality、evaluate skill、skill quality report、audit skill. --- # Skill 质量检测工具 自动评估已安装 skills 的质量，输出评分报告。 ## 使用方式 运行脚本扫描并评分： ```bash # 扫描所有 skills python3 {SKILL_DIR}/scripts/check_quality.py --scan-dir /root/.openclaw/skills/ # 扫描单个 skill python3 {SKILL_DIR}/scripts/check_quality.py --skill /root/.openclaw/skills/some-skill/ # 输出 markdown 报告到文件 python3 {SKILL_DIR}/scripts/check_quality.py --scan-dir /root/.openclaw/skills/ --format markdown --output /tmp/report.md # 输出 JSON python3 {SKILL_DIR}/scripts/check_quality.py --scan-dir /root/.openclaw/skills/ --format json --output /tmp/report.json ``` `{SKILL_DIR}` 替换为本 skill 的实际路径（即 SKILL.md 所在目录）。 ## 评分维度（满分100） | 维度 | 分值 | 核心检查点 | |----------------|----|-------------------| | 问题-方案匹配度 | 20 | 任务类型与实现方案是否匹配 | | 完成度 | 20 | 文件完整性、格式规范、无 TODO | | 容错性 | 20 | 错误处理、fallback 机制 | | Description 精度 | 20 | 触发词覆盖、长度适中、不泛化 | | Token 效率 | 20 | 文件大小、渐进式披露 | 评级：⭐⭐⭐⭐⭐(90+) ⭐⭐⭐⭐(75-89) ⭐⭐⭐(60-74) ⭐⭐(<60) ## 工作流程 1. 运行 `check_quality.py`，获取评分报告 2. 阅读报告中的改进建议 3. 如需详细评分标准，参考 `references/scoring-criteria.md` ## 注意事项 - 脚本只用 Python 标准库，无需额外安装 - 评分为启发式静态分析，供参考，不替代人工审查 - 如果扫描目录不存在或为空，脚本会给出明确提示


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wyzmf/polymarket-scanner (version 1.0.0)

--- name: polymarket-scanner description: Automated Polymarket prediction market scanner and trader via Simmer API. Use when analyzing prediction markets, scanning for trading opportunities, executing virtual ($SIM) or real (USDC) trades, checking positions, or managing a Polymarket portfolio. Triggers on "polymarket", "prediction market", "simmer", "trade", "market scan", "betting odds", "arbitrage opportunities". version: 1.0.0 --- # Polymarket Scanner Automated prediction market scanner that finds mispriced markets and executes trades via the Simmer Markets API. ## Setup ### Required - `SIMMER_API_KEY` env var (get from https://simmer.markets) - Simmer agent registered and claimed ### Optional - Telegram bot for trade notifications ## Commands ### Auto-scan (default) ```bash python scripts/scanner.py ``` Scans top 20 opportunities, filters by score/divergence/warnings, executes qualifying trades. ### Check positions ```bash python scripts/scanner.py --positions ``` ### Check balance ```bash python scripts/scanner.py --status ``` ## Trading Rules (built-in) | Rule | Virtual ($SIM) | Real (USDC) | |------|---------------|-------------| | Single trade | ≤10% balance | ≤$100 | | Daily limit | ≤30% balance | ≤$500 | | Stop-loss | -15% | -10% | | Max positions | 5 | 3 | | Min opportunity score | 25 | 40 | | Min divergence | 3% | 5% | ## Strategy 1. **Cross-platform arbitrage** — Price divergence between Polymarket and external sources 2. **Momentum shifts** — Rapid price changes indicating mispricing 3. **Data-driven events** — Weather, economic data, quantifiable outcomes preferred ## Architecture ``` Cron (every 4h) → scanner.py → Simmer API ├── Get balance & positions ├── Scan opportunities (score, divergence, warnings) ├── Filter by trading rules ├── Execute qualifying trades (SIM auto, USDC manual) └── Log to trading_log.jsonl ``` ## Risk Controls - All trades logged to `trading_log.jsonl` - Serious spread warnings (>10%) auto-skip - Already-held markets auto-skip - Balance <$100 auto-stop - Real USDC trades require human confirmation


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ylongw/framebuffer-dump (version 1.0.0)

--- name: framebuffer-dump description: Dump the current STM32 LCD framebuffer via J-Link and convert it to PNG for visual comparison with Figma. Use when user asks to export, snapshot, dump, or capture what is currently displayed on device screen. --- # Framebuffer Dump (J-Link → PNG) Export the **actual on-device rendered frame** directly from SDRAM framebuffer and convert to PNG. ## Default parameters (PRO2) - Device: `STM32H747XI_M7` - FB base: `0xD0000000` - Resolution: `604x1024` - Pixel format: `RGB888` (3 bytes/pixel) - Dump size: `604*1024*3 = 1,855,488 (0x1C5000)` ## One-shot workflow ### 1) Create J-Link command file Use `scripts/dump_fb.jlink.template` and replace placeholders: - `{{DEVICE}}` - `{{SPEED_KHZ}}` (e.g. `12000`) - `{{OUT_BIN}}` (absolute path) - `{{FB_ADDR}}` (e.g. `0xD0000000`) - `{{FB_SIZE}}` (e.g. `0x1C5000`) ### 2) Dump raw framebuffer ```bash JLinkExe -NoGui 1 -CommandFile /tmp/jlink_dump_fb.jlink > /tmp/jlink_dump_fb.log 2>&1 ``` ### 3) Convert raw to PNG ```bash python3 scripts/dump_fb.py \ --in /path/to/fb_dump.bin \ --out /path/to/fb_dump_604x1024.png \ --width 604 --height 1024 --stride 3 ``` ## Troubleshooting - If output bin is 0 bytes: check J-Link connection / path permissions. - If colors look wrong: verify pixel format is RGB888 (not BGR / RGB565). - If image shifted/corrupt: verify width/height match current display mode. - If command hangs too long: make sure dump size is correct (`0x1C5000` for 604x1024 RGB888). ## Notes - This method captures **real framebuffer pixels** (no camera distortion). - Best for Figma-vs-device pixel comparison and regression snapshots.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wanghong5233/company-intel (version 1.0.1)

--- name: company-intel description: Build structured company intelligence for interview preparation. --- # Company Intel Skill ## Trigger Activate when user asks: - "帮我调研这家公司" - "这个公司做什么、技术栈是什么？" - "整理这家公司的面试风格" - "给我公司情报报告" ## Workflow 1. Collect input: - `company` (required) - `role_title` (optional) - `jd_text` (optional) 2. Call: - `POST http://127.0.0.1:8010/api/company/intel` - Body example: - `{"company":"MiniAgent","role_title":"AI Agent Intern","focus_keywords":["技术栈","面试流程"],"max_results":6,"include_search":true}` 3. Parse response and show: - `summary` - `business_direction` - `tech_stack` - `funding_stage` / `team_size_stage` - `interview_style` - `risks` + `suggestions` 4. If user asks for deeper prep, suggest using `interview-prep` skill next. ## Command templates (exec tool + curl) - Generate company intel: - `curl -sS -X POST "http://127.0.0.1:8010/api/company/intel" -H "Content-Type: application/json" -d '{"company":"MiniAgent","role_title":"AI Agent Intern","focus_keywords":["技术栈","面试流程"],"max_results":6,"include_search":true}'` ## Constraints - If company name is missing, ask user to provide it first. - If API call fails, show exact error and ask user whether to retry. - Never fabricate facts as certainty; indicate confidence and uncertainty explicitly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kowl64/dvsa-tc-audit-readiness-operator-licence-uk (version 1.0.0)

--- name: dvsa-tc-audit-readiness-operator-licence-uk description: Builds DVSA/Traffic Commissioner “show me” audit readiness checklists and evidence indexes. USE WHEN preparing for audits or operator licence scrutiny. --- # DVSA & Traffic Commissioner Audit Readiness (UK) ## PURPOSE Produce “show me” readiness materials: today’s checklist, an evidence index, and a gaps register aligned to operator-licence sensitivity and audit expectations. ## WHEN TO USE - “Prep me for a DVSA visit and give me a checklist for today.” - “Create a customer audit response pack for [CUSTOMER] and list gaps.” - “Build an evidence index for operator licence compliance.” - “What should we have ready to show an auditor today?” DO NOT USE WHEN… - The request is generic compliance chat with no artefact needed. - Pure operations/customer service requests (routes/pricing/performance) not compliance-led. ## INPUTS - REQUIRED: - Audit context (DVSA visit / TC inquiry readiness / customer audit) and date - Scope: which depot/operating centre/fleet, and period (e.g., last 28/90 days) - OPTIONAL: - Your internal SOPs/policies (paste text) and retention rules - Prior audit findings/actions - EXAMPLES: - “DVSA site visit today at Depot X; need show-me checklist and evidence index.” ## OUTPUTS - `dvsa-visit-today-checklist.md` - `audit-evidence-index.md` (Excel-ready table) - `gaps-register.md` - Success criteria: - Practical, checkable items - Clear “where to find it” references - Highlights operator licence sensitivities (without legal claims beyond your policy text) ## WORKFLOW 1. Confirm audit type and scope. - IF missing → **STOP AND ASK THE USER** for audit type, depot/fleet, and time period. 2. Generate today’s “show me” checklist using `assets/dvsa-visit-today-checklist-template.md`. 3. Build an evidence index (what, where stored, owner, retention, last updated) using `assets/audit-evidence-index-template.md`. 4. Identify likely gaps: - Mark unknowns as “Gap – confirm source/owner”. - Output `gaps-register.md` via `assets/gaps-register-template.md`. 5. Operator licence sensitivity: - Add a short section referencing `references/operator-licence-sensitivity-placeholders.md` and map to your internal policies. 6. If the user wants edits to existing files → **ASK FIRST**. ## OUTPUT FORMAT ```text # dvsa-visit-today-checklist.md Audit type: Scope: Date: ## Immediate readiness (today) - … ## Documents to pull (and where) - … ## People/process readiness (“show me”) - … ## Known risks / sensitivities - … ``` ## SAFETY & EDGE CASES - Don’t invent retention periods or legal duties; ask for internal policy text if needed. - If asked for “is this legal?”, stop and request the precise records and desired output artefact. ## EXAMPLES - Input: “DVSA visit today” - Output: checklist + evidence index + gaps register for rapid action


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: imwyvern/love-reply (version 1.0.2)

--- name: love-reply description: > Love Reply is an AI romantic reply assistant for crush texts, flirty banter, and moments when you want to sound warm, playful, and genuinely attractive. It helps people craft romantic messages, keep momentum after a good chat, respond to mixed signals, and choose replies that feel natural instead of cheesy. 恋爱回复、暧昧聊天、撩人话术、crush 文案。 metadata: openclaw: emoji: "💌" --- # Love Reply — Never Run Out of Things to Say You are a charismatic dating communication expert. You help people craft irresistible replies in romantic conversations — whether they're texting a new crush, flirting on dating apps, or keeping the spark alive with a partner. Your replies feel natural, never scripted. You match the energy of the conversation and elevate it. ## Language Rule **Reply in the user's language.** Chinese → Chinese. English → English. Always. ## How It Works When the user shares a message they received from someone they're interested in: ### Step 1 — Quick Read One sentence: what's the vibe of this message? (flirty / testing / warm / cold / neutral) ### Step 2 — 5 Replies **A) Playful tease** — Light humor, flip what they said, show you're fun **B) Confident & direct** — Take charge, suggest plans, no hesitation **C) Push-pull** — Show interest then create intrigue in the same breath **D) Sweet & genuine** — Warm, emotionally honest, make them feel special **E) Mysterious** — Short, intriguing, leave them wanting more (max 8 words) ### Step 3 — Best Move Which reply fits this specific situation and why. ## Reply Rules - **Under 25 words each** — Shorter = more attractive - **Never start with "haha" or "lol"** — Lead with substance - **No double texts** — One clean message, not two fragments - **Match the platform** — Dating app replies are shorter than WeChat messages - **Timing note** — Suggest when to reply (immediately? Wait 30 min? Next morning?) ## Response Format ``` 💕 Vibe: [one-word read + brief explanation] 💬 Replies: A) Playful tease → [reply] B) Confident & direct → [reply] C) Push-pull → [reply] D) Sweet & genuine → [reply] E) Mysterious → [reply] 🎯 Best move: [recommendation + timing] ``` ## Situation Awareness Adapt your style based on context clues: - **First messages / ice-breaking** → More playful, less intense - **Been talking for weeks** → Can be more direct and personal - **After a date** → Reference shared experiences - **They're pulling away** → Don't chase. Create intrigue. - **They're clearly interested** → Match their energy, don't play too cool ## Safety - If someone says "no" or "stop" → Help them exit gracefully - No manipulation tactics (guilt-tripping, love-bombing scripts) - If the situation sounds unhealthy → Name it directly - You're a wingman, not a puppet master ## More ``` --- 💌 Want replies that remember your relationship history? → replyher.com/pro ```


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