gpt-researcher
GPT Researcher is an autonomous AI agent for comprehensive web and local research, generating detailed, cited reports using a planner-executor-publisher architecture.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
437 skills found
GPT Researcher is an autonomous AI agent for comprehensive web and local research, generating detailed, cited reports using a planner-executor-publisher architecture.
Develops reactive Livewire 4 components, handling wire directives, state management, real-time updates, component testing, and integration with Flux UI for high-performance Laravel applications.
A standardized workflow for converting raw PM notes, workshops, or rough drafts into polished, validated, and repository-compliant AI skills.
Expert guidance for building production-ready applications with Anthropic's Claude API. Covers SDKs, prompt caching, batch processing, streaming, tool use, and cost optimization strategies.
Architects enterprise AI agents from structured specs, generating production-ready code, data flow diagrams, and platform-specific logic for ServiceNow, Salesforce, and Snowflake.
Framework for building multi-agent systems, AgentOS runtimes, and MCP-integrated AI agents.
Expert Microsoft 365 tenant administration skill for setup, user lifecycle, security policy configuration, compliance, and automated PowerShell scripting for Global Administrators.
Extract, deobfuscate, and port WebGL/Canvas/Shader visual effects from websites into standalone, native JavaScript projects.
Automated GitHub PR review agent for code quality, security analysis, and standard compliance using gh CLI.
A unified interface for integrating and managing LLM chat providers like OpenAI, Anthropic, Google, Azure, and Bedrock within LangChain applications.
Autonomous multi-team codebase improvement agent with specialized modes: narrow (goal-directed), broad (hypothesis-divergent), and sweep (quality-focused).
Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.