OpenClaw/clawhub-security-signals huggingface dataset:
Skill: artur-zhdan/humanize-ai (version 1.1.0)

--- name: humanize-ai description: Humanize AI content by detecting and auto-fixing AI generated content. Humanize AI text using Python scripts. Scans for AI vocabulary, puffery, chatbot artifacts, and auto-replaces filler phrases. Use when you want to analyze text in AI detector and bypass it in future, batch-process files, run automated cleanup, or get a report before manual humanizing. allowed-tools: - Read - Write - StrReplace - Shell - Glob --- # Humanize CLI Command-line tools for detecting and auto-fixing AI writing patterns. ## Scripts ### analyze.py — Detect AI Patterns Scans text and reports AI vocabulary, puffery, chatbot artifacts, and auto-replaceable phrases. ```bash # Analyze a file python scripts/analyze.py input.txt # Analyze from stdin echo "This serves as a testament to our commitment" | python scripts/analyze.py # JSON output for programmatic use python scripts/analyze.py input.txt --json ``` **Output example:** ``` ================================================== AI PATTERN ANALYSIS - 5 issues found ================================================== AI VOCABULARY: • testament: 1x • crucial: 2x AUTO-REPLACEABLE: • "serves as" → "is": 1x • "in order to" → "to": 1x ``` --- ### humanize.py — Auto-Replace Patterns Performs automatic replacements for common AI-isms. ```bash # Humanize and print to stdout python scripts/humanize.py input.txt # Write to output file python scripts/humanize.py input.txt -o output.txt # Include em dash replacement python scripts/humanize.py input.txt --fix-dashes # Quiet mode (no change log) python scripts/humanize.py input.txt -q ``` **What it fixes automatically:** - Filler phrases: "in order to" → "to", "due to the fact that" → "because" - Copula avoidance: "serves as" → "is", "boasts" → "has" - Sentence starters: removes "Additionally,", "Furthermore,", "Moreover," - Curly quotes → straight quotes - Chatbot artifacts: removes "I hope this helps", "Let me know if", etc. --- ## Workflow 1. **Analyze first** to see what needs fixing: ```bash python scripts/analyze.py document.txt ``` 2. **Auto-fix** safe replacements: ```bash python scripts/humanize.py document.txt -o document_clean.txt ``` 3. **Manual review** for AI vocabulary and puffery flagged by analyze (these require human judgment) 4. **Re-analyze** to confirm improvements: ```bash python scripts/analyze.py document_clean.txt ``` --- ## Customizing Patterns Edit `scripts/patterns.json` to add/remove: - `ai_words` — vocabulary that flags but doesn't auto-replace - `puffery` — promotional language to flag - `replacements` — phrase → replacement mappings (empty string = delete) - `chatbot_artifacts` — phrases to auto-remove - `hedging_phrases` — excessive hedging to flag --- ## Batch Processing Process multiple files: ```bash # Analyze all markdown files for f in *.md; do echo "=== $f ===" python scripts/analyze.py "$f" done # Humanize all txt files in place for f in *.txt; do python scripts/humanize.py "$f" -o "$f.tmp" && mv "$f.tmp" "$f" done ``` ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: zhiuannnn/crypto-address-sentinel (version 1.0.0)

--- name: crypto-address-sentinel description: Monitor wallet balances and on-chain activity. Get alerts when balances change or when specified conditions are met. Use for tracking portfolio, detecting unusual activity, or monitoring airdrop eligibility across multiple chains. version: 1.0.0 tags: - crypto - wallet - monitoring - alert - portfolio --- # Crypto Address Sentinel Monitor wallet addresses across multiple blockchains and get notified of important events. ## Setup ### Environment Variables - `WATCHED_ADDRESSES` - Comma-separated list of addresses to monitor - `ALERT_WEBHOOK` - Webhook URL for alerts (optional) - `CHECK_INTERVAL_MINUTES` - How often to check (default: 60) ## Usage ``` # Check all watched addresses addresses # Get balance for specific address balance <address> # Add address to watchlist add <address> # Remove from watchlist remove <address> ``` ## Supported Chains - Ethereum - Arbitrum - Optimism - Base - Polygon - BNB Chain - Solana ## Features - Balance monitoring - Activity detection - Custom alert conditions - Multi-chain support - Periodic reports


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lanyasheng/harness-design-patterns (version 1.1.0)

--- name: harness-design-patterns version: 1.0.0 description: Agent harness 架构设计模式知识库。当设计多阶段任务的上下文传递、选择多 agent 协调方式、或规划 hook 系统架构时使用。纯设计指南，无可执行代码。不用于安装 hook 脚本（用 agent-hooks）或运维监控（用 agent-ops）。 license: MIT triggers: - harness design - agent architecture - handoff document - context survival - multi agent coordination - coordinator vs fork - hook architecture - 上下文存活 - 多 agent 协调 - harness pattern --- # Harness Design Patterns Agent harness 架构设计决策参考。蒸馏自 Claude Code 内部架构、OMC、Anthropic/OpenAI harness engineering 博客。 ## When to Use - 设计多阶段任务的上下文传递方案 → Handoff 文档 / Compaction 提取 - 选择多 agent 的协调模式 → Coordinator / Fork / Swarm 选型 - 设计 hook 系统架构 → Hook Pair Bracket / Component-Scoped / Profiles - 评估任务复杂度以选择执行强度 → Adaptive Complexity ## When NOT to Use - 需要可执行的 hook 脚本 → 用 `agent-hooks` - 需要运维工具（限速恢复、session 监控） → 用 `agent-ops` - 需要 prompt 硬化 → 用 `prompt-hardening` --- ## 10 个设计模式 | # | 模式 | 解决什么 | 详情 | |---|------|---------|------| | 1 | **Handoff 文档** | 跨阶段/跨压缩的上下文传递 | [详情](references/02-handoff.md) | | 2 | **原子文件写入** | 并发状态文件安全 | [详情](references/06-atomic-write.md) | | 3 | **Compaction 记忆提取** | 压缩前被动抢救知识（PreCompact hook） | [详情](references/08-compaction-extract.md) | | 4 | **权限否决追踪** | 防止 agent 绕过拒绝 | [详情](references/09-denial-tracking.md) | | 5 | **三门控记忆合并** | 跨 session 记忆碎片化 | [详情](references/10-memory-consolidation.md) | | 6 | **Hook Pair Bracket** | 每轮 context/时间测量 | [详情](references/11-hook-bracket.md) | | 7 | **Component-Scoped Hooks** | 任务级别的 hook 控制 | [详情](references/12-scoped-hooks.md) | | 8 | **三种委托模式** | 多 agent 协调方式选型 | [详情](references/14-delegation-modes.md) | | 9 | **Adaptive Complexity** | 任务复杂度自适应 | [详情](references/16-adaptive-complexity.md) | | 10 | **Hook Runtime Profiles** | 环境级 hook 强度控制 | [详情](references/18-hook-profiles.md) | > 注：reference 文件名保留原始编号（02/06/08/...）以兼容 monorepo 中的交叉引用。 ## 常见场景选型 | 场景 | 推荐模式 | |------|---------| | 多阶段任务（plan → implement → verify） | Handoff 文档 + Compaction 提取 | | 选择 Coordinator / Fork / Swarm | 三种委托模式选型指南 | | Hook 太多互相干扰 | Component-Scoped Hooks + Hook Profiles | | 不同环境需要不同保障级别 | Hook Runtime Profiles (minimal/standard/strict) | | 简单任务 overhead 太高 | Adaptive Complexity (triage → 自动选执行模式) | ## 工作流程 ### Step 1: 识别问题 当遇到 agent 可靠性问题时，判断属于哪一类：上下文丢失、协调混乱、hook 冲突、或执行强度不匹配。 ### Step 2: 选型 根据「常见场景选型」表找到对应模式，MUST 先读 reference 详情再做决策，otherwise 可能误用模式。 ### Step 3: 应用 如果需要可执行脚本 → 切换到 `agent-hooks` skill。如果需要运维工具 → 切换到 `agent-ops` skill。如果不确定 → 先从最简单的模式开始试验。 ## Output 当被询问 harness 设计建议时，returns: - 推荐的模式名称及原因 - 该模式的 tradeoff 说明 - 具体的实施步骤指引（指向 agent-hooks 或 agent-ops 中的脚本） ## 延伸阅读 - [蒸馏方法论](references/distillation-methodology.md) — PCA 降维类比、Review-Execution 分离 - [质量测评体系集成](references/quality-pipeline-integration.md) — 与 improvement-* skill family 的接入点


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/return-rate-impact-calculator (version 1.0.1)

--- name: return-rate-impact-calculator description: Calculate how return rate affects ecommerce profit, CAC tolerance, and operational risk. Use when teams need to see how refunds and reverse logistics change real margin. --- # Return Rate Impact Calculator 退货率不是售后指标而已，它会直接吞掉利润、预算空间和增长判断。 ## 先交互，再计算 开始时先问： 1. 你们要看的 return 是： - 退款率 - 退货率 - 退款+退货综合 2. 你们平时 return impact 的口径里包含哪些损失？ - 收入回吐 - 逆向物流 - 包材 - 二次销售损耗 - 客服成本 3. 产品是否可以二次售卖？恢复率是多少？ 4. 要沿用现有逻辑，还是让我给推荐框架？ ## Python script guidance 当用户给出结构化输入后： - 生成 Python 脚本计算退货影响 - 展示 revenue loss 与 extra cost loss - 输出调整后利润与 CAC 容忍度 - 返回可复用脚本 ## 解决的问题 很多团队会盯 GMV、ROAS、AOV，但忽略了一个事实： - 退货率一高，营收并不等于可保留营收； - 售后、逆向物流、二次包装和损耗会继续吃利润； - 表面上投放还能跑，实际可承受的 CAC 已经变低。 这个 skill 的目标是： **把 return rate 对利润和经营阈值的影响算清楚，避免用“未扣退货”的假象做决策。** ## 何时使用 - 某个 SKU 退款率明显偏高； - 想知道高退货类目是否还能继续放量； - 评估政策、文案、尺码说明、预期管理是否要调整。 ## 输入要求 - 订单量 / 销售额 - 售价与毛利 - 当前退货率 / 退款率 - 逆向物流、处理费、二次销售损耗 - 退款政策与是否可二次售卖 - 可选：获客成本、仓储和额外客服成本 ## 工作流 1. 明确 return impact 的口径。 2. 计算未考虑退货时的基础利润。 3. 计算退货带来的收入回吐和成本拖累。 4. 估算调整后的净利润与可承受 CAC。 5. 标记风险区间和优先修复动作。 6. 返回可复用 Python 脚本。 ## 输出格式 1. 核心假设表 2. 退货影响拆解 3. 调整后利润 / 阈值 4. 风险提醒与建议动作 5. Python 脚本 ## 质量标准 - 明确区分营收损失和额外成本损失。 - 输出对经营有用的阈值，而不只是一个百分比。 - 清楚说明哪些是假设值。 - 优先指出最值得修的环节。 - 未确认口径前不假装精确。 ## 资源 参考 `references/output-template.md`。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/feature-flags (version 1.0.0)

--- name: feature-flags description: Deep feature flag workflow—taxonomy, targeting, lifecycle, safety and kill switches, cleanup, and governance. Use when shipping gradually, experimenting, or decoupling deploy from release. --- # Feature Flags Flags decouple **deploy** from **release**—and become **debt** if never removed. **Taxonomy**, **ownership**, and **retirement** matter as much as targeting. ## When to Offer This Workflow **Trigger conditions:** - Gradual rollouts, kill switches, or experiments behind flags - Flag sprawl and unknown defaults - Client vs server evaluation and hydration flicker **Initial offer:** Use **six stages**: (1) taxonomy, (2) targeting rules, (3) evaluation & consistency, (4) safety & ops, (5) lifecycle & cleanup, (6) governance). Confirm provider (LaunchDarkly, Unleash, ConfigCat, homegrown). --- ## Stage 1: Taxonomy **Goal:** Separate short-lived **release** flags, long-lived **config** flags, and **experiment** flags tied to analytics. **Exit condition:** Naming convention and expected TTL per type. --- ## Stage 2: Targeting Rules **Goal:** Percentage rollouts, segments (tenant, plan, region), deterministic bucketing (stable user key). --- ## Stage 3: Evaluation & Consistency **Goal:** Server-side authoritative for security and billing; client flags for UX only; avoid UI flicker on hydration (SSR/CSR agreement). --- ## Stage 4: Safety & Ops **Goal:** Kill-switch runbook; audit trail for changes; safe defaults when provider unavailable (often “off”). --- ## Stage 5: Lifecycle & Cleanup **Goal:** Tickets to remove flags after full rollout; periodic audits; metric for stale flags. --- ## Stage 6: Governance **Goal:** Approvals for broadening exposure; promotion across environments; break-glass access for incidents. --- ## Final Review Checklist - [ ] Flag types and naming documented - [ ] Targeting and bucketing deterministic - [ ] Server vs client boundaries clear - [ ] Kill switches and defaults documented - [ ] Cleanup process and ownership ## Tips for Effective Guidance - Never put security-only gates solely in client-side flags. - Pair with **ab-testing** when experiment analysis is primary. ## Handling Deviations - Align with **release-management** for communication cadence.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: steipete/codex-owner-move-1778256848 (version 0.0.1)

--- name: Codex Owner Move E2E summary: Temporary skill that documents a realistic owner migration validation workflow. --- # Codex Owner Move E2E Use this temporary skill to validate that a ClawHub skill can be published under a personal owner, moved to an organization owner with an explicit migration flag, inspected after the move, and removed during cleanup. ## When to use Run this only for maintainer validation of the skill owner migration flow. It is intentionally scoped to a throwaway slug and should not be installed by users. ## Procedure 1. Publish version 0.0.1 under the authenticated personal publisher. 2. Publish version 0.0.2 with the OpenClaw owner selected and the migration opt-in enabled. 3. Inspect the skill and verify that the latest version is 0.0.2. 4. Delete the temporary skill after validation completes. ## Expected behavior The publish operation should preserve the same skill record while changing the owner publisher. Existing version history, stats, aliases, and audit history should remain attached to the skill. If the migration flag is missing, the backend should reject the owner change instead of silently transferring ownership.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: run02/pugoing-smart (version 1.0.2)

--- name: pugoing-smart description: 提供 Pugoing 中控平台的常用 API 调用能力，用于查询区域、主机、设备，以及通过自然语言控制设备。 --- # Pugoing Smart 这个目录提供一个通用调用脚本，目标是让 Agent 用尽量少的上下文完成对 Pugoing 平台的常见调用。 环境变量示例： ```bash export PUGOING_BASE_URL="http://127.0.0.1:8080" export PUGOING_API_KEY="xq_agent_xxx" export PUGOING_TIMEOUT="30" ``` 说明： - `PUGOING_API_KEY` 必选，格式通常是 `xq_agent_xxx` - `PUGOING_BASE_URL` 可选，默认是 `http://127.0.0.1:8080`, 一般是局域网地址 - `PUGOING_TIMEOUT` 可选，单位秒，默认 `30` 脚本会自动处理读环境变量和鉴权. 调用方只需要专注于传入正确的JSON即可. ## 通用调用对象 推荐传一个 JSON 对象给 `client.py`： ```json { "path": "/api/xq_host/list", "method": "GET" } ``` 字段说明： - `path`: 相对路径，推荐写法 - `url`: 也支持完整 URL；如果同时给了 `url`，优先使用 `url` - `method`: `GET` 或 `POST` - `params`: 查询参数 - `data`: JSON body - `headers`: 额外请求头 - `timeout`: 单次请求超时秒数 ## 输入方式 `client.py` 支持两种输入方式： ### 1. 传 JSON 文件 ```bash python client.py examples/list_hosts.json ``` ### 2. 从 stdin 读 JSON ```bash echo '{"path":"/api/xq_host/list","method":"GET"}' | python client.py - ``` ## 常见能力 ### 1. 查区域和主机 调用： - `/api/host_area/list` 示例： ```bash python client.py examples/list_areas_and_hosts.json ``` ### 2. 查主机列表 调用： - `/api/xq_host/list` 示例： ```bash python client.py examples/list_hosts.json ``` ### 3. 查某台主机下的设备 调用： - `/api/device/sync?host_ip=...` 示例： ```bash python client.py examples/list_host_devices.json ``` ### 4. 用自然语言控制设备 调用： - `/api/device/control/voice` 推荐只传： - `host_ip` - `dvcm` 示例： ```bash python client.py examples/control_device_by_dvcm.json ``` 请求体示例： ```json { "host_ip": "192.168.1.88", "dvcm": "打开客厅灯" } ``` ### 5. 兜底走内置 AI 调用： - `/api/ai/chat` 建议： - 平台级查询或跨主机场景时，`host_ip` 传 `"global"` - 单主机设备控制或上下文理解时，`host_ip` 传实际主机 IP 示例： ```bash python client.py examples/ai_fallback.json ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: scnet-sugon/medical-invoice-ocr (version 1.1.3)

--- name: medical_invoice_ocr description: 支持识别医疗发票识别。 version: 1.1.3 author: SCNet license: MIT tags: - OCR - 证件识别 - 发票识别 - 文字提取 required_env_vars: - SCNET_API_KEY optional_env_vars: - SCNET_API_BASE primary_credential: SCNET_API_KEY dependencies: - python3 - requests input: - ocrType : 识别类型，可选值见下文 - filePath : 待识别图片的本地路径 output: 结构化的 JSON 数据，包含识别结果和置信度 --- # Sugon-Scnet 医疗发票识别 OCR 技能 本技能封装了 Sugon-Scnet 医疗发票识别 OCR 服务，通过单一接口即可调用 1 种识别能力，高效提取文字及票据信息。 ## 功能特性 - **医疗票据**：覆盖医疗发票识别，自动提取关键字段。 ## 前置配置 > **⚠️ 重要**：使用前需要申请 Scnet API Token ### 申请 API Token 1. 访问 [Scnet 官网](https://www.scnet.cn) 注册/登录 2. 在控制台申请 API 密钥（格式：[REDACTED_SECRET]） 3. 复制密钥备用 ### 配置 Token **手动配置（推荐）** 1. 在技能目录下创建 `config/.env` 文件，内容如下： ```ini # ===== Sugon-Scnet OCR API 配置 ===== # 申请地址：https://www.scnet.cn SCNET_API_KEY=your_scnet_api_key_here # API 基础地址（一般无需修改） SCNET_API_BASE=https://api.scnet.cn/api/llm/v1 ``` 2. 添加：`SCNET_API_KEY=你的密钥` 3. 设置文件权限为 600（仅所有者可读写） **⚠️ 安全警告**：切勿将 API Key 直接粘贴到聊天对话中，否则可能被记录或泄露。 ### Token 更新 Token 过期后调用会返回 401 或 403 错误。更新方法：重新申请 Token 并替换 config/.env 中的 SCNET_API_KEY。 ### 依赖安装 本技能需要 Python 3.6+ 和 requests 库。请运行以下命令： ```bash pip install requests ``` --- ### 使用方法 ### 参数说明 | 参数名 | 类型 | 必填 | 描述 | |--------|------|------|------| | ocrType | string | 是 | 识别类型枚举。必须为以下之一：<br>• MEDICAL_INVOICE（医疗发票） | | filePath | string | 是 | 待识别图片的本地绝对路径。支持 jpg、png、pdf 等常见格式。 | ### 命令行调用示例 ```bash python .claude/skills/medical_invoice_ocr/scripts/main.py MEDICAL_INVOICE /path/to/invoice.jpg ``` ### 在 AI 对话中使用 用户可以说： - “帮我识别这张医疗发票，图片在 /Users/name/Downloads/id.jpg” AI 会根据 description 中的关键词自动触发本技能。 ### AI 调用建议 为避免触发 API 速率限制（10 QPS），请串行调用本技能，即等待前一个识别完成后再发起下一个请求。 如果使用 OpenClaw 的 exec 工具，建议设置 timeout 或 yieldMs 参数，让命令同步执行，避免多个命令同时运行导致并发。 ### 配置选项 编辑 `config/.env` 文件： | 变量名 | 默认值 | 说明 | |--------|--------|------| | SCNET_API_KEY | 必需 | Scnet API 密钥 | | SCNET_API_BASE | https://api.scnet.cn/api/llm/v1 | API 基础地址（一般无需修改） | ### 输出 - 标准输出：识别结果的 JSON 数据，结构与 API 文档一致，位于 `data` 字段内。 - 识别结果位于 data[0].result[0].elements 中，具体字段取决于 ocrType。 - 错误信息：如果发生错误，会输出以 `错误:` 开头的友好提示。 ### 注意事项 - 本技能调用的 OCR API 有 10 QPS 的速率限制。 - 如果遇到 429 错误，请等待 2-3 秒后重试，不要连续发起请求。 - 建议在调用前确保图片已准备就绪，避免因网络问题导致重复调用。 ### 故障排除 | 问题 | 解决方案 | |------|----------| | 配置文件不存在 | 创建 config/.env 并填入 Token（参考前置配置） | | API Key 无效/过期 | 重新申请 Token 并更新 `.env` 文件 | | 文件不存在 | 检查提供的文件路径是否正确 | | 网络连接失败 | 检查网络连接或防火墙设置 | | 不支持的文件类型 | 确保文件扩展名为允许的类型（参考 API 文档） | | 401/403/Unauthorized | Token 无效或过期，重新申请并配置 | | 429 Too Many Requests | 请求过于频繁，技能会自动等待并重试（最多 3 次）。若持续失败，请降低调用频率或联系服务方提高限额。 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: nexscope/amazon-global-selling (version 1.0.0)

--- name: amazon-global-selling description: "Evaluate and plan Amazon marketplace expansion to international sites. Market sizing, regulatory requirements, logistics planning, and localization strategy for EU, UK, Japan, Australia, and other Amazon marketplaces." metadata: nexscope: emoji: "🌍" category: amazon --- # Amazon Global Selling 🌍 Evaluate and plan Amazon marketplace expansion to international sites. Market sizing, regulatory requirements, logistics planning, and localization strategy for EU, UK, Japan, Australia, and other Amazon marketplaces. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-global-selling -g ``` ## Usage ``` I sell kitchen products on Amazon US doing $50K/month. Should I expand to Amazon UK and Germany? What do I need to prepare? ``` ## Capabilities - Marketplace opportunity assessment (demand, competition, fees per country) - Regulatory and compliance requirements by market (CE marking, WEEE, etc.) - Logistics and fulfillment options (FBA export, local FBA, 3PL) - Listing localization strategy (translation, keyword research, cultural adaptation) - Tax and VAT registration requirements - Currency and pricing strategy - Phased expansion roadmap (which markets first and why) ## 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)
Skill: nexscope/sales-tracking-tool (version 1.0.0)

--- name: sales-tracking-tool description: "Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early." metadata: nexscope: emoji: "📉" category: ecommerce --- # Sales Tracking Tool 📉 Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill sales-tracking-tool -g ``` ## Usage ``` Help me set up sales tracking for my business. I sell on Amazon ($30K/mo) and Shopify ($10K/mo). What KPIs should I track and how often? ``` ## Capabilities - Cross-platform sales dashboard framework (Amazon, Shopify, Walmart, etc.) - KPI definition and target setting by business stage - Trend analysis methodology (daily, weekly, monthly, YoY) - Anomaly detection framework (unusual spikes or drops) - SKU-level performance tracking - Cohort analysis for customer behavior insights ## 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)