Agent Skills: Context Optimization & Management

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Quick Summary

Goal: Manage context window efficiently to maintain productivity in long Claude Code sessions.

Workflow:

1. Write — Save critical findings to persistent memory entities
2. Select — Retrieve relevant memories at session/task start
3. Compress — Create context anchors every 10 operations summarizing progress
4. Isolate — Delegate exploration tasks to sub-agents to reduce context usage

Key Rules:

• Write context anchor every 10 operations (re-read task, verify alignment, summarize)
• Use offset/limit and grep before reading large files
• Combine search patterns with OR instead of sequential searches
• At 100K tokens: required compression; at 150K: critical save and summarize

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

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:

// After discovering important patterns or decisions
mcp__memory__create_entities([
    {
        name: 'EmployeeValidation',
        entityType: 'Pattern',
        observations: ['Uses validation framework fluent API', 'Async validation via ValidateRequestAsync', 'Found in 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:

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

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

When to Select:

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

3. Compressing (Summarize Long Trajectories)

Create context anchors every 10 operations:

=== 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 service-specific repository
- Notifications via entity event handlers, not direct calls
- Validation uses validation framework fluent .AndAsync()

# Next Action: Create query handler with GetQueryBuilder pattern

4. Isolating (Use Sub-Agents)

Delegate specialized tasks to sub-agents:

// Explore codebase (reduced context)
Task({ subagent_type: 'Explore', prompt: 'Find all entity event handlers in the target 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

=== 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

// ❌ 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

// ❌ 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

// ✅ 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

// 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

// 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

Related

• memory-management

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality