context-optimization

name	context-optimization
version	1.0.0
description	[Utilities] Use when managing context window usage, compressing long sessions, or optimizing token usage.
disable-model-invocation	true

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

---

[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.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

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

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.