LangGraph Architecture Analysis Skill

A skill for analyzing LangGraph application architecture, identifying bottlenecks, and proposing multiple improvement strategies.

📋 Overview

This skill analyzes existing LangGraph applications and proposes graph structure improvements:

1. Current State Analysis: Performance measurement and graph structure understanding
2. Problem Identification: Organizing bottlenecks and architectural issues
3. Improvement Proposals: Generate 3-5 diverse improvement proposals (all candidates for parallel exploration)

Important:

• This skill only performs analysis and proposals. It does not implement changes.
• Output all improvement proposals. The arch-tune command will implement and evaluate them in parallel.

🎯 When to Use

Use this skill in the following situations:

1. When performance improvement of existing applications is needed

   • Latency exceeds targets
   • Cost is too high
   • Accuracy is insufficient

2. When considering architecture-level improvements

   • Prompt optimization (fine-tune) has limitations
   • Graph structure changes are needed
   • Considering introduction of new patterns

3. When you want to compare multiple improvement options

   • Unclear which architecture is optimal
   • Want to understand trade-offs

📖 Analysis and Proposal Workflow

Step 1: Verify Evaluation Environment

Purpose: Prepare for performance measurement

Actions:

1. Verify existence of ev

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LangGraph Application Fine-Tuning Skill

A skill for iteratively optimizing prompts and processing logic in each node of a LangGraph application based on evaluation criteria.

📋 Overview

This skill executes the following process to improve the performance of existing LangGraph applications:

1. Load Objectives: Retrieve optimization goals and evaluation criteria from .langgraph-master/fine-tune.md (if this file doesn’t exist, help the user create it based on their requirements)
2. Identify Optimization Targets: Extract nodes containing LLM prompts using Serena MCP (if Serena MCP is unavailable, investigate the codebase using ls, read, etc.)
3. Baseline Evaluation: Measure current performance through multiple runs
4. Implement Improvements: Identify the most effective improvement areas and optimize prompts and processing logic
5. Re-evaluation: Measure performance after improvements
6. Iteration: Repeat steps 4-5 until goals are achieved

Important Constraint: Only optimize prompts and processing logic within each node without modifying the graph structure (nodes, edges configuration).

🎯 When to Use This Skill

Use this skill in the following situations:

1. When performance improvement of existing applications is needed

   • Want to improve LLM output quality
   • Want to improve response speed
   • Want to reduce error rate

2. When evaluation criteria are clear

   • Optimization goals are defined in `.langgraph-master/fine

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LangGraph Agent Construction Skill

A comprehensive guide for building AI agents using LangGraph.

📚 Learning Content

01. Core Concepts

Understanding the three core elements of LangGraph

• State
• Node
• Edge
• Advantages of the graph-based approach

02. Graph Architecture

Six major graph patterns and agent design

• Workflow vs Agent Differences
• Prompt Chaining (Sequential Processing)
• Parallelization
• Routing (Branching)
• Orchestrator-Worker
• Evaluator-Optimizer
• Agent (Autonomous Tool Usage)
• Subgraph

03. Memory Management

Persistence and checkpoint functionality

• Checkpointer
• Store (Long-term Memory)
• Persistence

04. Tool Integration

External tool integration and execution control

• Tool Definition
• [Command API (Control API)

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