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Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.","endman100\u002Fskill-router-nexus","endman100","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F31997019?v=4","01M6KPDX17BYDWW4RRB0TNGGMX","01M6KPDX64Y2QBEWVFG5W5PWPF",0,"MIT","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAgent-Execute\u002Fautogpt\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"AutoGPT","autogpt agents","builder","agent platform","when","for","orchestra","ai automation","multi","systems","platform","visual","research","use","autonomous","AI Platform","Visual Builder","Workflow Automation","autonomous agents","automation","workflow","ai agent","2026-11-10 10:12:30",false,"---\nname: autogpt-agents\ndescription: Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Agents, AutoGPT, Autonomous Agents, Workflow Automation, Visual Builder, AI Platform]\ndependencies: [autogpt-platform>=0.4.0]\n---\n\n# AutoGPT - Autonomous AI Agent Platform\n\nComprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.\n\n## When to use AutoGPT\n\n**Use AutoGPT when:**\n- Building autonomous agents that run continuously\n- Creating visual workflow-based AI agents\n- Deploying agents with external triggers (webhooks, schedules)\n- Building complex multi-step automation pipelines\n- Need a no-code\u002Flow-code agent builder\n\n**Key features:**\n- **Visual Agent Builder**: Drag-and-drop node-based workflow editor\n- **Continuous Execution**: Agents run persistently with triggers\n- **Marketplace**: Pre-built agents and blocks to share\u002Freuse\n- **Block System**: Modular components for LLM, tools, integrations\n- **Forge Toolkit**: Developer tools for custom agent creation\n- **Benchmark System**: Standardized agent performance testing\n\n**Use alternatives instead:**\n- **LangChain\u002FLlamaIndex**: If you need more control over agent logic\n- **CrewAI**: For role-based multi-agent collaboration\n- **OpenAI Assistants**: For simple hosted agent deployments\n- **Semantic Kernel**: For Microsoft ecosystem integration\n\n## Quick start\n\n### Installation (Docker)\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT.git\ncd AutoGPT\u002Fautogpt_platform\n\n# Copy environment file\ncp .env.example .env\n\n# Start backend services\ndocker compose up -d --build\n\n# Start frontend (in separate terminal)\ncd frontend\ncp .env.example .env\nnpm install\nnpm run dev\n```\n\n### Access the platform\n\n- **Frontend UI**: http:\u002F\u002Flocalhost:3000\n- **Backend API**: http:\u002F\u002Flocalhost:8006\u002Fapi\n- **WebSocket**: ws:\u002F\u002Flocalhost:8001\u002Fws\n\n## Architecture overview\n\nAutoGPT has two main systems:\n\n### AutoGPT Platform (Production)\n- Visual agent builder with React frontend\n- FastAPI backend with execution engine\n- PostgreSQL + Redis + RabbitMQ infrastructure\n\n### AutoGPT Classic (Development)\n- **Forge**: Agent development toolkit\n- **Benchmark**: Performance testing framework\n- **CLI**: Command-line interface for development\n\n## Core concepts\n\n### Graphs and nodes\n\nAgents are represented as **graphs** containing **nodes** connected by **links**:\n\n```\nGraph (Agent)\n  ├── Node (Input)\n  │   └── Block (AgentInputBlock)\n  ├── Node (Process)\n  │   └── Block (LLMBlock)\n  ├── Node (Decision)\n  │   └── Block (SmartDecisionMaker)\n  └── Node (Output)\n      └── Block (AgentOutputBlock)\n```\n\n### Blocks\n\nBlocks are reusable functional components:\n\n| Block Type | Purpose |\n|------------|---------|\n| `INPUT` | Agent entry points |\n| `OUTPUT` | Agent outputs |\n| `AI` | LLM calls, text generation |\n| `WEBHOOK` | External triggers |\n| `STANDARD` | General operations |\n| `AGENT` | Nested agent execution |\n\n### Execution flow\n\n```\nUser\u002FTrigger → Graph Execution → Node Execution → Block.execute()\n     ↓              ↓                 ↓\n  Inputs      Queue System      Output Yields\n```\n\n## Building agents\n\n### Using the visual builder\n\n1. **Open Agent Builder** at http:\u002F\u002Flocalhost:3000\n2. **Add blocks** from the BlocksControl panel\n3. **Connect nodes** by dragging between handles\n4. **Configure inputs** in each node\n5. **Run agent** using PrimaryActionBar\n\n### Available blocks\n\n**AI Blocks:**\n- `AITextGeneratorBlock` - Generate text with LLMs\n- `AIConversationBlock` - Multi-turn conversations\n- `SmartDecisionMakerBlock` - Conditional logic\n\n**Integration Blocks:**\n- GitHub, Google, Discord, Notion connectors\n- Webhook triggers and handlers\n- HTTP request blocks\n\n**Control Blocks:**\n- Input\u002FOutput blocks\n- Branching and decision nodes\n- Loop and iteration blocks\n\n## Agent execution\n\n### Trigger types\n\n**Manual execution:**\n```http\nPOST \u002Fapi\u002Fv1\u002Fgraphs\u002F{graph_id}\u002Fexecute\nContent-Type: application\u002Fjson\n\n{\n  \"inputs\": {\n    \"input_name\": \"value\"\n  }\n}\n```\n\n**Webhook trigger:**\n```http\nPOST \u002Fapi\u002Fv1\u002Fwebhooks\u002F{webhook_id}\nContent-Type: application\u002Fjson\n\n{\n  \"data\": \"webhook payload\"\n}\n```\n\n**Scheduled execution:**\n```json\n{\n  \"schedule\": \"0 *\u002F2 * * *\",\n  \"graph_id\": \"graph-uuid\",\n  \"inputs\": {}\n}\n```\n\n### Monitoring execution\n\n**WebSocket updates:**\n```javascript\nconst ws = new WebSocket('ws:\u002F\u002Flocalhost:8001\u002Fws');\n\nws.onmessage = (event) => {\n  const update = JSON.parse(event.data);\n  console.log(`Node ${update.node_id}: ${update.status}`);\n};\n```\n\n**REST API polling:**\n```http\nGET \u002Fapi\u002Fv1\u002Fexecutions\u002F{execution_id}\n```\n\n## Using Forge (Development)\n\n### Create custom agent\n\n```bash\n# Setup forge environment\ncd classic\n.\u002Frun setup\n\n# Create new agent from template\n.\u002Frun forge create my-agent\n\n# Start agent server\n.\u002Frun forge start my-agent\n```\n\n### Agent structure\n\n```\nmy-agent\u002F\n├── agent.py          # Main agent logic\n├── abilities\u002F        # Custom abilities\n│   ├── __init__.py\n│   └── custom.py\n├── prompts\u002F          # Prompt templates\n└── config.yaml       # Agent configuration\n```\n\n### Implement custom ability\n\n```python\nfrom forge import Ability, ability\n\n@ability(\n    name=\"custom_search\",\n    description=\"Search for information\",\n    parameters={\n        \"query\": {\"type\": \"string\", \"description\": \"Search query\"}\n    }\n)\ndef custom_search(query: str) -> str:\n    \"\"\"Custom search ability.\"\"\"\n    # Implement search logic\n    result = perform_search(query)\n    return result\n```\n\n## Benchmarking agents\n\n### Run benchmarks\n\n```bash\n# Run all benchmarks\n.\u002Frun benchmark\n\n# Run specific category\n.\u002Frun benchmark --category coding\n\n# Run with specific agent\n.\u002Frun benchmark --agent my-agent\n```\n\n### Benchmark categories\n\n- **Coding**: Code generation and debugging\n- **Retrieval**: Information finding\n- **Web**: Web browsing and interaction\n- **Writing**: Text generation tasks\n\n### VCR cassettes\n\nBenchmarks use recorded HTTP responses for reproducibility:\n\n```bash\n# Record new cassettes\n.\u002Frun benchmark --record\n\n# Run with existing cassettes\n.\u002Frun benchmark --playback\n```\n\n## Integrations\n\n### Adding credentials\n\n1. Navigate to Profile > Integrations\n2. Select provider (OpenAI, GitHub, Google, etc.)\n3. Enter API keys or authorize OAuth\n4. Credentials are encrypted and stored securely\n\n### Using credentials in blocks\n\nBlocks automatically access user credentials:\n\n```python\nclass MyLLMBlock(Block):\n    def execute(self, inputs):\n        # Credentials are injected by the system\n        credentials = self.get_credentials(\"openai\")\n        client = OpenAI(api_key=credentials.api_key)\n        # ...\n```\n\n### Supported providers\n\n| Provider | Auth Type | Use Cases |\n|----------|-----------|-----------|\n| OpenAI | API Key | LLM, embeddings |\n| Anthropic | API Key | Claude models |\n| GitHub | OAuth | Code, repos |\n| Google | OAuth | Drive, Gmail, Calendar |\n| Discord | Bot Token | Messaging |\n| Notion | OAuth | Documents |\n\n## Deployment\n\n### Docker production setup\n\n```yaml\n# docker-compose.prod.yml\nservices:\n  rest_server:\n    image: autogpt\u002Fplatform-backend\n    environment:\n      - DATABASE_URL=postgresql:\u002F\u002F...\n      - REDIS_URL=redis:\u002F\u002Fredis:6379\n    ports:\n      - \"8006:8006\"\n\n  executor:\n    image: autogpt\u002Fplatform-backend\n    command: poetry run executor\n\n  frontend:\n    image: autogpt\u002Fplatform-frontend\n    ports:\n      - \"3000:3000\"\n```\n\n### Environment variables\n\n| Variable | Purpose |\n|----------|---------|\n| `DATABASE_URL` | PostgreSQL connection |\n| `REDIS_URL` | Redis connection |\n| `RABBITMQ_URL` | RabbitMQ connection |\n| `ENCRYPTION_KEY` | Credential encryption |\n| `SUPABASE_URL` | Authentication |\n\n### Generate encryption key\n\n```bash\ncd autogpt_platform\u002Fbackend\npoetry run cli gen-encrypt-key\n```\n\n## Best practices\n\n1. **Start simple**: Begin with 3-5 node agents\n2. **Test incrementally**: Run and test after each change\n3. **Use webhooks**: External triggers for event-driven agents\n4. **Monitor costs**: Track LLM API usage via credits system\n5. **Version agents**: Save working versions before changes\n6. **Benchmark**: Use agbenchmark to validate agent quality\n\n## Common issues\n\n**Services not starting:**\n```bash\n# Check container status\ndocker compose ps\n\n# View logs\ndocker compose logs rest_server\n\n# Restart services\ndocker compose restart\n```\n\n**Database connection issues:**\n```bash\n# Run migrations\ncd backend\npoetry run prisma migrate deploy\n```\n\n**Agent execution stuck:**\n```bash\n# Check RabbitMQ queue\n# Visit http:\u002F\u002Flocalhost:15672 (guest\u002Fguest)\n\n# Clear stuck executions\ndocker compose restart executor\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom blocks, deployment, scaling\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.agpt.co\n- **Repository**: https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fautogpt\n- **License**: MIT (Classic) \u002F Polyform Shield (Platform)\n","# AutoGPT - Autonomous AI Agent Platform\n\nComprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.\n\n## When to use AutoGPT\n\n**Use AutoGPT when:**\n- Building autonomous agents that run continuously\n- Creating visual workflow-based AI agents\n- Deploying agents with external triggers (webhooks, schedules)\n- Building complex multi-step automation pipelines\n- Need a no-code\u002Flow-code agent builder\n\n**Key features:**\n- **Visual Agent Builder**: Drag-and-drop node-based workflow editor\n- **Continuous Execution**: Agents run persistently with triggers\n- **Marketplace**: Pre-built agents and blocks to share\u002Freuse\n- **Block System**: Modular components for LLM, tools, integrations\n- **Forge Toolkit**: Developer tools for custom agent creation\n- **Benchmark System**: Standardized agent performance testing\n\n**Use alternatives instead:**\n- **LangChain\u002FLlamaIndex**: If you need more control over agent logic\n- **CrewAI**: For role-based multi-agent collaboration\n- **OpenAI Assistants**: For simple hosted agent deployments\n- **Semantic Kernel**: For Microsoft ecosystem integration\n\n## Quick start\n\n### Installation (Docker)\n\n```bash\n# Clone repository\ngit clone https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT.git\ncd AutoGPT\u002Fautogpt_platform\n\n# Copy environment file\ncp .env.example .env\n\n# Start backend services\ndocker compose up -d --build\n\n# Start frontend (in separate terminal)\ncd frontend\ncp .env.example .env\nnpm install\nnpm run dev\n```\n\n### Access the platform\n\n- **Frontend UI**: http:\u002F\u002Flocalhost:3000\n- **Backend API**: http:\u002F\u002Flocalhost:8006\u002Fapi\n- **WebSocket**: ws:\u002F\u002Flocalhost:8001\u002Fws\n\n## Architecture overview\n\nAutoGPT has two main systems:\n\n### AutoGPT Platform (Production)\n- Visual agent builder with React frontend\n- FastAPI backend with execution engine\n- PostgreSQL + Redis + RabbitMQ infrastructure\n\n### AutoGPT Classic (Development)\n- **Forge**: Agent development toolkit\n- **Benchmark**: Performance testing framework\n- **CLI**: Command-line interface for development\n\n## Core concepts\n\n### Graphs and nodes\n\nAgents are represented as **graphs** containing **nodes** connected by **links**:\n\n```\nGraph (Agent)\n  ├── Node (Input)\n  │   └── Block (AgentInputBlock)\n  ├── Node (Process)\n  │   └── Block (LLMBlock)\n  ├── Node (Decision)\n  │   └── Block (SmartDecisionMaker)\n  └── Node (Output)\n      └── Block (AgentOutputBlock)\n```\n\n### Blocks\n\nBlocks are reusable functional components:\n\n| Block Type | Purpose |\n|------------|---------|\n| `INPUT` | Agent entry points |\n| `OUTPUT` | Agent outputs |\n| `AI` | LLM calls, text generation |\n| `WEBHOOK` | External triggers |\n| `STANDARD` | General operations |\n| `AGENT` | Nested agent execution |\n\n### Execution flow\n\n```\nUser\u002FTrigger → Graph Execution → Node Execution → Block.execute()\n     ↓              ↓                 ↓\n  Inputs      Queue System      Output Yields\n```\n\n## Building agents\n\n### Using the visual builder\n\n1. **Open Agent Builder** at http:\u002F\u002Flocalhost:3000\n2. **Add blocks** from the BlocksControl panel\n3. **Connect nodes** by dragging between handles\n4. **Configure inputs** in each node\n5. **Run agent** using PrimaryActionBar\n\n### Available blocks\n\n**AI Blocks:**\n- `AITextGeneratorBlock` - Generate text with LLMs\n- `AIConversationBlock` - Multi-turn conversations\n- `SmartDecisionMakerBlock` - Conditional logic\n\n**Integration Blocks:**\n- GitHub, Google, Discord, Notion connectors\n- Webhook triggers and handlers\n- HTTP request blocks\n\n**Control Blocks:**\n- Input\u002FOutput blocks\n- Branching and decision nodes\n- Loop and iteration blocks\n\n## Agent execution\n\n### Trigger types\n\n**Manual execution:**\n```http\nPOST \u002Fapi\u002Fv1\u002Fgraphs\u002F{graph_id}\u002Fexecute\nContent-Type: application\u002Fjson\n\n{\n  \"inputs\": {\n    \"input_name\": \"value\"\n  }\n}\n```\n\n**Webhook trigger:**\n```http\nPOST \u002Fapi\u002Fv1\u002Fwebhooks\u002F{webhook_id}\nContent-Type: application\u002Fjson\n\n{\n  \"data\": \"webhook payload\"\n}\n```\n\n**Scheduled execution:**\n```json\n{\n  \"schedule\": \"0 *\u002F2 * * *\",\n  \"graph_id\": \"graph-uuid\",\n  \"inputs\": {}\n}\n```\n\n### Monitoring execution\n\n**WebSocket updates:**\n```javascript\nconst ws = new WebSocket('ws:\u002F\u002Flocalhost:8001\u002Fws');\n\nws.onmessage = (event) => {\n  const update = JSON.parse(event.data);\n  console.log(`Node ${update.node_id}: ${update.status}`);\n};\n```\n\n**REST API polling:**\n```http\nGET \u002Fapi\u002Fv1\u002Fexecutions\u002F{execution_id}\n```\n\n## Using Forge (Development)\n\n### Create custom agent\n\n```bash\n# Setup forge environment\ncd classic\n.\u002Frun setup\n\n# Create new agent from template\n.\u002Frun forge create my-agent\n\n# Start agent server\n.\u002Frun forge start my-agent\n```\n\n### Agent structure\n\n```\nmy-agent\u002F\n├── agent.py          # Main agent logic\n├── abilities\u002F        # Custom abilities\n│   ├── __init__.py\n│   └── custom.py\n├── prompts\u002F          # Prompt templates\n└── config.yaml       # Agent configuration\n```\n\n### Implement custom ability\n\n```python\nfrom forge import Ability, ability\n\n@ability(\n    name=\"custom_search\",\n    description=\"Search for information\",\n    parameters={\n        \"query\": {\"type\": \"string\", \"description\": \"Search query\"}\n    }\n)\ndef custom_search(query: str) -> str:\n    \"\"\"Custom search ability.\"\"\"\n    # Implement search logic\n    result = perform_search(query)\n    return result\n```\n\n## Benchmarking agents\n\n### Run benchmarks\n\n```bash\n# Run all benchmarks\n.\u002Frun benchmark\n\n# Run specific category\n.\u002Frun benchmark --category coding\n\n# Run with specific agent\n.\u002Frun benchmark --agent my-agent\n```\n\n### Benchmark categories\n\n- **Coding**: Code generation and debugging\n- **Retrieval**: Information finding\n- **Web**: Web browsing and interaction\n- **Writing**: Text generation tasks\n\n### VCR cassettes\n\nBenchmarks use recorded HTTP responses for reproducibility:\n\n```bash\n# Record new cassettes\n.\u002Frun benchmark --record\n\n# Run with existing cassettes\n.\u002Frun benchmark --playback\n```\n\n## Integrations\n\n### Adding credentials\n\n1. Navigate to Profile > Integrations\n2. Select provider (OpenAI, GitHub, Google, etc.)\n3. Enter API keys or authorize OAuth\n4. Credentials are encrypted and stored securely\n\n### Using credentials in blocks\n\nBlocks automatically access user credentials:\n\n```python\nclass MyLLMBlock(Block):\n    def execute(self, inputs):\n        # Credentials are injected by the system\n        credentials = self.get_credentials(\"openai\")\n        client = OpenAI(api_key=credentials.api_key)\n        # ...\n```\n\n### Supported providers\n\n| Provider | Auth Type | Use Cases |\n|----------|-----------|-----------|\n| OpenAI | API Key | LLM, embeddings |\n| Anthropic | API Key | Claude models |\n| GitHub | OAuth | Code, repos |\n| Google | OAuth | Drive, Gmail, Calendar |\n| Discord | Bot Token | Messaging |\n| Notion | OAuth | Documents |\n\n## Deployment\n\n### Docker production setup\n\n```yaml\n# docker-compose.prod.yml\nservices:\n  rest_server:\n    image: autogpt\u002Fplatform-backend\n    environment:\n      - DATABASE_URL=postgresql:\u002F\u002F...\n      - REDIS_URL=redis:\u002F\u002Fredis:6379\n    ports:\n      - \"8006:8006\"\n\n  executor:\n    image: autogpt\u002Fplatform-backend\n    command: poetry run executor\n\n  frontend:\n    image: autogpt\u002Fplatform-frontend\n    ports:\n      - \"3000:3000\"\n```\n\n### Environment variables\n\n| Variable | Purpose |\n|----------|---------|\n| `DATABASE_URL` | PostgreSQL connection |\n| `REDIS_URL` | Redis connection |\n| `RABBITMQ_URL` | RabbitMQ connection |\n| `ENCRYPTION_KEY` | Credential encryption |\n| `SUPABASE_URL` | Authentication |\n\n### Generate encryption key\n\n```bash\ncd autogpt_platform\u002Fbackend\npoetry run cli gen-encrypt-key\n```\n\n## Best practices\n\n1. **Start simple**: Begin with 3-5 node agents\n2. **Test incrementally**: Run and test after each change\n3. **Use webhooks**: External triggers for event-driven agents\n4. **Monitor costs**: Track LLM API usage via credits system\n5. **Version agents**: Save working versions before changes\n6. **Benchmark**: Use agbenchmark to validate agent quality\n\n## Common issues\n\n**Services not starting:**\n```bash\n# Check container status\ndocker compose ps\n\n# View logs\ndocker compose logs rest_server\n\n# Restart services\ndocker compose restart\n```\n\n**Database connection issues:**\n```bash\n# Run migrations\ncd backend\npoetry run prisma migrate deploy\n```\n\n**Agent execution stuck:**\n```bash\n# Check RabbitMQ queue\n# Visit http:\u002F\u002Flocalhost:15672 (guest\u002Fguest)\n\n# Clear stuck executions\ndocker compose restart executor\n```\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Custom blocks, deployment, scaling\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues, debugging\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.agpt.co\n- **Repository**: https:\u002F\u002Fgithub.com\u002FSignificant-Gravitas\u002FAutoGPT\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fautogpt\n- **License**: MIT (Classic) \u002F Polyform Shield (Platform)\n",{"name":5,"tags":50,"author":53,"license":14,"version":54,"description":7,"dependencies":55},[51,23,52,40,39,38],"Agents","Autonomous Agents","Orchestra Research","1.0.0",[56],"autogpt-platform>=0.4.0","main","f19b254e8975d0bfddb394686706106930d7964d","Agent-Execute\u002Fautogpt","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus","https:\u002F\u002Fgithub.com\u002Fendman100","2026-11-13 20:29:52",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add endman100\u002Fskill-router-nexus --skill autogpt-agents",{"tool":69,"command":70},"bunx","bunx skills add endman100\u002Fskill-router-nexus --skill autogpt-agents",{"tool":72,"command":73},"pnpm","pnpm dlx skills add endman100\u002Fskill-router-nexus --skill autogpt-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAgent-Execute\u002Fautogpt\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",13209,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAgent-Execute\u002Fautogpt\u002Freferences\u002Fadvanced-usage.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Ftroubleshooting.md",7689,"https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FAgent-Execute\u002Fautogpt\u002Freferences\u002Ftroubleshooting.md",[88,112,143,165,174,198],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":92,"sourceHtmlUrl":93,"categories":94,"keywords":100,"publishedAt":45},"01M6MPCT5BXVER8HQZTW3QE9AS","create-master","基于佛教经典文献，生成特定高僧大德的 AI 教学角色","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FMetaphysics-and-Spirituality\u002FMaster-skill\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000039","教师与教育","PROFESSION","适用职业",[101,102,103,104,105,106,107,108,109,110,111],"create master","master","create","生成","true","webfetch","edit","write","read","grep","Bash",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":116,"sourceHtmlUrl":117,"categories":118,"keywords":126,"publishedAt":45},"01M6M43AKE95Y64E1JRXEF5QQ3","writing-dna-skill","从至少 20 篇完整文章中蒸馏可复用的写作 DNA，分析语言、文章结构、选题逻辑、素材策略、认知框架和视觉风格，并生成 Writing-DNA.md；按该 DNA 写作时会读取全部蒸馏产物和 5 篇相关原文。用于中英文作者、账号、品牌或出版物的风格分析与一致性写作。 Distill reusable Writing DNA from at least 20 complete articles for Chinese or English authors, publications, brands, and accounts; use for language, structure, topic logic, source strategy, cognitive-frame, and visual-style analysis.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fwriting-dna-skill\u002FSKILL.md",[119,122,123],{"categoryNo":120,"categoryName":121,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":124,"categoryName":125,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000036","内容创作者",[127,128,129,130,131,132,34,133,134,135,36,136,137,104,138,139,140,28,141,142],"writing dna skill","DNA","writing","strategy","english","logic","structure","chinese","reusable","style","distill","analysis","frame","source","at","complete",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":147,"sourceHtmlUrl":148,"categories":149,"keywords":152,"publishedAt":45},"01M6M428TTCGQ4ME3KY99VN037","stop-slop","Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fstop-slop\u002FSKILL.md",[150,151],{"categoryNo":120,"categoryName":121,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":124,"categoryName":125,"taxonomyType":98,"taxonomyTypeDesc":99},[153,154,155,156,27,157,158,159,160,36,161,162,163,28,164,129],"slop","stop slop","stop","drafting","drafts","https","patterns","prose","remove","editing","text","content",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":169,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":45},"01M6M41X0B7EBX42TK435DMGGR","shuorenhua","检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fshuorenhua\u002FSKILL.md",[],[167,173],"文本",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":178,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":45},"01M6M3ZD12WBEW5KXKDM4N6DFG","naming","Name products, SaaS, brands, open source projects, bots, and apps. Use when the user needs to name something, find a brand name, or pick a product name. Metaphor-driven process that produces memorable, meaningful names and avoids AI slop.","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fnaming-main\u002FSKILL.md",[],[176,182,183,184,185,186,36,187,109,140,188,189,190,191,111,192,193,194,195,27,196,197,153],"repo","websearch","product name","apps","bots","view","brand","pick","gh","projects","open","saas","open source","product","describe","ai slop",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":202,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":45},"01M6M3YVXAFAAWJ7F6JMPFREYE","humanizer-zh","去除文本中的 AI 生成痕迹。适用于编辑或审阅文本，使其听起来更自然、更像人类书写。\n基于维基百科的\"AI 写作特征\"综合指南。检测并修复以下模式：夸大的象征意义、\n宣传性语言、以 -ing 结尾的肤浅分析、模糊的归因、破折号过度使用、三段式法则、\nAI 词汇、否定式排比、过多的连接性短语。","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fendman100\u002Fskill-router-nexus\u002Fblob\u002Fmain\u002FWriting-and-Content\u002Fhumanizer-zh\u002FSKILL.md",[205],{"categoryNo":120,"categoryName":121,"taxonomyType":20,"taxonomyTypeDesc":21},[207,208,209,109,108,107,129,104,173,210,153,155],"humanizer","zh","humanizer zh","翻译"]