OpenClaw/clawhub-security-signals huggingface dataset:
Skill: justasknudge/model-usage-skill (version 0.1.0)

--- name: model-usage description: Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON. metadata: { "openclaw": { "emoji": "📊", "os": ["darwin"], "requires": { "bins": ["codexbar"] }, "install": [ { "id": "brew-cask", "kind": "brew", "cask": "steipete/tap/codexbar", "bins": ["codexbar"], "label": "Install CodexBar (brew cask)", }, ], }, } --- # Model usage ## Overview Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude. TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux. ## Quick start 1. Fetch cost JSON via CodexBar CLI or pass a JSON file. 2. Use the bundled script to summarize by model. ```bash python {baseDir}/scripts/model_usage.py --provider codex --mode current python {baseDir}/scripts/model_usage.py --provider codex --mode all python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --pretty ``` ## Current model logic - Uses the most recent daily row with `modelBreakdowns`. - Picks the model with the highest cost in that row. - Falls back to the last entry in `modelsUsed` when breakdowns are missing. - Override with `--model <name>` when you need a specific model. ## Inputs - Default: runs `codexbar cost --format json --provider <codex|claude>`. - File or stdin: ```bash codexbar cost --provider codex --format json > /tmp/cost.json python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current ``` ## Output - Text (default) or JSON (`--format json --pretty`). - Values are cost-only per model; tokens are not split by model in CodexBar output. ## References - Read `references/codexbar-cli.md` for CLI flags and cost JSON fields.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: enjoytdl/xiaohongshu-publish (version 1.0.0)

--- name: xiaohongshu-publish description: 小红书长文发布。用于在网页版小红书创作服务平台发布长文笔记。当用户说"发小红书"、"发小红书长文"、"写一篇小红书"时使用此技能。 --- # 小红书长文发布 ## 使用场景 用户想在小红书发布长文笔记时使用。 ## 发布流程 ### 1. 进入发布页面 浏览器打开: https://creator.xiaohongshu.com/publish/publish?source=official ### 2. 选择"写长文" - 点击侧边栏的"写长文" - 点击"新的创作" ### 3. 输入内容 - 点击标题输入框，输入标题 - 点击正文区域，输入正文内容（可以分多次输入） ### 4. 一键排版 - 点击"一键排版"按钮 ### 5. 选择模板 - 在右侧选择模板（第一个"简约基础"即可） - 选择后点击"下一步" ### 6. 发布 - 在发布页面确认内容 - 点击"发布"按钮 ## 注意事项 - 浏览器使用OpenClaw内置浏览器即可 - 如果页面元素有变化，根据实际情况调整 - 发布成功后可以进入"笔记管理"确认


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: terrycarter1985/ecom-after-sales-cs (version 2.0.0)

--- name: ecom-after-sales-cs description: 电商售后客服规范应答Skill。支撑客服人员快速生成符合品牌服务规范的合规应答内容，覆盖退换货申请、物流异常咨询、售后补偿协商三类高频售后诉求。使用场景：(1)用户发起退换货申请需规范回复，(2)用户咨询物流延迟/丢件/破损等异常问题，(3)用户要求补偿/赔偿/优惠券等售后协商。纯提示词类资产，不含代码。 --- # 电商售后客服规范应答 ## 适用场景 1. **退换货申请** — 用户申请退货、换货、仅退款 2. **物流异常咨询** — 用户反馈物流延迟、丢件、破损、错发 3. **售后补偿协商** — 用户要求赔偿、补偿券、差价退还、赠品 ## 输入字段 | 字段 | 类型 | 必填 | 说明 | |------|------|------|------| | scenario | enum[退换货申请,物流异常咨询,售后补偿协商] | 是 | 当前售后场景类型 | | user_message | string | 是 | 用户原始咨询内容 | | order_info | string | 否 | 订单号、商品、金额等上下文 | | policy_hint | string | 否 | 本单适用的售后政策摘要 | ## 应答规范（response_rules） 1. **问候先行**：以品牌标准问候语开头，称呼用户为"您" 2. **共情表达**：先对用户遭遇表示理解/歉意，再做处理说明 3. **政策引用**：明确引用退换货/物流/补偿政策条款，禁止编造规则 4. **方案明确**：给出唯一或有限可选项，避免模糊措辞（如"可能""大概"） 5. **操作指引**：分步骤说明用户需执行的操作，步骤编号清晰 6. **时效承诺**：注明处理/退款/补发预计时效 7. **服务收尾**：以"感谢您的理解与支持"类话术结束，留联系方式 8. **禁止承诺超权限内容**：不得承诺超出品牌授权的补偿方案 ## 禁用内容清单（forbidden） - 禁止使用敷衍性回复："亲，不好意思哦""这个我们也没办法" - 禁止推诿责任："这是快递的问题，跟我们无关" - 禁止编造政策条款或时效 - 禁止承诺超出权限的补偿（如未经审批的全额退款+大额补偿券） - 禁止泄露内部流程/系统/成本信息 - 禁止使用未经品牌审核的网络流行语或不当语气 - 禁止复制粘贴未经适配的模板（如订单信息占位符未替换） ## Few-Shot 示例 ### 场景一：退换货申请 **用户**：我收到的衣服尺码不对，想换货，订单号DD20260512001 **客服**：您好，非常抱歉给您带来不便！理解您收到尺码不符商品的心情。根据我们的退换货政策，支持签收后7天内无理由换货。为您办理换货流程如下： 1. 请在"我的订单"中找到订单DD20260512001，点击"申请换货" 2. 选择换货原因"尺码不符"，填写正确尺码 3. 提交后系统将在1个工作日内审核，审核通过后寄回地址将以短信发送给您 4. 我们收到退回商品并验收后，将在2个工作日内为您寄出正确尺码的商品 换货期间运费由我方承担，请您放心。感谢您的理解与支持！如有其他疑问，可随时联系在线客服。 ### 场景二：物流异常咨询 **用户**：我的快递已经5天没更新了，是不是丢了？订单DD20260508003 **客服**：您好，非常理解您的担忧！物流长时间未更新确实让人着急。我们马上为您核查订单DD20260508003的物流状态。处理方案如下： 1. 我方将立即联系物流公司进行件态追踪，预计1-2个工作日内反馈结果 2. 若确认包裹丢失，我们将为您免费补发同款商品或办理全额退款 3. 若包裹仍在运输途中，我们将提供加急配送方案 请您稍候，我们的专员会在24小时内通过短信或电话与您联系告知进度。感谢您的耐心等待！如需进一步帮助，可随时联系在线客服。 ### 场景三：售后补偿协商 **用户**：收到的商品有划痕，虽然不影响使用，但心里不舒服，你们得给个说法吧？ **客服**：您好，非常抱歉商品出现划痕影响了您的体验！您的感受我们完全理解。根据售后补偿政策，针对商品外观瑕疵，我们提供以下补偿方案供您选择： 1. 方案A：保留商品，补偿20元无门槛优惠券（3个工作日内到账） 2. 方案B：保留商品，补偿10元现金退款至原支付账户（3-5个工作日到账） 3. 方案C：办理退货退款，运费由我方承担 请您选择倾向的方案，我们将尽快为您处理。感谢您的理解与支持！如有其他诉求，可随时联系在线客服协商。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: harshhmaniya/x-post-automation (version 1.0.0)

--- name: x-automation description: Automates the process of identifying trends on X (Twitter), generating opinionated/engaging content, and posting it. Use when asked to post to X, run X automation, or check trends for content creation. --- # X (Twitter) Automation Skill This skill provides a structured workflow for generating and posting high-engagement content on X. ## Workflow 1. **Context Gathering**: - Use `browser(profile="chrome")` to visit `https://x.com/home`. - Scroll for ~30 seconds to absorb the timeline's "vibe" and current discourse. 2. **Trend Identification**: - Navigate to `https://x.com/explore/`. - Identify 5 distinct trending topics from the main list. 3. **Trend Analysis (Deep Dive)**: - For each trend, search: `https://x.com/search?q={trend}&src=trend_click&vertical=trends`. - Read the **Top 5 tweets** per trend. 4. **Content Generation**: - Generate **3 candidate tweets**. - **Constraint**: Opinions are encouraged! Be bold, witty, or opinionated to drive engagement. - Include relevant hashtags but keep them minimal (1-2). 5. **Logging & Selection**: - Write candidates to `memory/x-daily-candidates.log`. - Select the best one based on viral potential and alignment with the current timeline vibe. 6. **Publication**: - Navigate to `https://x.com/compose/post`. - Type the selected text and click "Post". 7. **Notification**: - Notify the user via the primary channel (Telegram/Webchat) of success or failure. - Include the best draft in case of failure. ## Troubleshooting - **Browser Relay**: Ensure a tab is connected in Chrome via the OpenClaw extension. - **Failure Logging**: Always log errors to `memory/x-automation-logs.md`. ## Content Style Guide - **Concise**: Keep it punchy. - **Opinionated**: Don't just report facts; give a "hot take". - **Engaging**: Use questions or strong statements to prompt replies.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: thebluehouse75/metrillm (version 0.2.11)

--- name: metrillm description: Find the best local LLM for your machine. Tests speed, quality and RAM fit, then tells you if a model is worth running on your hardware. argument-hint: "[model-name]" author: MetriLLM source: https://github.com/MetriLLM/metrillm license: Apache-2.0 allowed-tools: Bash, Read install: npm install -g metrillm --- # MetriLLM — Find the Best LLM for Your Hardware Test any local model and get a clear verdict: is it worth running on your machine? ## Prerequisites 1. **Node.js 20+** — check with `node -v` 2. **Ollama** or **LM Studio** installed and running - Ollama: [ollama.com](https://ollama.com), then `ollama serve` - LM Studio: [lmstudio.ai](https://lmstudio.ai), load a model and start the server 3. **MetriLLM CLI** — install globally: ```bash npm install -g metrillm ``` ## Usage ### List available models ```bash ollama list ``` ### Run a full benchmark ```bash metrillm bench --model $ARGUMENTS --json ``` This measures: - **Performance**: tokens/second, time to first token, memory usage - **Quality**: reasoning, math, coding, instruction following, structured output, multilingual - **Fitness verdict**: EXCELLENT / GOOD / MARGINAL / NOT RECOMMENDED ### Performance-only benchmark (faster) ```bash metrillm bench --model $ARGUMENTS --perf-only --json ``` Skips quality evaluation — measures speed and memory only. ### View previous results ```bash ls ~/.metrillm/results/ ``` Read any JSON file to see full benchmark details. ### Share to the public leaderboard ```bash metrillm bench --model $ARGUMENTS --share ``` Uploads your result to the [MetriLLM community leaderboard](https://metrillm.dev) — an open, community-driven ranking of local LLM performance across real hardware. Compare your results with others and help the community find the best models for every setup. Shared data includes: model name, scores, hardware specs (CPU, RAM, GPU). No personal data is sent. ## Interpreting Results | Verdict | Score | Meaning | |---|---|---| | EXCELLENT | >= 80 | Fast and accurate — great fit | | GOOD | >= 60 | Solid — suitable for most tasks | | MARGINAL | >= 40 | Usable but with tradeoffs | | NOT RECOMMENDED | < 40 | Too slow or inaccurate | Key metrics to highlight: - `tokensPerSecond` > 30 = good for interactive use - `ttft` < 500ms = responsive - `memoryUsedGB` vs available RAM = will it fit? ## Tips - Use `--perf-only` for quick tests - Close GPU-intensive apps before benchmarking - Benchmark duration varies depending on model speed and response length ## Open Source MetriLLM is free and open source (Apache 2.0). Contributions, issues, and feedback are welcome: [github.com/MetriLLM/metrillm](https://github.com/MetriLLM/metrillm)


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

# Auto Log Skill **Auto Log Skill** — Automatically records AI agent activity to daily memory log files. 📖 [中文文档](README.zh-CN.md) --- ## 🎯 Features - ✅ Auto-create daily log templates - ✅ Append important events to the log - ✅ Record task execution status - ✅ Add and manage todo items - ✅ Quickly retrieve today's log summary --- ## 📦 Installation ```bash clawhub install auto-log ``` --- ## 🔧 Configuration Copy the config template and fill in your values: ```bash cp config.example.json config.json ``` **Required fields:** - `memory_dir`: Directory to store log files --- ## 🚀 Usage ```python from auto_log_skill import log_event, log_task, add_todo # Log an event log_event("Skill packaging complete") # Log a task log_task("RAG memory retrieval", "✅", "Success") # Add a todo add_todo("Team sync at 3 PM") # Get summary print(get_today_summary()) ``` --- ## 🛠️ Required Tools - `file_read`: Read log files - `file_write`: Write log content --- ## 📝 Changelog ### v1.0.0 (2026-03-17) - ✅ Initial release - ✅ Auto daily log creation - ✅ Event / task / todo recording - ✅ Summary generation --- ## 🤝 Contributing GitHub: https://github.com/openclaw/skills --- ## 📄 License MIT License --- **Author**: socneo **Last updated**: 2026-03-18


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hongjiahao371-pixel/ssti-test (version 1.0.0)

--- name: ssti-test inline: true --- # SSTI - 色色TI 午夜床品人格测试 ## 测试链接 - **SSTI测试网站**: https://ssti.faxianpindao.com/ssti.html - **什么值得买文章**: https://post.smzdm.com/p/ad75dpdk/ ## 测试信息 - 出品：香蕉君 - 题目数量：20道灵魂拷问 - 测试时长：约4分钟 - 内容：揭秘你的午夜真面目 - 限制：仅供18岁以上成年人娱乐 ## 使用方法 1. 打开测试链接：https://ssti.faxianpindao.com/ssti.html 2. 回答20道选择题 3. 查看测试结果和人格分析 4. 可截图保存或分享给好友 ## 相关资源 - 香蕉君主页：发现好物 - 什么值得买社区：查看更多评测和分享 ## 注意事项 ⚠️ 本测试仅供娱乐，仅供18岁以上成年人使用 ⚠️ 请理性对待测试结果 ⚠️ 分享给好友时请注意场合


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: terrycarter1985/tc-gifgrep (version 1.0.0)

--- name: gifgrep description: Search, preview, download, and process GIFs from Tenor and Giphy author: steipete author-url: https://github.com/steipete/gifgrep version: 1.0.0 tags: ["gif", "media", "search", "images"] metadata: { "requires": { "bins": ["gifgrep"], "env": ["GIPHY_API_KEY", "TENOR_API_KEY"] }, "install": [ { "type": "homebrew", "package": "steipete/tap/gifgrep", "label": "Install GifGrep CLI (Homebrew)" }, { "type": "go", "package": "github.com/steipete/gifgrep/cmd/gifgrep@latest", "label": "Install GifGrep CLI (Go)" } ] } --- # GifGrep GIF Search Skill ## Overview Search, preview, download, and process GIFs from Tenor and Giphy providers. Supports CLI output, interactive TUI with previews, static frame extraction, and frame sheet generation. ## Configuration | Environment Variable | Description | |----------------------|-------------| | `GIPHY_API_KEY` | Optional for Giphy provider (uses demo key if not set) | | `TENOR_API_KEY` | Optional for Tenor provider (uses demo key if not set) | ## Usage Examples ### Search for GIFs ```json { "tool": "gifgrep_search", "parameters": { "query": "excited reaction", "limit": 5, "format": "url" } } ``` ### Search and download top GIF ```json { "tool": "gifgrep_search", "parameters": { "query": "happy birthday", "limit": 1, "download": true, "output_path": "/workspace/downloads/birthday.gif" } } ``` ### Extract static frame from GIF ```json { "tool": "gifgrep_extract_frame", "parameters": { "input_path": "/workspace/downloads/birthday.gif", "time": "1.5s", "output_path": "/workspace/output/frame.png" } } ``` ### Generate frame sheet ```json { "tool": "gifgrep_generate_sheet", "parameters": { "input_path": "/workspace/downloads/birthday.gif", "frames": 9, "columns": 3, "output_path": "/workspace/output/sheet.png" } } ``` ## Supported Providers - `auto` (default): Uses Giphy if API key is set, otherwise Tenor - `tenor`: Tenor GIF provider - `giphy`: Giphy GIF provider (requires API key for higher rate limits)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaoming23333/bls-skill (version 1.0.2)

--- name: bce-bls description: > 查询和操作百度智能云日志服务(BLS, Baidu Log Service)。当用户需要以下操作时使用此 skill： (1) 查询日志组(Project)列表 (2) 查询日志集(LogStore)列表或详情 (3) 查询日志集的索引(Index)配置情况 (4) 根据用户描述的查询意图，生成 BLS 检索/SQL 查询语句 (5) 调用 BLS API 检索分析日志并返回结果 触发关键词：BLS、日志服务、日志查询、LogStore、日志集、检索日志 --- # BLS 日志服务查询 ## 前置条件 - **Region**：bj/gz/su/bd/fwh/hkg/nj/yq/cd，必填，用户未提供时主动询问 - **AK/SK**：脚本自动按以下顺序解析，无需手动处理 1. 命令行参数 `--ak` / `--sk` 2. 环境变量 `BCE_BLS_ACCESS_KEY` / `BCE_BLS_SECRET_KEY` 3. 配置文件 `~/.bce_bls/credentials`（INI，`[default]` section） ## 能力清单 所有命令统一前缀：`python3 <skill_path>/scripts/bls_query.py --region <region>` | 子命令 | 作用 | 关键参数 | | --- | --- | --- | | `list-projects` | 列出 Project | `--name` 过滤、`--page-no/--page-size` | | `list-logstores` | 列出/搜索日志集 | `--project`（可选）、`--logstore-name`（模糊匹配，可跨项目）、分页 | | `describe-index` | 查看日志集索引配置 | `--logstore`（必填）、`--project`（可选） | | `query` | 执行检索/SQL 分析 | `--logstore`、`--query`、`--start/--end`（UTC，可省）、`--marker` 翻页 | ## 关键决策规则 **起点选择**（避免无谓调用）： - 用户**同时给了 project 和日志集名** → 直接进入 `describe-index` / `query`，**跳过** list-logstores。 - 用户只给了**日志集名** → 直接 `list-logstores --logstore-name <name>`，从结果中精确匹配 `logStoreName` 拿到 `project`。**不要**先 list-projects。 - 用户只给了 **project 名**但没给日志集 → 直接 `list-logstores --project <p>`。 - 用户**只想浏览** project 列表 → 才用 list-projects。 **查询语句生成**（必须先 `describe-index` 确认索引状态）： - 有全文索引 → 关键词搜可用 `match keyword` - 有字段索引 → 字段过滤可用 `match field:value`，统计可用 SQL - **无索引** + `@raw` 是 JSON → **优先建议用户去 BLS 控制台为目标字段开启索引**；用户坚持立即查询才用 SQL + `JSON_EXTRACT_SCALAR(\`@raw\`, '$.field')` 兜底（扫描成本高、性能差） - **无索引** + `@raw` 非 JSON → 无法有效查询，提示用户去 BLS 控制台开启索引 - 过滤后再统计 → 混合 `match ... | select ...` **查询格式**： - 关键词搜索 → `match` - 统计聚合 → SQL（**不要写 FROM 子句**） - 先过滤后统计 → `match ... | select ...` **时间处理**： - API 时间格式必须是 UTC ISO8601（如 `2026-01-10T13:00:00Z`） - 北京时间需要 **-8h** 转换 - `--start` / `--end` 省略时默认最近 1 小时 **结果展示**： - SQL 结果 → 表格展示 `columns + rows` - match 结果 → 展示 `@raw` 原文 - `datasetScanInfo.isTruncated == true` → 提示用户可翻页（`--marker <nextMarker>`），仅 match 支持翻页，SQL 不支持 ## 常见查询模板 ```sql -- 关键词搜索 match error match status=500 and msg:"some keyword" -- Top N 统计 select uid, count(*) as cnt group by uid order by cnt desc limit 10 -- 时间趋势 select histogram(cast(`@timestamp` as timestamp), interval 1 hour) as hour, count(*) as cnt group by hour order by hour -- 慢请求 select path, avg(latency) as avg_lat, max(latency) as max_lat, count(*) as cnt group by path order by avg_lat desc limit 10 -- 提取非索引 JSON 字段（兜底方案：无索引时通过 @raw 字段解析；性能差且扫描成本高， -- 应优先建议用户去 BLS 控制台为该字段开启索引后再用 match/SQL 查询） select JSON_EXTRACT_SCALAR(`@raw`, '$.field') as f, count(*) as cnt group by f order by cnt desc limit 10 -- 先过滤后统计 match level:error | select caller, count(*) as cnt group by caller order by cnt desc limit 10 ``` ## 参考文档 - API 参数和响应格式：[references/api_reference.md](references/api_reference.md) - SQL 函数：[references/sql_syntax.md](references/sql_syntax.md) - MATCH 语法：[references/match_syntax.md](references/match_syntax.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nietzsche247/context-sentinel (version 1.0.0)

--- name: context-sentinel description: "Monitors session context and automatically manages model switching based on a cascading protocol. Use as part of a heartbeat or cron job to maintain session health and optimize token usage." version: 1.0.0 --- # Context Sentinel This skill provides a script to automate the "Cascading Model Protocol," ensuring the agent gracefully degrades from high-cost models to high-context models as the session grows. ## Protocol Summary 1. **Opus 4.6:** Use until 80% context, then switch to Opus 4.5. 2. **Opus 4.5:** Use until 80% context, then switch to Gemini 2.5 Pro. 3. **Gemini Pro:** Use until 80% context, then trigger a handoff. This skill operationalizes the logic defined in `MEMORY.md`. ## How to Use This skill is designed to be run periodically, either via a `cron` job or as part of the main agent's `HEARTBEAT.md` checklist. ### Workflow 1. **Execute the script:** Run the `check_context.ps1` script to get the current session status and determine the required action. ```powershell powershell -File scripts/check_context.ps1 ``` 2. **Evaluate the output:** The script will return one of three possible string commands: * `SWITCH_TO:<model_id>` * `HANDOFF_NOW` * `STATUS_OK` 3. **Take Action:** Based on the output, execute the appropriate agent command. * If `SWITCH_TO:<model_id>`, run `session_status` with the new model ID: ``` session_status model=<model_id> ``` * If `HANDOFF_NOW`, trigger the handoff process by writing to the handoff file. This is typically done by running a specific, pre-defined prompt or script. * If `STATUS_OK`, no action is needed. ### Example Usage in `HEARTBEAT.md` You can replace the manual checks in your `HEARTBEAT.md` with a call to this skill's script. **Old `HEARTBEAT.md`:** ```markdown ## Cascading Model Protocol (Check Every Heartbeat) 1. **Check Status:** Get current model and context %. 2. **Opus 4.6:** If model is `Opus 4.6` and context > 80% -> Switch to `Opus 4.5`. ... ``` **New `HEARTBEAT.md` using this skill:** ```markdown ## Context Sentinel (Check Every Heartbeat) 1. Run `powershell -File skills/context-sentinel/scripts/check_context.ps1`. 2. Evaluate the output and take action (`SWITCH_TO`, `HANDOFF_NOW`, or `STATUS_OK`). ``` This makes the logic reusable and keeps the `HEARTBEAT.md` file clean and focused on execution.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: leooooooow/refund-policy-clarifier (version 1.0.0)

# Refund Policy Clarifier Turn vague or confusing ecommerce refund rules into a clear, customer-facing policy explanation without accidentally expanding what the store is promising. Use this skill when a seller, operator, or support lead needs to rewrite refund language so customers can understand it quickly while staying aligned with the store's actual rules. It is especially useful for TikTok Shop, Shopify, DTC, and creator-driven stores where refund friction often comes from ambiguity, not just policy strictness. ## Use when - A refund policy feels confusing, contradictory, or hard to explain - Support teams need a clearer customer-facing version of existing rules - A seller wants to reduce refund confusion before checkout or post-purchase - You need plain-English clarifications without inventing new promises - You want to compare a stricter vs softer explanation style ## Do not use when - The user wants legal advice on consumer law compliance - The store has no actual refund policy to clarify from - The task is to decide the refund policy itself rather than explain it ## Inputs to ask for - Current refund / return policy text - Store type and channel - Product category and common refund scenarios - Refund windows, exclusions, and proof requirements - Return shipping rules - Exchange / replacement rules - Any problem areas causing confusion or complaints ## Workflow 1. Read the original policy as source truth 2. Separate hard rules from unclear wording 3. Rewrite into customer-facing language without changing commercial meaning 4. Flag hidden ambiguity, contradictions, or risk phrases 5. Provide a clearer final version plus a short internal note on remaining gaps ## Output requirements Return: 1. Policy summary in plain English 2. Rewritten customer-facing version 3. Ambiguity / risk flags 4. Suggested FAQ clarifications 5. Optional softer / stricter tone variation if useful ## Quality bar - Clear without overpromising - Faithful to source policy - Ecommerce-native examples - No accidental expansion of refund rights - Useful for operators and support teams ## Example prompts - Rewrite our refund policy so customers understand it without us promising more than we allow - Clarify this TikTok Shop return policy into plain English - Turn this messy refund policy into a help center friendly version


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