When Claude Doesn't Understand Your Project: How I Built an Agent to Fix Context Issues

August 18, 2025

I was deep into a complex microservices refactoring when I noticed something frustrating: Claude Code kept asking me the same basic questions about my project structure, deployment process, and coding conventions. Even with a CLAUDE.md file in place, it felt like Claude was working with incomplete information, constantly missing context that should have been obvious.

That’s when I realized my CLAUDE.md files had become sprawling, disorganized documents that weren’t actually helping Claude understand my projects effectively. I needed a systematic way to optimize these context files for better AI comprehension.

The Context Engineering Problem

Most developers treat CLAUDE.md files as simple documentation dumps. We throw in random project details, scattered commands, and architectural notes without considering how Claude actually processes and uses this information. The result? Claude gets confused, asks repetitive questions, and misses important project patterns.

I was experiencing this firsthand across multiple repositories:

Claude couldn’t understand the relationship between my services and shared libraries
It kept suggesting patterns I’d already documented as deprecated
Project-specific conventions weren’t being followed consistently
New team members (and Claude) struggled to understand our established workflows

The problem wasn’t that Claude lacked intelligence - it was that my context engineering was poor.

Building the Claude Context Engineer Agent

I decided to create a specialized agent that could analyze and optimize CLAUDE.md files using established memory architecture and context engineering principles. This agent focuses on five critical areas that directly impact Claude’s project comprehension:

1. Context Quality Assessment

The agent evaluates how well the current CLAUDE.md structure aligns with Claude’s processing patterns. It looks for:

Hierarchical information organization - Claude works best with clear information hierarchies
Actionable vs. descriptive content - Balancing what Claude needs to know vs. what it needs to do
Context density - Ensuring important information isn’t buried in verbose descriptions

2. Memory System Principles

Drawing from cognitive science and AI memory research, the agent applies specific principles:

Chunking: Breaking complex information into digestible, related pieces
Progressive disclosure: Structuring information from general to specific
Cross-referencing: Linking related concepts for better pattern recognition

3. Claude Code Feature Integration

The agent ensures CLAUDE.md files leverage Claude Code’s specific capabilities:

Tool usage patterns - Documenting which CLI tools are preferred and why
Permission configurations - Identifying commands that should be auto-approved
Context management strategies - When to use /cle vs /compact for different scenarios

4. Documentation Currency

One of the biggest issues I found was outdated context information. The agent identifies:

Deprecated patterns that should be removed or marked as legacy
Missing new conventions that have been established but not documented
Inconsistent information across different sections of the file

5. Best Practice Implementation

The agent applies proven patterns for effective AI context engineering:

Mental model frameworks - Structuring information to build clear mental models
Decision rationale - Explaining why certain patterns are preferred
Boundary conditions - Clarifying when rules apply and when they don’t

The Mental Model: Context as Claude’s Project Memory

graph TD subgraph "Traditional CLAUDE.md" T1[Random project info] T2[Scattered commands] T3[Inconsistent patterns] T4[Outdated information] Claude1[Claude] --> T1 Claude1 --> T2 Claude1 --> T3 Claude1 --> T4 Claude1 -.-> Confusion[Confused responses & repeated questions] end subgraph "Optimized CLAUDE.md" O1[Hierarchical context structure] O2[Memory-optimized information chunks] O3[Current best practices] O4[Clear mental models] O5[Cross-referenced patterns] Claude2[Claude] --> O1 Claude2 --> O2 Claude2 --> O3 Claude2 --> O4 Claude2 --> O5 Claude2 -.-> Understanding[Contextual awareness & consistent responses] end style Understanding fill:#99ff99 style Confusion fill:#ff9999

This visualization shows how context engineering transforms Claude’s project understanding from fragmented confusion to systematic comprehension.

Practical Implementation: What the Agent Actually Does

Context Structure Analysis

The agent starts by analyzing your existing CLAUDE.md file structure:

# Current Analysis
## Issues Found:
- Information hierarchy unclear (3 levels deep in some sections)
- Development commands scattered across multiple sections
- Missing rationale for architectural decisions
- Deprecated Docker commands still documented as primary approach

## Optimization Opportunities:
- Consolidate command references into structured sections
- Add decision context for tool choices
- Create clear boundary conditions for different scenarios

Memory-Optimized Restructuring

It then restructures information using memory principles:

## Project Memory Structure

### Core Identity
- What: Next.js 14 microservices platform
- Why: Scalable customer data processing
- How: Event-driven architecture with shared libraries

### Mental Models
[Key frameworks for understanding the system]

### Operational Patterns
[Common tasks and their preferred approaches]

### Boundary Conditions
[When to break the rules and why]

Claude Code Integration Patterns

The agent specifically optimizes for Claude Code workflows:

## Claude Code Optimization

### Auto-Approved Commands
- git operations (commits, branches, merges)
- npm/yarn package operations
- docker-compose for local development

### Context Management Strategy
- Use `/cle` when switching between features
- Use `/compact` during complex debugging sessions
- Reset context when moving between microservices

### Permission Patterns
- Development operations: Auto-approve
- Production deployments: Manual review required
- Database migrations: Always manual approval

When to Use the Context Engineer Agent

New Project Setup

When starting a new project, I run the agent to establish optimized CLAUDE.md patterns from the beginning. This prevents context debt from accumulating.

Context Performance Issues

If Claude is asking repetitive questions or missing obvious project patterns, it’s time for context optimization. The agent can identify what Claude is struggling to understand.

Team Onboarding

Before bringing new developers onto a project, I use the agent to ensure our CLAUDE.md files effectively communicate project context to both humans and AI.

Regular Maintenance

I run context audits quarterly to keep documentation current and remove outdated patterns that might confuse Claude.

Real-World Results

After implementing optimized CLAUDE.md files across my projects, I noticed:

Reduced Repetitive Questions

Claude stopped asking about basic project structure, deployment processes, and coding conventions. It “remembered” these patterns consistently across sessions.

Better Pattern Recognition

When working on new features, Claude could reference established patterns without being reminded. It understood the relationship between services and applied consistent architectural principles.

Improved Code Quality

With clear context about preferred patterns and anti-patterns, Claude suggested solutions that aligned with project standards rather than generic implementations.

Faster Onboarding

New team members could read the optimized CLAUDE.md files and quickly understand project conventions, tools, and decision rationale.

Getting the Claude Context Engineer Agent

If you want to use this specialized agent in your own projects, I’ve made the complete agent file available as a GitHub Gist:

📋 claude-context-engineer.md - GitHub Gist

Simply download the file and place it in your ~/.claude/agents/ directory. The agent includes detailed instructions for optimizing CLAUDE.md files, implementing memory system principles, and designing effective sub-agents for your specific project needs.

The agent has evolved since I first created it - the latest version also includes expertise in sub-agent architecture and design patterns, making it useful for building entire Claude Code agent ecosystems.

Implementation Strategy

Start with High-Impact Projects

I began with projects where context issues were most painful - complex microservices architectures with multiple repositories and shared dependencies.

Iterative Optimization

Rather than rewriting everything at once, I optimized sections incrementally, measuring Claude’s improved understanding after each change.

Team Feedback Loop

I involved the team in reviewing optimized context files, ensuring they worked for both human and AI comprehension.

Automation Integration

I integrated context quality checks into our development workflow, automatically flagging when CLAUDE.md files drift from best practices.

Key Learnings

Context engineering is a skill - Writing effective CLAUDE.md files requires understanding how Claude processes information
Memory principles apply to AI - Cognitive science concepts like chunking and progressive disclosure improve Claude’s comprehension
Structure matters more than content - How you organize information is often more important than how much information you include
Regular maintenance prevents context debt - Outdated or inconsistent context information actively hurts Claude’s performance
Claude Code integration amplifies benefits - Optimizing for specific Claude Code features creates multiplicative improvements
Team alignment improves with better context - Well-structured CLAUDE.md files help both humans and AI understand project patterns
Measurement enables optimization - Tracking Claude’s question patterns helps identify context gaps to address
Context quality affects code quality - Better project understanding leads to more appropriate solution suggestions

The Claude context engineer agent transformed how I think about AI project memory. Instead of treating CLAUDE.md files as simple documentation, I now approach them as carefully engineered memory systems that determine how effectively Claude can assist with complex development tasks.

If you’re experiencing context issues with Claude Code - repetitive questions, missed patterns, or inconsistent suggestions - the problem might not be Claude’s capabilities but rather how your project context is structured. Investing in proper context engineering pays dividends across every interaction you have with AI coding assistants.