[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GNXZPKYQMRX7S5207JC7VB":3,"skill-same:01M6GNXZPKYQMRX7S5207JC7VB":97},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":6,"ownerHtmlUrl":69,"ownerBlogUrl":70,"ownerDescription":71,"updatedAt":72,"install":73,"resources":86},"01M6GNXZPKYQMRX7S5207JC7VB","workflow-automation",null,"Automate complex workflows and repetitive tasks using AI agents and tool integration. Use when user wants to create automated pipelines, integrate multiple services, or build task-specific agents.","jiayaoqijia\u002Fcryptoskill","jiayaoqijia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1453879?v=4","01M6GMYTX7Q7S504VC6E756ENR","YQ","01M6GMYV1QKJ1CC75MTTGT9PR0",81,"MIT","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fai-crypto\u002Fxspoonai-official-workflow-automation\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"workflow","automation","Workflow Automation","when","python","with","task","build","integrate","use","automate","tool","Pydantic","langgraph","services","productivity","create","tasks","workflows","sdk","ai agents","2026-11-10 10:12:30",false,"---\nname: workflow-automation\ndescription: Automate complex workflows and repetitive tasks using AI agents and tool integration. Use when user wants to create automated pipelines, integrate multiple services, or build task-specific agents.\nversion: 1.1.0\nauthor: with-philia\nlicense: MIT\ntags: [AI, Automation, Workflow, Productivity, Agent, Integration, Python]\ndependencies: [spoonos-sdk, langgraph, pydantic]\nallowed-tools: Python(spoon_ai:*), Python(langgraph:*)\n---\n\n# AI Workflow Automation\n\nCreate intelligent, automated workflows that combine AI reasoning with external tool execution. This skill enables the construction of robust pipelines for data processing, content generation, and system integration, moving beyond simple scripts to adaptive, state-aware agents.\n\n## When to Use\n\n- **Complex Pipelines**: User wants to connect multiple APIs (e.g., \"fetch data from X, process with Y, send to Z\").\n- **Intelligent Automation**: User wants to \"automate a daily report\", \"build a news digest bot\", or \"triage customer emails\".\n- **Background Tasks**: User asks for a \"background worker\" or \"cron job\" with AI capabilities.\n- **Decision Support**: User wants to streamline a repetitive manual process involving decision making (e.g., \"approve if score > 80, else review\").\n\n## Decision Flow\n\n**Determine the complexity and requirements of the automation.**\n\n```\nUser Request for Automation\n        │\n  Analyze Complexity\n        │\n   ┌────┴─────────────────┐\n Simple                 Complex\n (Linear Sequence)      (Branching\u002FLooping\u002FState)\n    │                      │\n  Script                 Agent \u002F Graph\n  Based                  Based\n    │                      │\n    │                  Need Human Approval?\n    │                  ┌───┴───┐\n    │                 Yes     No\n    │                  │       │\n    │             Human-in-  Fully\n    │             the-loop   Autonomous\n    │                  │       │\n    └──────┬───────────┴───────┘\n           │\n     Select Tools & APIs\n           │\n     Define State Schema\n           │\n     Implement Logic\n           │\n     Test & Deploy\n```\n\n## Core Concepts\n\n1.  **Triggers**: Events that initiate the workflow (e.g., Schedule\u002FCron, Webhook, Manual Input, File Upload).\n2.  **Steps (Nodes)**: Discrete units of work.\n    - _Action_: Executing a tool or API call (deterministic).\n    - _Reasoning_: LLM processing (summarization, decision making, extraction).\n3.  **State**: The shared context passed between steps (e.g., `messages`, `current_data`, `errors`).\n4.  **Routing (Edges)**: Logic to determine the next step based on current state (e.g., \"if error, go to retry; else go to finish\").\n5.  **Human-in-the-loop**: Pausing execution for human review before proceeding with critical actions.\n\n## Implementation Guide\n\n### Step 1: Define the Workflow\n\nClearly outline the inputs, processing steps, decision points, and desired outputs.\n\n_Example: Daily Tech News Digest_\n\n- **Input**: RSS feeds or NewsAPI query.\n- **Process**:\n  1. Fetch latest articles.\n  2. Filter for relevance (e.g., \"AI\", \"LLM\").\n  3. Summarize each article into 3 bullet points.\n  4. Generate an HTML email template.\n- **Output**: Send email via SMTP or SendGrid.\n\n### Step 2: Select Tools & Manage Secrets\n\nIdentify necessary integrations and ensure API keys are managed securely (e.g., `.env` file).\n\n- **Data Source**: `requests` (Python), `NewsAPI`, `Tavily`.\n- **Processing**: `OpenAI` or `Anthropic` (via SpoonOS).\n- **Output**: `smtplib`, `SendGrid`, `Slack Webhook`.\n\n**Security Note**: Never hardcode API keys. Use `os.getenv(\"API_KEY\")`.\n\n### Step 3: Build the Agent (SpoonReactSkill)\n\nFor simpler, tool-using agents, use `SpoonReactSkill`.\n\n```python\nimport os\nfrom spoon_ai.agents import SpoonReactSkill\nfrom spoon_ai.tools import Tool\nfrom spoon_ai.llm import ChatBot\n\nclass NewsDigestAgent(SpoonReactSkill):\n    def __init__(self):\n        super().__init__(\n            name=\"news_digest_bot\",\n            description=\"Fetches and summarizes tech news.\",\n            llm=ChatBot(model=\"gpt-4o\"),\n            tools=[self.fetch_news, self.send_email]\n        )\n\n    @Tool\n    def fetch_news(self, topic: str) -> str:\n        \"\"\"Fetches latest news about a topic. Returns JSON string of articles.\"\"\"\n        # Mock implementation\n        return f\"Found 5 articles about {topic}...\"\n\n    @Tool\n    def send_email(self, recipient: str, subject: str, content: str) -> str:\n        \"\"\"Sends the summarized content via email.\"\"\"\n        print(f\"Sending email to {recipient}: {subject}\")\n        return \"Email sent successfully.\"\n\n# Usage\nasync def run_agent():\n    agent = NewsDigestAgent()\n    await agent.initialize()\n    result = await agent.run(\"Send me a digest of today's AI news to user@example.com\")\n    print(result)\n```\n\n### Step 4: Orchestration (StateGraph)\n\nFor complex workflows with strict control flow, loops, or state management, use `StateGraph`.\n\n```python\nfrom typing import TypedDict, Annotated\nfrom langgraph.graph import StateGraph, END\n\nclass AgentState(TypedDict):\n    topic: str\n    articles: list\n    summary: str\n\ndef fetch_node(state: AgentState):\n    # Fetch logic\n    return {\"articles\": [\"Article 1\", \"Article 2\"]}\n\ndef summarize_node(state: AgentState):\n    # LLM summarization logic\n    summary = f\"Summary of {len(state['articles'])} articles...\"\n    return {\"summary\": summary}\n\ndef notify_node(state: AgentState):\n    # Send notification\n    print(f\"Sending: {state['summary']}\")\n    return {}\n\n# Define Graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"fetch\", fetch_node)\nworkflow.add_node(\"summarize\", summarize_node)\nworkflow.add_node(\"notify\", notify_node)\n\nworkflow.set_entry_point(\"fetch\")\nworkflow.add_edge(\"fetch\", \"summarize\")\nworkflow.add_edge(\"summarize\", \"notify\")\nworkflow.add_edge(\"notify\", END)\n\napp = workflow.compile()\n```\n\n## Example Workflows\n\n### 1. Automated Code Reviewer\n\n**Goal**: Automatically review new Pull Requests and post comments.\n\n1.  **Trigger**: GitHub Webhook (PR Created).\n2.  **Step 1 (Fetch)**: Get PR diff using GitHub API.\n3.  **Step 2 (Analyze)**: Send diff to LLM with prompt \"Identify bugs and security risks\".\n4.  **Step 3 (Filter)**: Ignore minor style issues if configured.\n5.  **Step 4 (Post)**: Post review comments to GitHub PR.\n\n### 2. Customer Support Triage\n\n**Goal**: Categorize and draft responses for support tickets.\n\n1.  **Trigger**: New ticket in Zendesk\u002FJira.\n2.  **Step 1 (Read)**: Extract ticket subject and body.\n3.  **Step 2 (Classify)**: LLM classifies intent (Billing, Technical, Feature Request).\n4.  **Step 3 (RAG)**: Retrieve relevant documentation based on classification.\n5.  **Step 4 (Draft)**: Generate response using retrieved docs.\n6.  **Step 5 (Human Review)**: Save as \"Draft\" status for human agent approval.\n\n### 3. Social Media Content Calendar\n\n**Goal**: Generate and schedule posts from a blog RSS feed.\n\n1.  **Trigger**: New item in RSS feed.\n2.  **Step 1 (Extract)**: Get title, content, and URL.\n3.  **Step 2 (Generate)**: Create variations for Twitter (short), LinkedIn (professional), and Facebook.\n4.  **Step 3 (Image)**: Generate a thumbnail using DALL-E \u002F Midjourney API.\n5.  **Step 4 (Schedule)**: Post to Buffer\u002FHootsuite API.\n\n## Tools & Integrations\n\n| Category          | Common Tools                                | Use Case                             |\n| :---------------- | :------------------------------------------ | :----------------------------------- |\n| **Communication** | Slack, Discord, Telegram, Email (SMTP\u002FIMAP) | Notifications, Chatbots, Alerts      |\n| **Development**   | GitHub, GitLab, Jira, Linear                | Code review, Issue tracking          |\n| **Data\u002FDocs**     | Notion, Google Sheets, Airtable, Vector DBs | Knowledge base, CRM, structured data |\n| **Social**        | Twitter (X), LinkedIn, Reddit               | Content publishing, monitoring       |\n| **Search**        | Tavily, Google Search, Bing                 | Real-time information retrieval      |\n\n## Debugging & Monitoring\n\n- **Traceability**: Use tools like LangSmith or Arize Phoenix to trace LLM calls and agent steps.\n- **Verbose Mode**: Enable verbose logging in your agent framework to see intermediate thoughts and tool outputs.\n- **Dry Run**: Implement a \"dry run\" flag that mocks side-effect actions (like sending emails or deleting files).\n\n## Security & Privacy\n\n- **Least Privilege**: Give the agent only the permissions it needs (e.g., read-only access to production DBs).\n- **PII Redaction**: Detect and redact Personally Identifiable Information before sending data to LLM providers.\n- **Human Approval**: Always require human confirmation for high-stakes actions (e.g., transferring funds, deploying code).\n\n## Best Practices\n\n- **Idempotency**: Ensure steps can be retried without side effects.\n- **Graceful Degradation**: If a non-critical tool fails (e.g., image generation), the workflow should continue or notify the user rather than crashing.\n- **Modular Design**: Create reusable tools (e.g., a generic `send_slack_message` tool) that can be used across multiple agents.\n","# AI Workflow Automation\n\nCreate intelligent, automated workflows that combine AI reasoning with external tool execution. This skill enables the construction of robust pipelines for data processing, content generation, and system integration, moving beyond simple scripts to adaptive, state-aware agents.\n\n## When to Use\n\n- **Complex Pipelines**: User wants to connect multiple APIs (e.g., \"fetch data from X, process with Y, send to Z\").\n- **Intelligent Automation**: User wants to \"automate a daily report\", \"build a news digest bot\", or \"triage customer emails\".\n- **Background Tasks**: User asks for a \"background worker\" or \"cron job\" with AI capabilities.\n- **Decision Support**: User wants to streamline a repetitive manual process involving decision making (e.g., \"approve if score > 80, else review\").\n\n## Decision Flow\n\n**Determine the complexity and requirements of the automation.**\n\n```\nUser Request for Automation\n        │\n  Analyze Complexity\n        │\n   ┌────┴─────────────────┐\n Simple                 Complex\n (Linear Sequence)      (Branching\u002FLooping\u002FState)\n    │                      │\n  Script                 Agent \u002F Graph\n  Based                  Based\n    │                      │\n    │                  Need Human Approval?\n    │                  ┌───┴───┐\n    │                 Yes     No\n    │                  │       │\n    │             Human-in-  Fully\n    │             the-loop   Autonomous\n    │                  │       │\n    └──────┬───────────┴───────┘\n           │\n     Select Tools & APIs\n           │\n     Define State Schema\n           │\n     Implement Logic\n           │\n     Test & Deploy\n```\n\n## Core Concepts\n\n1.  **Triggers**: Events that initiate the workflow (e.g., Schedule\u002FCron, Webhook, Manual Input, File Upload).\n2.  **Steps (Nodes)**: Discrete units of work.\n    - _Action_: Executing a tool or API call (deterministic).\n    - _Reasoning_: LLM processing (summarization, decision making, extraction).\n3.  **State**: The shared context passed between steps (e.g., `messages`, `current_data`, `errors`).\n4.  **Routing (Edges)**: Logic to determine the next step based on current state (e.g., \"if error, go to retry; else go to finish\").\n5.  **Human-in-the-loop**: Pausing execution for human review before proceeding with critical actions.\n\n## Implementation Guide\n\n### Step 1: Define the Workflow\n\nClearly outline the inputs, processing steps, decision points, and desired outputs.\n\n_Example: Daily Tech News Digest_\n\n- **Input**: RSS feeds or NewsAPI query.\n- **Process**:\n  1. Fetch latest articles.\n  2. Filter for relevance (e.g., \"AI\", \"LLM\").\n  3. Summarize each article into 3 bullet points.\n  4. Generate an HTML email template.\n- **Output**: Send email via SMTP or SendGrid.\n\n### Step 2: Select Tools & Manage Secrets\n\nIdentify necessary integrations and ensure API keys are managed securely (e.g., `.env` file).\n\n- **Data Source**: `requests` (Python), `NewsAPI`, `Tavily`.\n- **Processing**: `OpenAI` or `Anthropic` (via SpoonOS).\n- **Output**: `smtplib`, `SendGrid`, `Slack Webhook`.\n\n**Security Note**: Never hardcode API keys. Use `os.getenv(\"API_KEY\")`.\n\n### Step 3: Build the Agent (SpoonReactSkill)\n\nFor simpler, tool-using agents, use `SpoonReactSkill`.\n\n```python\nimport os\nfrom spoon_ai.agents import SpoonReactSkill\nfrom spoon_ai.tools import Tool\nfrom spoon_ai.llm import ChatBot\n\nclass NewsDigestAgent(SpoonReactSkill):\n    def __init__(self):\n        super().__init__(\n            name=\"news_digest_bot\",\n            description=\"Fetches and summarizes tech news.\",\n            llm=ChatBot(model=\"gpt-4o\"),\n            tools=[self.fetch_news, self.send_email]\n        )\n\n    @Tool\n    def fetch_news(self, topic: str) -> str:\n        \"\"\"Fetches latest news about a topic. Returns JSON string of articles.\"\"\"\n        # Mock implementation\n        return f\"Found 5 articles about {topic}...\"\n\n    @Tool\n    def send_email(self, recipient: str, subject: str, content: str) -> str:\n        \"\"\"Sends the summarized content via email.\"\"\"\n        print(f\"Sending email to {recipient}: {subject}\")\n        return \"Email sent successfully.\"\n\n# Usage\nasync def run_agent():\n    agent = NewsDigestAgent()\n    await agent.initialize()\n    result = await agent.run(\"Send me a digest of today's AI news to user@example.com\")\n    print(result)\n```\n\n### Step 4: Orchestration (StateGraph)\n\nFor complex workflows with strict control flow, loops, or state management, use `StateGraph`.\n\n```python\nfrom typing import TypedDict, Annotated\nfrom langgraph.graph import StateGraph, END\n\nclass AgentState(TypedDict):\n    topic: str\n    articles: list\n    summary: str\n\ndef fetch_node(state: AgentState):\n    # Fetch logic\n    return {\"articles\": [\"Article 1\", \"Article 2\"]}\n\ndef summarize_node(state: AgentState):\n    # LLM summarization logic\n    summary = f\"Summary of {len(state['articles'])} articles...\"\n    return {\"summary\": summary}\n\ndef notify_node(state: AgentState):\n    # Send notification\n    print(f\"Sending: {state['summary']}\")\n    return {}\n\n# Define Graph\nworkflow = StateGraph(AgentState)\nworkflow.add_node(\"fetch\", fetch_node)\nworkflow.add_node(\"summarize\", summarize_node)\nworkflow.add_node(\"notify\", notify_node)\n\nworkflow.set_entry_point(\"fetch\")\nworkflow.add_edge(\"fetch\", \"summarize\")\nworkflow.add_edge(\"summarize\", \"notify\")\nworkflow.add_edge(\"notify\", END)\n\napp = workflow.compile()\n```\n\n## Example Workflows\n\n### 1. Automated Code Reviewer\n\n**Goal**: Automatically review new Pull Requests and post comments.\n\n1.  **Trigger**: GitHub Webhook (PR Created).\n2.  **Step 1 (Fetch)**: Get PR diff using GitHub API.\n3.  **Step 2 (Analyze)**: Send diff to LLM with prompt \"Identify bugs and security risks\".\n4.  **Step 3 (Filter)**: Ignore minor style issues if configured.\n5.  **Step 4 (Post)**: Post review comments to GitHub PR.\n\n### 2. Customer Support Triage\n\n**Goal**: Categorize and draft responses for support tickets.\n\n1.  **Trigger**: New ticket in Zendesk\u002FJira.\n2.  **Step 1 (Read)**: Extract ticket subject and body.\n3.  **Step 2 (Classify)**: LLM classifies intent (Billing, Technical, Feature Request).\n4.  **Step 3 (RAG)**: Retrieve relevant documentation based on classification.\n5.  **Step 4 (Draft)**: Generate response using retrieved docs.\n6.  **Step 5 (Human Review)**: Save as \"Draft\" status for human agent approval.\n\n### 3. Social Media Content Calendar\n\n**Goal**: Generate and schedule posts from a blog RSS feed.\n\n1.  **Trigger**: New item in RSS feed.\n2.  **Step 1 (Extract)**: Get title, content, and URL.\n3.  **Step 2 (Generate)**: Create variations for Twitter (short), LinkedIn (professional), and Facebook.\n4.  **Step 3 (Image)**: Generate a thumbnail using DALL-E \u002F Midjourney API.\n5.  **Step 4 (Schedule)**: Post to Buffer\u002FHootsuite API.\n\n## Tools & Integrations\n\n| Category          | Common Tools                                | Use Case                             |\n| :---------------- | :------------------------------------------ | :----------------------------------- |\n| **Communication** | Slack, Discord, Telegram, Email (SMTP\u002FIMAP) | Notifications, Chatbots, Alerts      |\n| **Development**   | GitHub, GitLab, Jira, Linear                | Code review, Issue tracking          |\n| **Data\u002FDocs**     | Notion, Google Sheets, Airtable, Vector DBs | Knowledge base, CRM, structured data |\n| **Social**        | Twitter (X), LinkedIn, Reddit               | Content publishing, monitoring       |\n| **Search**        | Tavily, Google Search, Bing                 | Real-time information retrieval      |\n\n## Debugging & Monitoring\n\n- **Traceability**: Use tools like LangSmith or Arize Phoenix to trace LLM calls and agent steps.\n- **Verbose Mode**: Enable verbose logging in your agent framework to see intermediate thoughts and tool outputs.\n- **Dry Run**: Implement a \"dry run\" flag that mocks side-effect actions (like sending emails or deleting files).\n\n## Security & Privacy\n\n- **Least Privilege**: Give the agent only the permissions it needs (e.g., read-only access to production DBs).\n- **PII Redaction**: Detect and redact Personally Identifiable Information before sending data to LLM providers.\n- **Human Approval**: Always require human confirmation for high-stakes actions (e.g., transferring funds, deploying code).\n\n## Best Practices\n\n- **Idempotency**: Ensure steps can be retried without side effects.\n- **Graceful Degradation**: If a non-critical tool fails (e.g., image generation), the workflow should continue or notify the user rather than crashing.\n- **Modular Design**: Create reusable tools (e.g., a generic `send_slack_message` tool) that can be used across multiple agents.\n",{"name":5,"tags":50,"author":58,"license":15,"version":59,"description":7,"dependencies":60,"allowed-tools":63},[51,52,53,54,55,56,57],"AI","Automation","Workflow","Productivity","Agent","Integration","Python","with-philia","1.1.0",[61,37,62],"spoonos-sdk","pydantic","Python(spoon_ai:*), Python(langgraph:*)","main","3df1d0d67b30ff15236561f8c0f9f06d33f42dfd","skills\u002Fai-crypto\u002Fxspoonai-official-workflow-automation",17,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia","https:\u002F\u002Feth2030.com","Hardcore infra. Building @alt-research and applied cryptography.","2026-11-13 02:31:19",{"source":8,"skillName":5,"commands":74,"prompt":84,"zipAvailable":85},[75,78,81],{"tool":76,"command":77},"npx","npx skills add jiayaoqijia\u002Fcryptoskill --skill workflow-automation",{"tool":79,"command":80},"bunx","bunx skills add jiayaoqijia\u002Fcryptoskill --skill workflow-automation",{"tool":82,"command":83},"pnpm","pnpm dlx skills add jiayaoqijia\u002Fcryptoskill --skill workflow-automation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fai-crypto\u002Fxspoonai-official-workflow-automation\u002FSKILL.md",true,[87,93],{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"SOURCE.md","REFERENCE","参考文档",371,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fai-crypto\u002Fxspoonai-official-workflow-automation\u002FSOURCE.md",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"TRUST.auto.yaml",2232,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fai-crypto\u002Fxspoonai-official-workflow-automation\u002FTRUST.auto.yaml",[98,125,147,165,178,188],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":102,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":45},"01M712C436EQYE43342Q4E7GG1","recipe-performance","Diagnose a specific MetaMask user flow's real-app performance and Sentry critical-user-flow telemetry with deterministic mm-harness recipes. Use for CUF boundaries, cold\u002Fwarm\u002Fbackground-resume behavior, latency regressions, lifecycle tags, duplicate or blocking network requests, long tasks, layout\u002Fpaint cost, screen redraws, React rerenders, or browser trace analysis where tests alone cannot prove the real app.","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fwallets\u002Fmetamask-official-recipe-performance\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fwallets\u002Fmetamask-official-recipe-performance\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115,116,117,118,41,119,120,121,122,123,124],"performance","recipe 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