By laurigates
Build and develop LangChain LLM applications in TypeScript/JavaScript and Python: initialize projects with Bun/NPM and core deps, create chains/agents/RAG with OpenAI/Anthropic/Zod, construct stateful LangGraph workflows with checkpoints/human-in-loop, and assemble hierarchical deep agents for multi-step orchestration, file context, subagents, and persistent memory.
Build hierarchical AI agents using the deep-agents TypeScript/npm package. Use when you want to create an orchestrator agent that plans and executes multi-step tasks, manages file system context, delegates subtasks to child agents, or maintains persistent memory across runs with the Deep Agents library.
LangChain JS/TS framework for building LLM-powered applications - models, chains, tools, and RAG patterns.
Initialize a new LangChain TypeScript project with recommended configuration
Build stateful AI agents in Python using LangGraph's graph-based workflow framework. Use when you want to create a state machine agent with checkpoints, define agent behavior as a graph of nodes and edges, add human-in-the-loop approval steps, or compose multiple agents as subgraphs in a LangGraph application.
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npx claudepluginhub laurigates/claude-plugins --plugin langchain-pluginBevy game engine development - ECS, rendering, game architecture
Test execution, TDD workflow, testing strategies, and quality analysis
Python development ecosystem - uv, ruff, pytest, packaging, type checking
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Home Assistant configuration management - YAML configuration, automations, scripts, scenes, and entity management for Home Assistant installations
Prompt engineering techniques for accurate, grounded Claude responses — anti-hallucination workflow with citation-backed analysis
Language-agnostic development process harness implementing the Stateless Agent Methodology (SAM) 7-stage pipeline with ARL human touchpoint model and Voltron-style language plugin composition. Provides orchestration, workflows, planning, verification, and testing methodology that any language plugin can compose with.
Complete collection of battle-tested Claude Code configs from an Anthropic hackathon winner - agents, skills, hooks, and rules evolved over 10+ months of intensive daily use
LLM application development with RAG, embeddings, LangChain, and prompt engineering
Expert agents for specific programming languages (Python, Go, Rust, etc.)
High-intelligence Claude Code copilot with deep code reasoning, evidence-driven planning, orchestration-first execution, model routing, context budgeting, CI/CD integration, enterprise security, plugin development, prompt engineering, performance profiling, agent teams, channels (event-driven autonomy with CI webhook, mobile approval relay, Discord bridge, and fakechat dev profile), interactive tutorials, LSP integration, security-hardened hook script library, MCP Prompts coverage, common workflow packs, runtime selection guide, computer-use patterns, checkpointing, scheduled-task blueprints, repo bootstrap scanner, hook policy engine (8 installable packs), layered memory deployment, role-based subagent packs (implementer, debugger, migration-lead, dependency-auditor, release-coordinator), 5 agent-team topology kits, autonomy operating mode (4 profiles + 3 gates), and a queryable 15-tool MCP documentation server with autonomy advisor.