Project Context Understanding - Deep Codebase Mapping

Purpose

This skill provides deep understanding of project structure, dependencies, connections, architecture patterns, and conventions. It maps the entire codebase to enable context-aware analysis and personalized recommendations.

Unique Value:

• Maps entire codebase structure (files, directories, modules)
• Maps dependencies (internal and external)
• Maps connections (imports, exports, API calls, data flows)
• Understands architecture patterns used
• Identifies codebase conventions and style
• Tracks relationships between components

When to Use:

• Before any major planning/build/review
• When understanding codebase structure is critical
• When dependencies need to be mapped
• When architecture patterns need to be identified
• When codebase conventions need to be understood

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Quick Start

Understand project context by mapping codebase structure, dependencies, and patterns.

Example:

1. Identify purpose: Planning file upload feature, need to understand project structure
2. Map structure: Scan directories, identify modules, components
3. Map dependencies: Internal dependencies, external libraries
4. Map patterns: Architecture patterns, conventions, style

Result: Complete project context map for context-aware analysis.

Functionality First Mandate

BEFORE mapping project context, understand what you're analyzing:

1. What is the purpose of this analysis?

   • What functionality are we planning/building/reviewing?
   • What context do we need to understand?

2. What scope is relevant?

   • Entire codebase or specific modules?
   • Frontend, backend, or both?
   • Specific features or components?

3. THEN map project context - Map structure, dependencies, connections relevant to the functionality

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Process

Phase 1: Understand Analysis Purpose (MANDATORY FIRST STEP)

Before mapping, clarify:

1. What functionality are we analyzing?

   • What problem are we solving?
   • What feature/component/code are we working with?

2. What context do we need?

   • Codebase structure?
   • Dependencies?
   • Architecture patterns?
   • Codebase conventions?
   • All of the above?

3. What scope is relevant?

   • Entire codebase?
   • Specific directories/modules?
   • Related files only?

Example:

• Purpose: Planning file upload feature
• Context Needed: Backend API structure, storage services, CRM integration patterns
• Scope: src/api/, src/services/, src/integrations/

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Phase 2: Map Codebase Structure

Reference: See REFERENCE.md for detailed mapping techniques including:

• Directory structure mapping commands
• File pattern discovery
• Module structure analysis
• Output format examples

Use Grep/Glob to discover file structure:

1. Directory Structure: See REFERENCE.md for commands
2. File Patterns: See REFERENCE.md for commands
3. Module Structure: See REFERENCE.md for commands

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Phase 7: Create Dependency Graph Visualization

Create text-based dependency graph:

1. Module Dependencies:

   • Map which modules import from which
   • Identify circular dependencies
   • Identify dependency chains

2. External Dependencies:

   • Map external library usage
   • Identify critical dependencies
   • Identify version constraints

3. Visualization:

   Dependency Graph:
   
   components/UploadForm
     ├─> api/files
     │     ├─> services/storage
     │     │     └─> aws-sdk (external)
     │     └─> services/crm-client
     │           └─> axios (external)
     └─> utils/validation
           └─> (no dependencies)
   

Document:

• Module dependency graph
• External dependency usage
• Critical dependency paths

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Phase 8: Track Component Relationships

Track relationships between components:

1. Component Hierarchy:

   • Parent-child relationships
   • Component composition patterns
   • Shared component usage

2. Data Flow:

   • Props flow (parent → child)
   • State flow (component → state management)
   • Event flow (child → parent)

3. Service Relationships:

   • Service dependencies
   • Service composition
   • Service interfaces

Document:

• Component relationships
• Data flow patterns
• Service relationships

Example Output:

Component Relationships:
UploadForm (parent)
  ├─> FileInput (child)
  │     └─> Receives: onFileSelect, accept, maxSize
  │     └─> Emits: fileSelected event
  ├─> ProgressBar (child)
  │     └─> Receives: progress (0-100)
  └─> ErrorMessage (child)
        └─> Receives: error message

Data Flow:
User selects file → FileInput → UploadForm → api/files → storage service

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Output Format

MANDATORY TEMPLATE - Use this exact structure:

# Project Context Analysis

## Analysis Purpose

[What functionality are we analyzing? What context do we need?]

## Codebase Structure

[Directory structure, file organization, module boundaries]

## Dependencies

[External dependencies, internal dependencies, dependency graph]

## Connections

[Import/export relationships, API calls, data flows]

## Architecture Patterns

[Architecture type, design patterns, pattern examples]

## Codebase Conventions

[Naming conventions, structure conventions, style conventions]

## Dependency Graph

[Text-based visualization of dependencies]

## Component Relationships

[Component hierarchy, data flow, service relationships]

## Key Insights

[Summary of critical findings relevant to functionality]

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Usage Guidelines

For Planning

1. Map entire codebase structure to understand where new features fit
2. Map dependencies to understand integration points
3. Map architecture patterns to follow existing patterns
4. Map conventions to maintain consistency

For Building

1. Map relevant modules to understand where to add code
2. Map dependencies to understand what's available
3. Map patterns to follow existing patterns
4. Map conventions to maintain code style

For Review

1. Map affected modules to understand impact
2. Map dependencies to check for breaking changes
3. Map patterns to verify pattern compliance
4. Map conventions to verify style compliance

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6. Accurate: Verify findings by reading actual files

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Troubleshooting

Common Issues:

1. Context mapping without understanding purpose

   • Symptom: Mapping everything, not relevant context
   • Cause: Didn't identify purpose of analysis
   • Fix: Identify purpose first, map only relevant context
   • Prevention: Always identify purpose before mapping

2. Incomplete context mapping

   • Symptom: Missing dependencies or connections
   • Cause: Didn't complete all mapping steps
   • Fix: Complete all steps: structure, dependencies, connections, patterns, conventions
   • Prevention: Always complete all mapping steps

3. Context not verified

   • Symptom: Mapped context inaccurate or outdated
   • Cause: Didn't verify findings by reading files
   • Fix: Verify findings by reading actual files
   • Prevention: Always verify findings by reading files

If issues persist:

• Verify purpose was identified first
• Check that all mapping steps were completed
• Ensure findings were verified by reading files
• Review key principles section

Key Principles

1. Purpose-Driven: Map context relevant to functionality being analyzed
2. Comprehensive: Map structure, dependencies, connections, patterns, conventions
3. Visual: Use text-based visualizations for clarity
4. Actionable: Provide insights that inform decisions
5. Efficient: Use Grep/Glob to discover patterns quickly
6. Accurate: Verify findings by reading actual files

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Common Mistakes to Avoid

1. Mapping Everything: Don't map entire codebase if only specific modules are relevant
2. Missing Dependencies: Don't forget to map internal dependencies
3. Ignoring Patterns: Don't miss architectural patterns that inform design
4. Generic Analysis: Don't provide generic analysis - be specific to codebase
5. No Verification: Don't assume structure - verify by reading files
6. Missing Connections: Don't forget to map import/export relationships

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This skill enables context-aware analysis and personalized recommendations by deeply understanding project structure, dependencies, connections, architecture patterns, and conventions.