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Documentation Index

Fetch the complete documentation index at: https://docs.agents-squads.com/llms.txt

Use this file to discover all available pages before exploring further.

Why Context Matters

Context is the foundation of agent intelligence. The quality and structure of information you provide directly impacts:
  • Decision accuracy - Better context = better decisions
  • Token efficiency - Optimized context = lower costs
  • Response speed - Focused context = faster processing

Context Feeding Strategies

Progressive Disclosure

Start with minimal context, expand only when needed:
# Level 1: Task Context (always include)
- Current objective
- Immediate constraints
- Expected output format

# Level 2: Domain Context (include when relevant)
- Project conventions
- Team standards
- Historical decisions

# Level 3: Deep Context (include on-demand)
- Full codebase structure
- Complete documentation
- External references

Structured Context Files

Use project instruction files for persistent context:
project/
├── CLAUDE.md           # Project-level context
├── src/
│   └── CLAUDE.md       # Module-specific context
└── .agents/
    └── memory/         # Dynamic agent memory

Context Hierarchy

  1. System context - Model capabilities, safety guidelines
  2. User context - Personal preferences (global CLAUDE.md)
  3. Project context - Codebase rules (project CLAUDE.md)
  4. Task context - Current objective, constraints
  5. Dynamic context - Retrieved knowledge, memory queries

Optimization Techniques

Minimize Redundant Reads

# Bad: Reading the same file multiple times
Read file.ts
# ... do something ...
Read file.ts  # Redundant!

# Good: Read once, reference in context
Read file.ts
# Continue working with the content already in context

Use Targeted Queries

# Bad: Read entire codebase
Read **/*.ts

# Good: Query specific patterns
Grep "export function" --type ts
Glob "src/api/**/*.ts"

Leverage Memory

# Check existing knowledge before researching
squads memory query "authentication patterns"

# Update memory after discoveries
squads memory update engineering

Context Window Management

Monitor Usage

The context window has limits. Track usage:
ModelContext WindowRecommended Max
Claude Sonnet200K tokens~150K tokens
Claude Opus200K tokens~150K tokens
Claude Haiku200K tokens~150K tokens

When Context Gets Large

  1. Summarize - Compress verbose content
  2. Prune - Remove irrelevant sections
  3. Externalize - Store in memory, reference by key
  4. Delegate - Spawn sub-agents with focused context

Anti-Patterns

Avoid these common mistakes:
  • Loading entire repositories into context
  • Repeating instructions already in CLAUDE.md
  • Including raw API responses without filtering
  • Keeping stale context from previous tasks

Best Practices Checklist

  • Use CLAUDE.md for persistent project context
  • Query memory before starting research
  • Read files once, not repeatedly
  • Use Glob/Grep for targeted searches
  • Prune context when switching tasks
  • Update memory after significant discoveries

Token Economics

Understand and optimize token costs

Memory System

Learn about persistent agent memory