[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADKFJ9PDJ2E0YNKV62AR6M":3,"skill-same:01M6ADKFJ9PDJ2E0YNKV62AR6M":91},{"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":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6ADKFJ9PDJ2E0YNKV62AR6M","agents-autogpt",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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fagents-autogpt\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],"agents autogpt","autonomous","agent platform","when","for","orchestra","ai automation","multi","platform","visual","builder","use","research","AI Platform","Visual Builder","Workflow Automation","AutoGPT","autonomous agents","automation","workflow","ai agent","2026-11-10 10:12:30",false,"---\nname: agents-autogpt\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":15,"version":54,"description":7,"dependencies":55},[51,40,52,39,38,37],"Agents","Autonomous Agents","Orchestra Research","1.0.0",[56],"autogpt-platform>=0.4.0","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fagents-autogpt",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add davila7\u002Fclaude-code-templates --skill agents-autogpt",{"tool":73,"command":74},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill agents-autogpt",{"tool":76,"command":77},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill agents-autogpt","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fagents-autogpt\u002FSKILL.md",true,[81,87],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",13209,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fagents-autogpt\u002Freferences\u002Fadvanced-usage.md",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Ftroubleshooting.md",7689,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fagents-autogpt\u002Freferences\u002Ftroubleshooting.md",[92,117,144,177,211,235],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":99,"publishedAt":45},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[98],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[100,101,102,103,104,28,105,106,35,27,107,108,109,110,111,112,113,114,115,116],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":124,"publishedAt":45},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[123],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[125,126,127,128,129,130,131,132,133,134,135,27,136,137,138,139,140,141,142,143],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":159,"publishedAt":45},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[150,153,154],{"categoryNo":151,"categoryName":152,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[160,161,162,163,164,165,166,28,167,168,169,170,171,172,173,35,174,175,27,176],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":181,"licenseFileUrl":182,"sourceHtmlUrl":183,"categories":184,"keywords":188,"publishedAt":45},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[185],{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[189,190,191,192,34,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,28,210],"artifacts builder","web","web artifacts builder","artifacts","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":181,"licenseFileUrl":215,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":45},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":186,"categoryName":187,"taxonomyType":21,"taxonomyTypeDesc":22},[223,224,225,226,227,190,228,198,199,229,230,231,232,28,233,108,234],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":181,"licenseFileUrl":239,"sourceHtmlUrl":240,"categories":241,"keywords":249,"publishedAt":45},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[242,245,246],{"categoryNo":243,"categoryName":244,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":247,"categoryName":248,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000036","内容创作者",[250,251,252,253,254,255,256,257,192,258,201,136,259,260,261,262,263,172,264,27,265,266,267,268,269,114,270],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]