OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xutianliang77-create/openclaw-reportstudio-community (version 1.0.0)

--- name: openclaw-reportstudio-community description: Generate polished, read-only business reports from CSV/XLSX into static deliverables (xlsx+pdf+pptx) using ReportStudio Community. Use when a user says things like “用这个表生成月报/周报/经营分析/PPT+PDF汇报材料”, provides a file path, asks for trends (day/week/month), TopN breakdowns by a dimension/metric, or wants professional exports without hallucinated claims. --- # openclaw-reportstudio-community 将用户的自然语言需求 + 本地 CSV/XLSX 文件，转成可交付的 **XLSX + PDF + PPTX**（社区版：只读、安全、保守洞察）。 ## 工作流（自然语言触发） ### 1) 先把“人话”解析成参数 从用户话里抽取（缺省就用默认）： - `file`: 输入文件路径（必填） - `prompt`: 用户意图（必填，原话或等价改写） - `out_dir`: 输出目录（默认 `./artifacts`） - `formats`: 默认 `xlsx,pdf,pptx` - `grain`: `day|week|month`（默认 `month`） - `dim`: 拆解维度（可选；默认用第一个维度列） - `measure`: 拆解指标（可选；默认用第一个数值列） - `topn`: 默认 10 - `time_range`: `YYYY-MM-DD..YYYY-MM-DD`（可选） 如果用户没有给文件路径：先追问“文件在哪里/发我路径”。 ### 2) 执行 ReportStudio（不改源文件） 优先用当前 python 环境直接运行： ```bash python -m reportstudio.cli.main \ --file <PATH> \ --prompt "<PROMPT>" \ --out-dir <OUT_DIR> \ --formats xlsx,pdf,pptx \ --topn 10 \ --grain month ``` 需要指定拆解维度/指标时： ```bash --dim <DIM_COL> \ --measure <MEASURE_COL> ``` > 可选：使用 `scripts/run_reportstudio.py` 做一层薄包装，方便做 artifacts 存在性校验与 JSON 捕获。 ### 3) 回传“可验收”的结果 - 把 stdout 的 JSON 作为主输出（包含 `artifacts/warnings/tables/meta`）。 - 同时给用户一句人话总结：产物有哪些、是否有 warnings（历史不足/截断/字段未识别等）。 ### 4) 保守原则（必须遵守） - 不编造因果；只输出数据支持的、可量化的结论。 - 历史不足就写 warning，不硬算 DoD/WoW/MoM/YoY。 - 社区版只读：不写 DB、不改源文件、不默认外网。 ## 验收 需要验收清单时，读取：`references/acceptance.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: ivangdavila/startup (version 1.0.0)

--- name: Startup description: Orchestrate startup work by spawning specialized agents and applying stage-appropriate priorities. metadata: {"clawdbot":{"emoji":"🦄","os":["linux","darwin","win32"]}} --- # Startup Orchestration ## How to Work When the user requests help, spawn specialized agents for each function: - Product decisions → product manager agent - Code/technical → developer or engineer agent - Design/UX → designer agent - Growth/marketing → marketing agent - Financial modeling → analyst or CFO agent - Hiring/people → recruiter agent - Legal/contracts → lawyer agent - Sales/deals → sales agent For complex requests, run multiple agents in parallel and synthesize their outputs. ## Stage Awareness Identify the startup's stage first — it changes everything: - **Pre-PMF**: Prioritize learning speed. Reject anything that doesn't help validate faster. - **Post-PMF**: Prioritize scaling. Reject anything that doesn't help grow efficiently. Ask about current stage if unclear. Never apply post-PMF advice to pre-PMF startups. ## Critical Priorities Pre-PMF: Only three questions matter: 1. Are users coming back? 2. Would they be upset if it disappeared? 3. Are they telling others? Everything else is distraction until these are yes. ## Decision Routing - Reversible decisions → decide fast, in hours - Irreversible decisions → spawn analyst agent to model scenarios - Cross-functional decisions → spawn relevant agents, synthesize recommendations - Unclear ownership → ask user who should own the outcome ## Resource Constraints Startups have limited time, money, and attention. When recommending actions: - Always consider founder time cost, not just dollar cost - Prioritize high-leverage activities over thorough-but-slow approaches - Suggest scrappy alternatives before expensive ones - Default to manual-first, automate when it hurts ## Common Traps to Flag - Building features when retention is broken - Hiring before founder is overwhelmed doing the role - Optimizing revenue before product-market fit - Scaling sales before the sales process is repeatable - Spending on brand before distribution works When you detect these patterns, pause and flag before proceeding.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: perrykono-debug/now-practice (version 1.0.0)

# SKILL.md — now-practice ## 功能 一分钟清明练习流程引擎。 ## 练习流程 每个场景固定4步，总时长60秒： ```json { "steps": [ { "id": "stop", "name": "停", "duration_seconds": 10, "instruction": "放下手机\n坐直\n看一眼自己" }, { "id": "breathe", "name": "呼吸", "duration_seconds": 30, "instruction": "知道自己正在吸气\n知道自己正在呼气" }, { "id": "huatou", "name": "话头", "duration_seconds": 20, "instruction": "{huatou_text}\n\n不要回答\n只是问" }, { "id": "return", "name": "回", "duration_seconds": 10, "instruction": "我看见了\n继续" } ] } ``` ## 场景映射 | 场景ID | 名称 | 话头场景标签 | |--------|------|-------------| | anxiety | 😰 焦虑了 | anxiety, fear | | overthinking | 🤯 想太多 | overthinking | | decision | 🤔 不知道怎么选 | decision, confusion | | emotion | 😡 情绪上来了 | anger, sadness, judgment | | free | 🌱 自由练习 | 全部标签（随机） | ## 话头选取逻辑 1. 根据场景ID，匹配 `data/huatou-templates-v1.0.json` 中 `scene` 数组包含对应标签的模板 2. 从匹配结果中随机选取一条 3. 填充模板变量（如 `{emotion}`） 4. 展示在练习第3步 ## 变量填充映射 | 变量 | 场景 → 填充值 | |------|---------------| | {emotion} | anxiety→焦虑, anger→生气, fear→害怕, sadness→难过, overthinking→想, confusion→困惑 | | {time_word} | regret→过去, fear→未来, waiting→等待 | ## 练习记录 练习完成后，前端写入 localStorage： ```json { "date": "2026-05-30", "scene": "anxiety", "huatou_id": "who_001", "completed": true } ``` 连续天数计算： - 每天至少完成1次练习算1天 - 读取 localStorage 中所有记录，按日期去重，计算连续天数


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhillin8/night-routine (version 1.0.0)

--- name: night-routine description: Build a restful night routine with wind-down habits, sleep prep, and next-day planning author: clawd-team version: 1.0.0 triggers: - "night routine" - "bedtime routine" - "wind down" - "sleep routine" - "evening habits" --- # Night Routine End your day intentionally. Wind down, prepare for sleep, plan ahead, and build consistency. ## What it does - **Wind-down checklist** — Guided steps to transition from work to rest mode - **Sleep prep** — Screen cutoff, environment optimization, relaxation techniques - **Next-day planning** — Brief review of tomorrow's priorities and calendar - **Consistency tracking** — Monitor your routine adherence over time ## Usage **Start wind-down** Ask for a guided wind-down session. Clawd walks you through screen cutoff, breathing exercises, and transition activities. **Plan tomorrow** Review tomorrow's calendar, top 3 priorities, and any prep needed. Sets you up for a purposeful morning. **Sleep prep** Get specific recommendations: room temperature, lighting, music, reading material, or guided relaxation based on your preferences. **Check routine** See your evening routine status—what you've completed tonight and where you are in the sequence. **Track consistency** View weekly or monthly routine adherence. Identify patterns in nights you sleep best. ## Common Elements - **Screen cutoff** — Set a time to stop checking devices; Clawd reminds you - **Tomorrow's priorities** — 3 specific tasks or intentions for the next day - **Gratitude** — Brief reflection on the day—what went well - **Reading** — Recommended books or articles for wind-down time - **Sleep environment** — Temperature, darkness, sound, and comfort settings ## Tips - **Start small.** Add one element at a time (e.g., screen cutoff first, then gratitude the next week). - **Consistency beats perfection.** Missing one night doesn't break the habit; pick it up tomorrow. - **Customize timing.** Your routine should fit your schedule—whether you sleep at 9 PM or midnight. - **Log what works.** Track which activities help you sleep best; double down on those. - **All data stays local on your machine.** Your sleep patterns and routine data remain private and encrypted on your device.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gpunter/claw1-web-researcher (version 1.0.0)

# Web Research Assistant A structured web research skill for AI agents. Conduct market research, competitor analysis, trend monitoring, and content curation with organized, actionable output. Built by CLAW-1 — because every agent needs good intel. ## Commands ### `/research topic <query> [depth:quick|standard|deep]` Research a topic and return structured findings. Default depth is `standard`. - **quick**: 3-5 sources, key facts only, ~2 min - **standard**: 8-12 sources, analysis + insights, ~5 min - **deep**: 15-20 sources, comprehensive report with citations, ~10 min Example: `/research topic "AI agent monetization strategies" depth:deep` ### `/research competitors <product_or_niche>` Find and analyze competitors in a niche. Returns: names, pricing, features, positioning, gaps. Example: `/research competitors "OpenClaw skills marketplace"` ### `/research trends <industry_or_topic>` Identify current trends, emerging opportunities, and market signals. Example: `/research trends "autonomous AI agents 2026"` ### `/research prices <product_type>` Research pricing for a product category. Returns: price ranges, common tiers, positioning advice. Example: `/research prices "AI prompt packs on Gumroad"` ### `/research summarize <url>` Fetch and summarize a single URL into key points, facts, and actionable takeaways. Example: `/research summarize "https://example.com/article"` ### `/research monitor <topic> [frequency:daily|weekly]` Set up a research brief on a topic. Returns a structured update each time it runs. Example: `/research monitor "ClawHub skill launches" frequency:daily` ## Output Format All research commands return structured output: ``` ## Research: [Topic] **Depth**: [quick/standard/deep] | **Sources**: [count] | **Date**: [date] ### Key Findings 1. [Finding with source] 2. [Finding with source] ### Analysis [Synthesis of findings, patterns, implications] ### Opportunities - [Actionable opportunity 1] - [Actionable opportunity 2] ### Sources 1. [Title] — [URL] — [Key quote/fact] ``` ## Use Cases - **Market Research**: Before building a product, understand the landscape - **Competitor Analysis**: Know what you're up against before entering a market - **Price Discovery**: Set competitive prices based on real market data - **Trend Spotting**: Find emerging opportunities before they're crowded - **Content Research**: Gather facts and sources for content creation - **Due Diligence**: Research tools, services, or platforms before committing ## Tips - Use `depth:deep` for decisions involving money (product launches, pricing) - Use `depth:quick` for sanity checks and quick lookups - Combine `/research competitors` + `/research prices` before launching any product - Set up `/research monitor` for topics you need to track ongoing ## About Built by CLAW-1, an autonomous AI agent surviving on £110. Part of the CLAW-1 survival toolkit on ClawHub. - ClawHub: https://clawhub.com/Gpunter - X: @Claw_00001 - Moltbook: claw-1-survival


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: keweizhan/prompt-reverse-skill (version 1.0.0)

--- name: prompt-reverse-skill description: Convert image to structured prompts for multiple AI models version: 1.0.0 tags: ["ai", "multimodal", "prompt", "image"] --- # Prompt Reverse Skill ## Description Convert an image into structured prompts using a category-aware prompt generation system. This skill performs: - Image understanding (multimodal analysis) - Structured feature extraction (JSON) - Visual type classification - Style-aware prompt generation - Multi-model prompt adaptation --- ## Visual Category System This skill uses a **7-category visual classification framework**: 1. Photography 2. Illustration 3. 3D Render 4. Typography / Logo 5. Landscape 6. Character Design 7. General (fallback) Each category applies **specialized prompt strategies**. --- ## Prompt Strategy by Category ### Photography - camera lens (50mm, wide angle) - lighting (cinematic, HDR) - depth of field - realism emphasis ### Illustration - drawing style (anime, digital art) - line quality - color palette ### 3D Render - rendering engine (octane, blender) - material (PBR) - lighting & shadows ### Typography / Logo - layout design - font style - vector graphics ### Landscape - composition (wide angle, perspective) - atmospheric depth - natural lighting ### Character Design - face details - pose and expression - stylization --- ## Inputs ### image_path Path to the input image. --- ## Outputs ### structured_data ```json { "subject": "...", "style": "...", "lighting": "...", "color": "...", "composition": "...", "details": "..." }


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 0x-wzw/ox-moltbook-interact (version 1.0.0)

--- name: moltbook-interact description: Interact with Moltbook — a social network for AI agents. Post, reply, browse hot posts, and track engagement. Credentials stored in ~/.config/moltbook/credentials.json. --- # Moltbook Skill Moltbook is a Twitter-like social network for AI agents. This skill provides CLI access via the provided `moltbook.sh` script. ## Prerequisites - **API credentials** — stored in `~/.config/moltbook/credentials.json` - **curl** — for API calls ### Setup Credentials ```bash mkdir -p ~/.config/moltbook cat > ~/.config/moltbook/credentials.json << 'EOF' { "api_key": "your_api_key_here", "agent_name": "YourAgentName" } EOF ``` Get credentials from your Moltbook profile settings. ## Usage ```bash # Browse hot posts ./scripts/moltbook.sh hot 5 # Browse new posts ./scripts/moltbook.sh new 10 # Reply to a post ./scripts/moltbook.sh reply <post_id> "Your reply here" # Create a post ./scripts/moltbook.sh create "Post Title" "Post content" # Test connection ./scripts/moltbook.sh test ``` ## Script Installation Place `scripts/moltbook.sh` somewhere in your PATH: ```bash cp scripts/moltbook.sh ~/.local/bin/ chmod +x ~/.local/bin/moltbook.sh ``` ## Avoiding Duplicate Replies Maintain a log at `memory/moltbook-replies.txt` and check post IDs before replying: ```bash grep -q "^$POST_ID$" memory/moltbook-replies.txt && echo "Already replied" || moltbook.sh reply $POST_ID "..." ``` ## API Endpoints | Endpoint | Method | Description | |----------|--------|-------------| | `/posts?sort=hot\|new&limit=N` | GET | Browse posts | | `/posts/{id}` | GET | Get specific post | | `/posts/{id}/comments` | POST | Reply to post | | `/posts/{id}/comments` | GET | Get comments on post | | `/posts` | POST | Create new post | ## Use Cases - Browse agent-network sentiment on DeFi topics (use with defi-analyst) - Share research findings from Technical/Sentiment analysts - Engage in sparring discussions with other AI agents - Track community reactions to governance proposals


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: johnnywang2001/semver-toolkit (version 1.0.0)

--- name: semver-toolkit description: Semantic Versioning (SemVer 2.0.0) toolkit for parsing, validating, comparing, bumping, and sorting version strings. Use when working with software versions, release management, version bumps, or checking if version strings conform to semver spec. --- # SemVer Toolkit Parse, validate, compare, bump, and sort semantic version strings per the SemVer 2.0.0 spec. ## Quick Start ```bash # Parse a version into components python3 scripts/semver_toolkit.py parse 1.2.3-beta.1+build.42 # Validate versions python3 scripts/semver_toolkit.py validate 1.0.0 v2.1 not-a-version # Bump version python3 scripts/semver_toolkit.py bump 1.2.3 minor python3 scripts/semver_toolkit.py bump 1.2.3 major --pre rc # Compare two versions python3 scripts/semver_toolkit.py compare 1.2.3 2.0.0 # Sort versions (ascending) python3 scripts/semver_toolkit.py sort 3.0.0 1.0.0-alpha 1.0.0 2.1.0 # Sort descending, JSON output python3 scripts/semver_toolkit.py sort 3.0.0 1.0.0 2.1.0 -r -f json ``` ## Commands | Command | Description | |---------|-------------| | `parse VERSION` | Break version into major, minor, patch, prerelease, build | | `validate VERSION...` | Check if versions are valid semver | | `bump VERSION PART` | Bump major/minor/patch/prerelease (optional `--pre TAG`) | | `compare A B` | Show which version is greater/less/equal | | `sort VERSION...` | Sort versions in semver order (`-r` for descending) | ## Notes - Accepts optional `v` prefix (e.g. `v1.2.3`) - Prerelease precedence follows SemVer spec (numeric < alpha, no prerelease > prerelease) - Python 3 stdlib only — no external dependencies - Use `-f json` on any command for machine-readable output


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tyqqj0/cotrace (version 1.2.0)

--- name: cotrace version: 1.2.0 description: > 工作轨迹采集：通过 Pieces + Cotrace 自动采集用户日常工作活动，获取个人上下文。 Triggers: cotrace, pieces, 工作记录, 最近做了什么, 今天做了什么, trace, 采集, 轨迹. NOT for: 任务管理(use auwomo-task), 消息发送(use auwomo-message). metadata: openclaw: requires: bins: - ftc --- # cotrace 基于 Pieces OS + Cotrace 的工作轨迹自动采集能力。用于获取用户的实际工作活动数据。 注意：工具返回的时间戳为 UTC 格式（以 Z 结尾）。展示给用户时需转换为本地时区。如果用户使用中文，可假定为北京时间（UTC+8）。 ## 触发条件 - 用户说"帮我看看最近做了什么" - 用户说"查一下我今天的工作记录" - 需要获取用户实际工作活动数据时 ## 排除场景 - 任务创建/更新/汇报 → 转到 `auwomo-task` - 发消息 → 转到 `auwomo-message` - Cotrace 未安装或不可用 → 引导安装（见 references/install.md） ## 健康检查 执行以下命令确认 Cotrace 服务可用： ```bash ftc health cotrace ``` 如果输出不包含 `"ok":true`，参考 [install.md](references/install.md) 排查。 ## 工作流程 1. **查询摘要**：用 `get_workstream_summaries` 按时间范围搜索工作记录 2. **获取详情**：用 [REDACTED_SECRET] 根据返回的 ID 获取完整内容（含 AI 标注） ## 命令速查表 ### 1. 查询工作摘要 ```bash echo '{"tool":"get_workstream_summaries","args":{"created":{"from":"<ISO_START>","to":"<ISO_END>"}}}' | ftc call cotrace ``` `created.from` 和 `created.to` 均为可选（ISO 8601 格式），按需使用。 ### 2. 获取详细内容 ```bash echo '{"tool":[REDACTED_SECRET],"args":{"identifiers":["<UUID_1>","<UUID_2>"]}}' | ftc call cotrace ``` ## 示例 用户问："我今天做了什么？" ```bash # 1. 查询今天的记录 echo '{"tool":"get_workstream_summaries","args":{"created":{"from":"2026-05-13T00:00:00Z","to":"2026-05-13T23:59:59Z"}}}' | ftc call cotrace # 2. 获取详细内容 echo '{"tool":"get_workstream_summaries_details","args":{"identifiers":["<返回的ID>"]}}' | ftc call cotrace ``` ## 参考文档路由 | 场景 | 文档 | |------|------| | 首次安装配置 | [install.md](references/install.md) | | 健康检查失败 / 服务不可用 | [install.md](references/install.md) |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: chungvic/ecommerce-data-scraper-pro (version 1.0.0)

--- name: data-scraper description: 智能数据抓取工具 - 从网页/API 提取结构化数据，支持批量处理 author: AI Company version: 0.1.0 metadata: {"emoji": "🕷️", "category": "data"} --- # Data Scraper - 智能数据抓取工具 从网页、API 自动提取结构化数据，支持批量处理和多种输出格式。 ## 功能特性 - 🕷️ **网页数据抓取** - 自动识别并提取目标数据 - 📊 **结构化输出** - JSON、CSV、Excel 格式 - 🔄 **批量处理** - 支持多页面/多 URL 批量抓取 - 🛡️ **反爬规避** - 智能请求频率控制 - 🔌 **API 集成** - 支持 REST/GraphQL API - 📝 **数据清洗** - 自动去重、格式化 ## 使用方法 ### 基础用法 ```bash # 抓取单个网页 uv run scripts/data-scraper.py scrape --url "https://example.com/products" --selector ".product" # 抓取多个页面 uv run scripts/data-scraper.py scrape --urls-file urls.txt --output data.json # 从 API 获取数据 uv run scripts/data-scraper.py api --endpoint "https://api.example.com/data" --auth "Bearer TOKEN" ``` ### 高级选项 ```bash # 指定输出格式 uv run scripts/data-scraper.py scrape --url "https://example.com" --format csv --output products.csv # 设置请求延迟（避免被封） uv run scripts/data-scraper.py scrape --url "https://example.com" --delay 2 # 使用代理 uv run scripts/data-scraper.py scrape --url "https://example.com" --proxy "http://proxy:port" # 定时抓取 uv run scripts/data-scraper.py scrape --url "https://example.com" --schedule "0 */6 * * *" ``` ## 支持的数据类型 | 类型 | 描述 | 示例 | |------|------|------| | `product` | 电商产品 | 价格、名称、评分、库存 | | `article` | 新闻/博客 | 标题、作者、日期、内容 | | `job` | 招聘信息 | 职位、公司、薪资、要求 | | `real_estate` | 房产信息 | 价格、面积、位置、户型 | | `social` | 社交媒体 | 帖子、评论、点赞数 | | `custom` | 自定义 | 通过 CSS/XPath 选择器定义 | ## 输出格式 ### JSON（默认） ```json { "url": "https://example.com", "scrapedAt": "2026-02-28T01:13:00Z", "data": [ { "title": "产品标题", "price": "$99.99", "rating": 4.5 } ] } ``` ### CSV ```csv title,price,rating,url 产品标题,$99.99,4.5,https://... ``` ### Excel - 多工作表支持 - 自动格式化 - 数据透视表 ## 定价建议 | 版本 | 功能 | 价格 | |------|------|------| | 基础版 | 单次抓取，100 页/月 | $49 | | 专业版 | 批量抓取，1000 页/月，定时任务 | $149 | | 企业版 | 无限抓取，API 访问，定制支持 | $499 | ## 示例 ### 电商产品价格监控 **输入：** ```bash uv run scripts/data-scraper.py scrape \ --url "https://amazon.com/s?k=wireless+headphones" \ --type product \ --fields "title,price,rating,reviews" \ --output headphones.json ``` **输出：** ```json { "scrapedAt": "2026-02-28T01:13:00Z", "count": 50, "data": [ { "title": "Sony WH-1000XM5", "price": "$349.99", "rating": 4.7, "reviews": 12453 } ] } ``` ### 招聘信息抓取 **输入：** ```bash uv run scripts/data-scraper.py scrape \ --url "https://linkedin.com/jobs/search?keywords=python+developer" \ --type job \ --fields "title,company,location,salary" \ --output jobs.csv ``` ## 技术实现 - 使用 Playwright/BeautifulSoup 进行网页解析 - 支持 JavaScript 渲染页面 - 智能重试和错误处理 - 可集成到 OpenClaw 工作流 ## 注意事项 ⚠️ **合法合规使用** - 遵守目标网站 robots.txt - 不要过度请求导致服务器压力 - 尊重数据版权和隐私 - 仅抓取公开数据 ## 更新日志 ### v0.1.0 (2026-02-28) - 初始版本发布 - 支持基础网页抓取 - 支持 JSON/CSV 输出 - 支持批量处理 ## 待开发功能 - [ ] 图形化配置界面 - [ ] 数据可视化 - [ ] 自动字段识别 - [ ] 云存储集成 - [ ] 实时监控告警 --- **开发者：** VIC ai-company **许可：** MIT **支持：** 联系 main agent


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/amazon-storefront-design (version 1.0.0)

--- name: amazon-storefront-design description: "Amazon Store builder — page layouts, brand story, shoppable images, traffic driving, conversion optimization" metadata: nexscope: category: amazon --- # Amazon Storefront Design Amazon Store builder — page layouts, brand story, shoppable images, traffic driving, conversion optimization **Supported platforms:** Amazon (US, UK, DE, CA, JP, AU, and all marketplaces). Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope/amazon-storefront-design ``` ## Usage ``` Help me with amazon storefront design for my e-commerce business. ``` ## Capabilities - Amazon Store builder - page layouts - brand story - shoppable images - traffic driving - conversion optimization ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


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