[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QMTE4H1RCFCH2WWYN6DWAF":3,"skill-same:01M6QMTE4H1RCFCH2WWYN6DWAF":89},{"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":56,"commitSha":57,"skillDirectory":58,"forksCount":14,"repositoryHtmlUrl":59,"repositoryHomepageUrl":60,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6QMTE4H1RCFCH2WWYN6DWAF","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.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fqc-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],"AutoGPT","autogpt agents","builder","for","orchestra","ai automation","multi","ai-research","systems","platform","visual","research","use","autonomous","AI Platform","Visual Builder","Workflow Automation","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]\ncategory: \"ai-research\"\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":49,"author":52,"license":15,"version":53,"category":31,"description":7,"dependencies":54},[50,24,51,40,39,38],"Agents","Autonomous Agents","Orchestra Research","1.0.0",[55],"autogpt-platform>=0.4.0","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fqc-autogpt","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill autogpt-agents",{"tool":71,"command":72},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill autogpt-agents",{"tool":74,"command":75},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill autogpt-agents","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fqc-autogpt\u002FSKILL.md",true,[79,85],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",13209,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fqc-autogpt\u002Freferences\u002Fadvanced-usage.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Ftroubleshooting.md",7689,"https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fqc-autogpt\u002Freferences\u002Ftroubleshooting.md",[90,119,141,166,191,213],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":99,"publishedAt":44},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,27,116,117,118],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":44},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[126,127,128,35,104,129,130,131,132,133,134,135,136,137,36,33,138,139,140,27],"twitter","x twitter growth","growth","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","grow","posting","competitive",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":148,"publishedAt":44},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[147],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[149,136,150,151,152,153,154,155,36,156,157,56,158,159,160,161,162,163,164,165],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":172,"publishedAt":44},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[173,174,175,176,177,178,179,180,181,36,182,111,183,184,185,186,187,188,189,190],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":44},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[197],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[42,199,26,200,201,151,178,202,203,204,205,206,136,207,110,208,209,210,211,212],"workflow builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":217,"categories":218,"keywords":219,"publishedAt":44},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[220,221,222,223,224,225,226,227,228,205,229,230,231,232,233,234,112,235,236,190],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]