[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XG167TJWPDFEGJ581QSJ6M":3,"skill-same:01M6XG167TJWPDFEGJ581QSJ6M":87},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6XG167TJWPDFEGJ581QSJ6M","autogpt-agents",null,"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.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fautogpt\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],"AutoGPT","autogpt agents","builder","agent platform","when","for","ai automation","multi","synthetic","platform","visual","ai agent","use","autonomous","Tools","AI Platform","Visual Builder","Workflow Automation","automation","workflow","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.\ncategory: llm-tools\nversion: 1.0.0\nauthor: Synthetic Sciences\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\n> **WARNING: Complex Infrastructure Required**\n> AutoGPT Platform requires Docker, PostgreSQL, Redis, and RabbitMQ. Setup typically\n> takes 30+ minutes and may fail on resource-constrained systems. The API evolves\n> rapidly and examples in this skill may be outdated.\n> Consider **CrewAI** or **LangChain** for simpler agent frameworks.\n\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\n> **WARNING: Complex Infrastructure Required**\n> AutoGPT Platform requires Docker, PostgreSQL, Redis, and RabbitMQ. Setup typically\n> takes 30+ minutes and may fail on resource-constrained systems. The API evolves\n> rapidly and examples in this skill may be outdated.\n> Consider **CrewAI** or **LangChain** for simpler agent frameworks.\n\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":49,"author":52,"license":15,"version":53,"category":54,"description":7,"dependencies":55},[50,24,51,41,40,39],"Agents","Autonomous Agents","Synthetic Sciences","1.0.0","llm-tools",[56],"autogpt-platform>=0.4.0","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fautogpt","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add fadrienne\u002FMyGitHub --skill autogpt-agents",{"tool":69,"command":70},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill autogpt-agents",{"tool":72,"command":73},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill autogpt-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\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\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fautogpt\u002Freferences\u002Fadvanced-usage.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Ftroubleshooting.md",7689,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fllm-tools\u002Fautogpt\u002Freferences\u002Ftroubleshooting.md",[88,115,140,174,201,228],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":95,"publishedAt":44},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[94],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[96,97,98,99,100,101,102,103,28,104,105,106,107,108,109,110,111,112,113,114],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":124,"publishedAt":44},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[106,125,96,126,127,128,28,129,130,131,29,132,36,133,134,135,136,137,138,139],"statistical analysis","academic research","implementing","selection","you","reporting","data","tests","with","test","research","results","statsmodels","models","help",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":155,"publishedAt":44},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[146,149,150],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[156,157,158,159,160,161,162,163,164,165,104,166,167,168,169,170,171,172,130,173],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":184,"publishedAt":44},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[180,183],{"categoryNo":181,"categoryName":182,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[185,157,186,187,188,189,190,191,192,164,28,193,194,195,196,197,198,199,29,200],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":213,"publishedAt":44},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[207,210],{"categoryNo":208,"categoryName":209,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":211,"categoryName":212,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000030","开发工程师",[214,215,135,216,217,218,219,103,220,28,104,106,221,222,223,224,225,226,172,227],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":232,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":241,"publishedAt":44},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[235,238,239,240],{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":151,"categoryName":152,"taxonomyType":153,"taxonomyTypeDesc":154},[156,242,243,244,245,246,185,247,248,28,104,106,249,250,251,252,253,254,157,172],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]