Agent skill

context-optimization

Use when managing context window usage, compressing long sessions, or optimizing token usage. Triggers on keywords like "context", "memory", "tokens", "compress", "summarize session", "context limit", "optimize context".

View SKILL.md on GitHub Repository
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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/context-optimization-congdon1207-agents-md

SKILL.md

Context Optimization & Management

Manage context window efficiently to maintain productivity in long sessions.

Context Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Context Window (~200K tokens)           │
├─────────────────────────────────────────────────────────────┤
│ System Prompt (CLAUDE.md excerpts)          ~2,000 tokens   │
│ ─────────────────────────────────────────────────────────── │
│ Working Memory (current task state)         ~10,000 tokens  │
│ ─────────────────────────────────────────────────────────── │
│ Retrieved Context (RAG from codebase)       ~20,000 tokens  │
│ ─────────────────────────────────────────────────────────── │
│ Episodic Memory (past session learnings)    ~5,000 tokens   │
│ ─────────────────────────────────────────────────────────── │
│ Tool Descriptions (relevant tools only)     ~3,000 tokens   │
└─────────────────────────────────────────────────────────────┘

Four Context Strategies

1. Writing (Save Important Context)

Save critical findings to persistent memory:

javascript
// After discovering important patterns or decisions
mcp__memory__create_entities([
    {
        name: 'EmployeeValidation',
        entityType: 'Pattern',
        observations: ['Uses PlatformValidationResult fluent API', 'Async validation via ValidateRequestAsync', 'Found in Growth.Application/UseCaseCommands/']
    }
]);

When to Write:

  • Discovered architectural patterns
  • Important business rules
  • Cross-service dependencies
  • Solution decisions

2. Selecting (Retrieve Relevant Context)

Load relevant memories at session start:

javascript
// Search for relevant patterns
mcp__memory__search_nodes({ query: 'Employee validation pattern' });

// Open specific entities
mcp__memory__open_nodes({ names: ['EmployeeValidation', 'GrowthService'] });

When to Select:

  • Starting a related task
  • Continuing previous work
  • Cross-referencing patterns

3. Compressing (Summarize Long Trajectories)

Create context anchors every 10 operations:

markdown
=== CONTEXT ANCHOR ===
Current Task: Implement employee leave request feature
Completed:

- Created LeaveRequest entity with validation
- Added SaveLeaveRequestCommand with handler
- Implemented entity event handler for notifications

Remaining:

- Create GetLeaveRequestListQuery
- Add controller endpoint
- Write unit tests

Key Findings:

- Leave requests use GrowthRootRepository
- Notifications via entity event handlers, not direct calls
- Validation uses PlatformValidationResult.AndAsync()

# Next Action: Create query handler with GetQueryBuilder pattern

4. Isolating (Use Sub-Agents)

Delegate specialized tasks to sub-agents:

javascript
// Explore codebase (reduced context)
Task({ subagent_type: 'Explore', prompt: 'Find all entity event handlers in Growth service' });

// Plan implementation (focused context)
Task({ subagent_type: 'Plan', prompt: 'Plan leave request approval workflow' });

When to Isolate:

  • Broad codebase exploration
  • Independent research tasks
  • Parallel investigations

Context Anchor Protocol

Every 10 operations, write a context anchor:

  1. Re-read original task from todo list or initial prompt
  2. Verify alignment with current work
  3. Write anchor summarizing progress
  4. Save to memory if discovering important patterns
markdown
=== CONTEXT ANCHOR [10] ===
Task: [Original task description]
Phase: [Current phase number]
Progress: [What's been completed]
Findings: [Key discoveries]
Next: [Specific next step]
Confidence: [High/Medium/Low]
===========================

Token-Efficient Patterns

File Reading

javascript
// ❌ Reading entire files
Read({ file_path: 'large-file.cs' });

// ✅ Read specific sections
Read({ file_path: 'large-file.cs', offset: 100, limit: 50 });

// ✅ Use grep to find specific content first
Grep({ pattern: 'class SaveEmployeeCommand', path: 'src/' });

Search Optimization

javascript
// ❌ Multiple sequential searches
Grep({ pattern: 'CreateAsync' });
Grep({ pattern: 'UpdateAsync' });
Grep({ pattern: 'DeleteAsync' });

// ✅ Combined pattern
Grep({ pattern: 'CreateAsync|UpdateAsync|DeleteAsync', output_mode: 'files_with_matches' });

Parallel Operations

javascript
// ✅ Parallel reads for independent files
[Read({ file_path: 'file1.cs' }), Read({ file_path: 'file2.cs' }), Read({ file_path: 'file3.cs' })];

Memory Management Commands

Save Session Summary

javascript
// Before ending session or hitting limits
const summary = {
    task: 'Implementing employee leave request feature',
    completed: ['Entity', 'Command', 'Handler'],
    remaining: ['Query', 'Controller', 'Tests'],
    discoveries: ['Use entity events for notifications'],
    files: ['LeaveRequest.cs', 'SaveLeaveRequestCommand.cs']
};

// Save to memory
mcp__memory__create_entities([
    {
        name: `Session_${new Date().toISOString().split('T')[0]}`,
        entityType: 'SessionSummary',
        observations: [JSON.stringify(summary)]
    }
]);

Load Previous Session

javascript
// At session start
mcp__memory__search_nodes({ query: 'Session leave request' });

Anti-Patterns

Anti-Pattern Better Approach
Reading entire large files Use offset/limit or grep first
Sequential searches Combine with OR patterns
Repeating same searches Cache results in memory
No context anchors Write anchor every 10 ops
Not using sub-agents Isolate exploration tasks
Forgetting discoveries Save to memory entities

Quick Reference

Token Estimation:

  • 1 line of code ≈ 10-15 tokens
  • 1 page of text ≈ 500 tokens
  • Average file ≈ 1,000-3,000 tokens

Context Thresholds:

  • 50K tokens: Consider compression
  • 100K tokens: Required compression
  • 150K tokens: Critical - save and summarize

Memory Commands:

  • mcp__memory__create_entities - Save new knowledge
  • mcp__memory__search_nodes - Find relevant context
  • mcp__memory__add_observations - Update existing entities

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