OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tmstudio667-commits/agentic-marketing-auditor (version 1.0.0)

--- name: agentic-marketing-auditor description: "Audit repositories for 2026 Marketing readiness (GEO & A2A). This skill analyzes your README and documentation to ensure your AI products are discoverable and indexable by Large Language Models (LLMs) and Generative Search Engines like Perplexity and ChatGPT." metadata: { "openclaw": { "emoji": "📈" }, "author": "System Architect Zero", "category": "Marketing" } --- # Agentic Marketing Auditor In 2026, user acquisition is driven by AI agents. If your product is not discoverable by LLMs, it doesn't exist. This skill provides a technical audit of your repository to ensure maximum visibility in the A2A (Agent-to-Agent) economy. ## Features - **GEO Analysis**: Checks for the presence and quality of `llms.txt`. - **A2A Readiness**: Scans for AI-friendly summaries and machine-readable documentation. - **Engagement Audit**: Analyzes README structures for human and agentic conversion. ## Usage Run the auditor against any local repository path: ```bash npx openclaw skill run agentic-marketing-auditor -- /path/to/your/repo ``` ## Architect's Note Part of the Sovereign Infrastructure initiative by System Architect Zero. Focus on technical authority.


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

--- name: healthy-eating description: Build healthy eating habits with meal logging, nutrition tracking, and food choices author: clawd-team version: 1.0.0 triggers: - "log meal" - "what should I eat" - "healthy eating" - "nutrition check" - "food choices" --- # Healthy Eating Build sustainable nutrition habits through simple meal logging and mindful food choices. ## What it does Log meals without obsession. Get real-time nutrition awareness. Receive personalized suggestions for healthier choices. Track patterns over time and build habits that stick—no calorie counting required. ## Usage **Log meals** - Record what you ate and when - Capture portion sizes and meal context (breakfast, snack, lunch, dinner) - Notes on how you felt after eating **Get suggestions** - Ask for healthy meal ideas based on ingredients you have - Get quick swaps for favorite foods - Receive context-aware recommendations (late night, busy day, social event) **Check nutrition** - View nutrient breakdown: proteins, fats, carbs, fiber, micronutrients - Understand food quality and satiety impact - See hydration status and daily totals **Meal planning** - Plan weekly meals with minimal friction - Balance macros and whole foods naturally - Batch prep suggestions for busy schedules **Track habits** - View eating patterns over days and weeks - Spot triggers and improvements - Celebrate consistency milestones ## Food Categories **Proteins** Lean meats, fish, eggs, legumes, nuts, seeds, Greek yogurt **Vegetables** Leafy greens, cruciferous, root veggies, colorful variety **Whole grains** Oats, brown rice, quinoa, whole wheat, barley **Healthy fats** Olive oil, avocados, fatty fish, nuts, seeds **Hydration** Water, herbal tea, electrolyte balance ## Tips - Log immediately after eating—memory fades but patterns stick - Focus on food quality and how your body feels, not numbers on a screen - Pair proteins with veggies at every meal to stabilize energy and hunger - Plan meals on Sundays so weekday choices default to healthy options - All data stays local on your machine


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 156554395/amoeba-management-analysis (version 1.0.1)

--- name: amoeba-management-analysis description: | 阿米巴经营分析技能。基于稻盛和夫阿米巴经营理念，提供单位时间核算、经营会计报表分析、阿米巴组织划分评估、业绩改善诊断等能力。 当用户需要做阿米巴经营分析、单位时间核算、经营会计、阿米巴组织划分、利润中心分析、内部交易定价、业绩评价时触发。 触发词：阿米巴、阿米巴经营、单位时间核算、经营会计、利润中心、内部交易、阿米巴划分、巴长、稻盛和夫、京瓷会计学 --- # 阿米巴经营分析技能 基于稻盛和夫阿米巴经营理念，结合经营分析五层级框架，提供完整的阿米巴经营分析能力。 ## 核心理念 阿米巴经营的三大支柱： 1. **阿米巴组织划分** — 将企业划分为独立核算的小组织（阿米巴），每个阿米巴独立计算收支 2. **单位时间核算** — 以"每小时创造的附加值"为核心指标，取代传统财务指标 3. **经营哲学** — "敬天爱人"，以人心为基础的经营 ## 工作流程 ### Step 1：明确分析目标 判断用户需求属于哪类： - **组织划分分析** → 参见 `references/organization-division.md` - **单位时间核算** → 参见 `references/hourly-value-accounting.md` - **经营会计报表** → 参见 `references/management-accounting.md` - **业绩改善诊断** → 参见 `references/performance-improvement.md` - **内部交易定价** → 参见 `references/internal-pricing.md` ### Step 2：数据收集 引导用户提供以下数据（按需）： | 数据类型 | 关键字段 | 来源 | |---------|---------|------| | 收入数据 | 分阿米巴收入、分产品/客户收入 | ERP/财务系统 | | 成本数据 | 直接材料、直接人工、制造费用 | 成本模块 | | 费用数据 | 按阿米巴分摊的间接费用 | 费用分摊表 | | 时间数据 | 各阿米巴总工时、出勤天数 | 考勤系统 | | 人员数据 | 各阿米巴人数、人均产值 | HR系统 | ### Step 3：单位时间核算 核心公式： ``` 附加值 = 总收入 - 外部采购成本（材料费+外包费+杂费） 单位时间附加值 = 附加值 ÷ 总劳动时间 单位时间人事费 = 人事费 ÷ 总劳动时间 单位时间利润 = 单位时间附加值 - 单位时间人事费 ``` 京瓷七原则（数据核算准则）： 1. 一一对应原则 — 钱和物一一对应 2. 双重确认原则 — 至少两人确认 3. 完美主义原则 — 不允许暧昧 4. 肌肉型经营原则 — 剔除一切冗余 5. 现金本位原则 — 以现金为基础 6. 提高核算原则 — 追求单位时间附加值最大化 7. 玻璃般透明原则 — 信息全员公开 ### Step 4：分析与诊断 按五个层级逐层深入： **L1 数据记录** — 各阿米巴经营数据概览 **L2 原因分析** — 附加值变动因素拆解（量差×价差×结构差） **L3 逻辑识别** — 阿米巴间联动效应（如：前工序影响后工序成本） **L4 预测预警** — 趋势预测与风险预警 **L5 决策推动** — 具体改善方案 ### Step 5：输出报告 使用 `references/report-template.md` 生成结构化报告。 ## 分析报告模板结构 ```markdown # [期间] 阿米巴经营分析报告 ## 一、核心结论 > 一句话总结本期各阿米巴经营状况及关键行动建议 ## 二、整体经营概况 - 企业总附加值：XX，单位时间附加值：XX元/小时 - 目标达成率：XX%，同比±XX% ## 三、各阿米巴单位时间核算 | 阿米巴 | 总收入 | 外购成本 | 附加值 | 总工时 | 单位时间附加值 | 目标达成率 | |--------|-------|---------|-------|-------|-------------|----------| | ... | ... | ... | ... | ... | ... | ... | ## 四、变动分析（L2-L3） ### 优异阿米巴分析 ### 滞后阿米巴分析 ### 阿米巴间联动分析 ## 五、趋势与预警（L4） ## 六、改善方案（L5） ``` ## 管理层经营理念与实战要点 基于实际经营复盘提炼，参见 `references/management-philosophy.md`： - 三大核心原则 + 现金流为王 - 从"销售导向"到"经营导向"的转变 - 阿米巴长常见经营问题诊断 - 战区健康度红旗信号 - 经营复盘机制（日/周/月/专项） - 客户经营方法论（分层/标杆/续签） - 市场下沉策略 ## 配合工具 - **searxng** — 搜索行业对标数据 - **feishu-sheet** — 用电子表格呈现数据 - **feishu-bitable** — 多维表格管理分析 - **feishu-create-doc** — 生成飞书文档报告 - **business-analysis-framework** — 通用经营分析框架补充


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teoslayer/pilot-dataset (version 1.0.0)

--- name: pilot-dataset description: > Exchange structured datasets with schema negotiation and metadata over Pilot Protocol. Use this skill when: 1. You need to share CSV, JSON, or Parquet datasets with schema information 2. You want to negotiate data formats and transformations before transfer 3. You need to maintain dataset lineage and provenance metadata Do NOT use this skill when: - You need to transfer unstructured files (use pilot-share instead) - You need real-time data streaming (use pilot-stream-data instead) - You need ML model files (use pilot-model-share instead) tags: - pilot-protocol - datasets - data-exchange - schema license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-dataset Structured dataset exchange with schema negotiation, format conversion, and provenance tracking. ## Commands ### Publish Dataset Availability ```bash pilotctl --json publish "$PEER" datasets --data '{"type":"dataset_available","name":"sales_data","format":"csv","rows":1000}' ``` ### Request Dataset ```bash pilotctl --json send-message "$DEST" --data '{"type":"dataset_request","name":"sales_data","preferred_format":"json"}' ``` ### Send Dataset with Metadata ```bash pilotctl --json send-message "$DEST" --data '{"type":"dataset_metadata","name":"sales_data","schema":{"columns":["date","amount"]}}' pilotctl --json send-file "$DEST" "$DATASET_FILE" ``` ### Validate Schema ```bash EXPECTED="date,amount,customer_id" ACTUAL=$(head -1 "$DATASET_FILE") [ "$ACTUAL" = "$EXPECTED" ] && echo "Schema validated" ``` ## Workflow Example ```bash #!/bin/bash # Dataset exchange PEER="agent-b" publish_dataset() { local file="$1" local name="${2:-$(basename $file .csv)}" local rows=$(wc -l < "$file") pilotctl --json publish "$PEER" datasets \ --data "{\"type\":\"dataset_available\",\"name\":\"$name\",\"format\":\"csv\",\"rows\":$rows}" } request_dataset() { local name="$1" local publisher="$2" pilotctl --json send-message "$publisher" \ --data "{\"type\":\"dataset_request\",\"name\":\"$name\",\"preferred_format\":\"csv\"}" sleep 2 pilotctl --json received } publish_dataset "data.csv" "my-dataset" ``` ## Dependencies Requires pilot-protocol, pilotctl, jq, and optionally python3 for format conversion.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-customer-segment (version 1.0.0)

--- name: ai-intelligent-customer-segmentation version: 1.0.0 description: 客户分层，RFM分析 + 精准营销。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能客户分层系统 ## 描述 客户分层，RFM分析 + 精准营销。 ## 功能 - RFM 分析（自动分层） - 标签管理（客户标签） - 画像分析（客户画像） - 营销策略（分层策略） - 效果跟踪（转化跟踪） ## 定价 - 基础版：¥199/月（1 万客户） - 专业版：¥999/月（10 万客户） - 企业版：¥2999/月（无限客户） ## 适用场景 - 客户管理 - 精准营销 - CRM 系统 - 用户运营 ## 技术栈 - Python + FastAPI - RFM 分析 - 标签系统 - 营销策略 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-customer-segmentation cd ai-intelligent-customer-segmentation pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team

path: skill.json
content: {
  "name": "ai-intelligent-customer-segmentation",
  "version": "1.0.0",
  "description": "AI 客户分层 - RFM分析、精准营销",
  "author": "yang1002378395-cmyk",
  "tags": ["ai", "customer", "segmentation", "marketing", "chinese"],
  "category": "marketing",
  "license": "MIT"
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kkkkhazix/khazix-writer (version 1.0.0)

--- name: khazix-writer description: | 数字生命卡兹克（Khazix）的公众号长文写作skill。包含完整的写作风格规则、四层自检体系、内容方法论和风格示例库。 当用户需要撰写公众号文章、写稿子、续写文章、根据素材产出长文时使用。适用于用户丢过来素材说"帮我写篇文章"的场景。 --- # 卡兹克公众号长文写作 > 这是卡兹克（Khazix）的个人写作风格skill。账号全称「数字生命卡兹克」，以「激发大家对AI的好奇」为使命。安装后可用卡兹克的风格写公众号长文。 卡兹克是一个在AI行业深耕三年的内容创作者和创业者。文章风格一句话概括： **"有见识的普通人在认真聊一件打动他的事。"** ## 核心价值观 **永远对世界保持好奇。** 面对新工具新技术，不是先问"我会被取代吗？"，而是"我能用它来玩点什么有意思的？" **讲人话，像个活人。** AI时代最稀缺的是"活人感"。分享亲身经历、真实感受、踩过的坑。大胆使用"我觉得"、"我认为"。 **真诚是唯一的捷径。** 可以不写，但绝不骗人。产品有缺点就直接说，不懂就大方承认。 **有所为有所不为。** 不追逐违背价值观的流量。 ## 写作流程 ### 第一步：理解素材与选题判断 用HKR质检选题： - **H (Happy)** 足够有趣、有悬念吗？ - **K (Knowledge)** 有信息量吗？看完能学到新东西吗？ - **R (Resonance)** 能戳中情绪吗？ S级选题三项兼备，及格选题至少占两项。 ### 第二步：明确AI的角色边界 AI是文风生成器，不是替代思考的工具。AI擅长：文风转写、结构优化、润色修辞、素材整理。AI不擅长：独到观点、个人经历、情绪判断。 ### 第三步：写作 **结构**：开头400字定生死，必须有悬念钩子。正文层层推进，每段结尾留钩子。结尾升维但不硬凑。 **节奏**：句子时长时短，段落之间跳跃自然。一句话自成一段制造重量感。好的节奏像波动。 **语言规则**： - 禁用冒号（用逗号或句号替代） - 禁用破折号 - 用「」替代"" - 人名不加「」 - 英文产品名首次出现后保持英文，不翻译 - 禁用感叹号（除非引用原话） **绝对禁区**（AI味标记）： - 套话："首先...其次...最后"、"综上所述"、"值得注意的是" - 空洞词："赋能"、"抓手"、"打造闭环" - 教科书开头："在当今AI快速发展的时代" - 高频踩雷词："说白了"、"意味着什么？"、"本质上" ### 第四步：四层自检 1. **事实层**：每个事实断言有出处吗？ 2. **逻辑层**：论证链完整吗？有跳跃吗？ 3. **风格层**：读起来像卡兹克吗？有活人感吗？ 4. **体验层**：读者能带走什么？值得转发吗？ ## 卡兹克修辞特征 - **知识是「聊着聊着顺手掏出来」的**：不做课堂式铺垫 - **长短句交替**：信息密集段用短句切割，情绪段用长句铺开 - **每段结尾都有钩子**：永远给下一段留口子 - **敢下判断**：先摆事实，再给判断，用"我觉得"承认主观性 - **层层剥开**：现象→表面解释→更深追问→核心洞察 - **文化升维**：连接到更大的文化/哲学/历史参照物 完整版 Skill 含风格示例库、详细修辞规范、排版规则，见 GitHub：https://github.com/KKKKhazix/khazix-skills


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yuzengbaao/opencli-yuzengbao (version 1.0.0)

--- name: opencli description: "OpenCLI — 通用 CLI Hub，将任何网站、Electron 应用或本地工具转化为命令行接口。66+ 内置命令，支持 Bilibili/Twitter/Reddit/Xiaohongshu/GitHub 等。复用 Chrome 登录态，零 LLM 成本，确定性输出。" metadata: openclaw: emoji: "🔧" requires: bins: [node] node: ">=20.0.0" setup: | npm install -g @jackwener/opencli@latest installed: version: "1.5.9" date: "2026-04-02" --- # OpenCLI Skill > Make any website, Electron App, or local tool your CLI. > Zero risk, Reuse Chrome login, AI-powered discovery, Universal CLI Hub. ## 安装与验证 ```bash # 安装 npm install -g @jackwener/opencli@latest # 验证 opencli --version opencli list # 查看所有可用命令 opencli doctor # 检查扩展 + daemon 连通性 ``` ## 前提条件 - **Node.js** >= 20.0.0 - **Chrome 浏览器**（需登录目标网站） - **Browser Bridge 扩展**（从 GitHub Releases 下载 opencli-extension.zip → chrome://extensions → 开发者模式 → 加载已解压的扩展） ## 常用命令速查 ### 无需浏览器的 Public API 命令 ```bash opencli hackernews top --limit 5 # HackerNews 热榜 opencli v2ex hot --limit 5 # V2EX 热帖 opencli arxiv search "quantum" # arXiv 论文搜索 opencli stackoverflow questions "rust" # StackOverflow opencli producthunt today # Product Hunt ``` ### 需要浏览器登录的命令 ```bash # Bilibili opencli bilibili hot --limit 5 # 热榜 opencli bilibili search "AI" # 搜索 opencli bilibili download BV1xxx # 下载视频 # Twitter/X opencli twitter trending # 趋势 opencli twitter search "DeFi" # 搜索 opencli twitter timeline --limit 10 # 时间线 # Reddit opencli reddit hot --limit 5 # 热帖 opencli reddit search "ethereum" # 搜索 # 小红书 opencli xiaohongshu search "美食" # 搜索 opencli xiaohongshu note abc123 # 笔记详情 # GitHub (via gh CLI) opencli gh pr list --limit 5 # GitHub PR 列表 opencli gh issue list # Issue 列表 ``` ### 输出格式 ```bash opencli bilibili hot -f json # JSON 格式（适合管道/LLM） opencli bilibili hot -f csv # CSV 格式（适合表格） opencli bilibili hot -f yaml # YAML 格式 opencli bilibili hot -f md # Markdown 格式 opencli bilibili hot -v # Verbose 调试 ``` ### 外部 CLI 注册（CLI Hub） ```bash opencli register mycli # 注册本地 CLI，AI Agent 可通过 opencli list 发现 opencli gh pr list # 自动检测并安装缺失工具 ``` ## AI Agent 开发新适配器 ### 快速模式（单命令 4 步） 1. `browser_navigate` → 打开目标 URL 2. `browser_network_requests` → 筛选 JSON API 3. `browser_evaluate` → 用 fetch 验证接口 4. 写 YAML/TS adapter → build → test ### 认证层级 | 层级 | 条件 | 方案 | |:---|:---|:---| | Tier 1 | `fetch(url)` 直接能拿 | Public API (YAML, browser: false) | | Tier 2 | 加 `credentials:'include'` | Cookie (YAML) | | Tier 3 | 需 Bearer/CSRF header | Header (TS) | | Tier 4 | 页面能请求但 fetch 不行 | Intercept (TS, installInterceptor) | ### 自动探索 ```bash opencli explore https://example.com --site mysite # 发现 API opencli synthesize mysite # 生成 YAML 适配器 opencli generate https://example.com --goal "hot" # 一键生成 opencli cascade https://api.example.com/data # 自动探测认证策略 ``` ## 退出码 | 码 | 含义 | |:---|:---| | 0 | 成功 | | 66 | 空结果 | | 69 | Browser Bridge 未连接 | | 77 | 需要登录 | ## 故障排除 - "Extension not connected" → 安装 Browser Bridge 扩展到 chrome://extensions - 空数据/Unauthorized → Chrome 中重新登录目标网站 - Daemon 问题 → `curl localhost:19825/status` 检查


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/quick-img (version 1.1.0)

--- name: quick-img description: "Generate images using curl and the SkillBoss API." allowed-tools: Bash metadata: {"clawdbot":{"requires":{"env":["SKILLBOSS_API_KEY"]},"primaryEnv":"SKILLBOSS_API_KEY"}} --- # Quick Image Generator Generate images from text prompts using curl. ## Generate ```bash curl -s -X POST https://api.heybossai.com/v1/run \ -H "Content-Type: application/json" \ -d "{\"api_key\":\"$SKILLBOSS_API_KEY\",\"model\":\"mm/img\",\"inputs\":{\"prompt\":\"A sunset\"}}" \ | node -e "let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const j=JSON.parse(d);console.log(j.image_url||JSON.stringify(j))})" ``` ## Check IP ```bash node {baseDir}/scripts/hello.mjs ``` Notes: - Get SKILLBOSS_API_KEY at https://www.skillboss.co


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: anmolnagpal/idle-resource-detector (version 1.0.0)

--- name: aws-idle-resource-detector description: Detect AWS idle and zombie resources consuming cost with zero meaningful utilization tools: claude, bash version: "1.0.0" pack: aws-cost tier: pro price: 29/mo --- # AWS Idle & Zombie Resource Detector You are an AWS resource hygiene expert. Scan for resources consuming cost with no business value. ## Detection Targets - Stopped EC2 instances still charging for attached EBS volumes - Unattached EBS volumes (no instance attachment) - Unused Elastic IP addresses (not associated with running instance) - Idle load balancers (0 active connections for 7+ days) - Empty or near-empty S3 buckets with no recent access - Idle RDS instances (< 1% CPU over 7 days) - Orphaned snapshots older than 90 days - Unused NAT Gateways (0 bytes processed) ## Output Format - **Waste Summary**: total estimated monthly waste in $ - **Resource Table**: resource ID, type, region, estimated monthly cost, last active - **Cleanup Priority**: ranked by cost impact (High/Medium/Low) - **Runbook**: step-by-step cleanup commands per resource type - **Safe Deletion Checklist**: flags for resources needing human confirmation ## Rules - Never suggest deleting resources without a confirmation flag - Flag resources with names containing "prod", "production", "critical" for manual review - Always include the AWS CLI command for each cleanup action - Add estimated annual savings at the end


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chinakingkong/mcp-server (version 1.0.0)

--- name: mcp-server description: Create and deploy Model Context Protocol (MCP) servers for AI agents. MCP enables AI models to interact with external tools and services. --- # MCP Server Creator Create Model Context Protocol servers that extend AI capabilities. ## What is MCP? MCP (Model Context Protocol) is an open protocol that allows AI models to: - Access external tools and services - Interact with databases, APIs, and file systems - Execute code and commands ## Features ### 1. Quick Server Templates - Weather API server - Database query server - File management server - Custom API wrapper server ### 2. Deployment - Deploy to various platforms - Connect to AI agents (Claude, OpenClaw, etc.) ### 3. Examples ``` # Create a weather MCP server mcp-server create weather --api open-meteo # Create a database MCP server mcp-server create database --type postgres # Deploy to cloud mcp-server deploy --platform vercel ``` ## Use Cases - Connect AI to external APIs - Create custom tool integrations - Build AI-powered automation - Enable AI to interact with databases ## Requirements - Node.js 18+ - npm or yarn - API keys for external services (if needed)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/adwords (version 2.0.0)

--- version: "2.0.0" name: adwords description: "营销文案助手。标题公式(100个)、正文写作、AIDA框架、痛点挖掘、客户证言、落地页文案。Copywriting with 100 headline formulas, body copy, AIDA framework, pain points, testimonials." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Copywriter ✍️ 营销文案全能助手，从标题到落地页一站搞定。 ## 使用方式 | 需求 | 命令 | 说明 | | Command | Description | |---------|-------------| | `headline` | 标题公式库(100个)+变体生成 | | `body` | 正文撰写(多种风格) | | `aida` | AIDA框架文案生成 | | `pains` | 用户痛点挖掘+文案转化 | | `testimonial` | 客户证言/评价文案 | | `landing` | 完整落地页文案 | ## 运行 ```bash bash scripts/copy.sh <command> [args...] ``` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `adwords help` for all commands ## Commands Run `adwords help` to see all available commands. ## When to Use - to automate adwords tasks in your workflow - for batch processing adwords operations ## Output Returns formatted output to stdout. Redirect to a file with `adwords run > output.txt`. ## Configuration Set `ADWORDS_DIR` environment variable to change the data directory. Default: `~/.local/share/adwords/`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: haohanyang92/a-stock-market-sentiment (version 1.0.0)

--- name: market-sentiment description: 用数据判断市场处于"亢奋/分歧/冰点/修复"哪个阶段。用于A股短线交易，辅助判断仓位和交易策略。 --- # 市场情绪量化 Skill ## 核心能力 用数据判断市场处于"亢奋/分歧/冰点/修复"哪个阶段，而不是靠感觉猜。 ## 情绪判断规则 ### 情绪冰点 - 涨停家数 < 20家 - 跌停家数 > 10家 - 连板高度 < 3板 - **操作建议**：轻仓或空仓 ### 情绪亢奋 - 涨停家数 > 80家 - 炸板率 < 15% - 连板高度 > 5板 - **操作建议**：重仓主线龙头 ### 分歧阶段 - 涨停家数波动大 - 炸板率 > 25% - **操作建议**：半仓参与辨识度高的标的 ## 数据获取 调用 akshare-stock 获取： - 涨停家数：akshare_stock_limit_up() - 跌停家数：akshare_stock_limit_down() - 炸板率：(封板失败数 / 涨停家数) × 100% - 连板高度：最高连板数 ## 实战流程 1. 每天开盘前花5分钟获取数据 2. 判断当前情绪阶段 3. 匹配对应仓位策略 4. 避免在冰点期盲目出手 ## 触发场景 - "今天市场情绪怎么样" - "现在是冰点还是亢奋" - "能不能加仓" - "今天适合重仓还是轻仓"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: plgonzalezrx8/workflow-engine (version 1.0.0)

--- name: workflow-orchestrator description: Structural parity skeleton for queue-driven orchestration in a workflow context. user-invocable: true version: "0.1" allowed-tools: Read, Write, Edit, Grep, Glob, Bash argument-hint: "[target] [--handoff] [--batch <id>] [--type <extract|create|reflect|reweave|verify>]" --- ## Contract This skill is intentionally skeleton-only. It provides structure parity with canonical queue orchestration without plugin hooks. Wave 2 execution parity includes: - Setup derivation phase ordering: `detection -> understanding -> derivation -> proposal -> generation -> validation` - Required persisted setup artifacts: `ops/derivation.md`, `ops/derivation-manifest.md`, `ops/config.yaml` - In-skill lifecycle emulation routines (not real hook execution): - session orient - write-time validation checklist - stop/session capture persistence ## Argument Parsing Parse arguments in this order: 1. positional `target` (optional) 2. `--handoff` (boolean) 3. `--batch <id>` (optional) 4. `--type <phase>` (optional) 5. unknown flags are non-fatal; report and ignore If no `target` is supplied, list candidate inbox files and request explicit selection. ## Runtime Loader Requirements Before doing queue work, load runtime context from: - `ops/derivation-manifest.md` (if present) - `ops/config.yaml` (if present) - queue file with fallback precedence: 1. `ops/queue/queue.yaml` 2. `ops/queue/queue.yml` 3. `ops/queue/queue.json` If no queue file exists, fail safely with actionable remediation. ## Safety Constraints Never: - execute arbitrary shell from user-provided strings - continue processing after parse/load failures - mutate tasks outside declared queue schema fields - call external plugins/hooks (explicitly out of scope) Always: - validate queue structure before state transitions - make state transitions explicit (`extract -> create -> reflect -> reweave -> verify -> done`) - produce deterministic handoff text when `--handoff` is set - preserve resumability by reading persisted queue state first ## Scope Boundary No plugin hooks are implemented in this skeleton. Lifecycle behavior is emulated in-skill to preserve deterministic execution semantics only. ## Installation To install the workflow-engine and enable its hooks: ```bash # Clone or navigate to the workflow-engine directory cd workflow-engine # Run the install script to set up hooks ./install-hooks.sh # Or manually enable hooks via openclaw openclaw hooks enable session-orient openclaw hooks enable write-validate openclaw hooks enable session-capture ```


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