llm-application-dev
LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.5 and GPT-5.2
By Seth Hobson1.8k GitHub starsUpdated 5 days ago
Directory evidence
- Runtimes
- Claude Code
- Parsed components
- 8 skill or MCP entries
- Source updated
- Sep 19, 2026
- Manifest status
- Canonical path parsed
The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →
Install llm-application-dev for Claude Code
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install llm-application-dev-2@agent-plugin-marketplacePaste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/Dicklesworthstone/pi_agent_rustClone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is tests/ext_conformance/artifacts/agents-wshobson/llm-application-dev/.
Plugin files
├── .claude-plugin/plugin.json├── skills/embedding-strategies/SKILL.md├── skills/hybrid-search-implementation/SKILL.md├── skills/langchain-architecture/SKILL.md├── skills/llm-evaluation/SKILL.md├── skills/prompt-engineering-patterns/SKILL.md├── skills/rag-implementation/SKILL.md├── skills/similarity-search-patterns/SKILL.md└── skills/vector-index-tuning/SKILL.md
Included Skills8
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Plugin manifests1
{
"name": "llm-application-dev",
"description": "LLM application development with LangGraph, RAG systems, vector search, and AI agent architectures for Claude 4.5 and GPT-5.2",
"author": {
"name": "Seth Hobson",
"email": "[email protected]"
}
}For maintainers
If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.
[llm-application-dev on Agent Plugins Marketplace](https://pluginsmp.com/plugins/llm-application-dev-2)