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	false

Quick Summary

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

Workflow:

1. Compress — Create context anchors every 10 operations summarizing progress
2. Isolate — Delegate exploration tasks to sub-agents to reduce context usage

Cross-session persistence (saving findings to survive a new session) is out of scope here — use file checkpoints via /checkpoint and restore with /recover (see the memory-management skill).

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   │
└─────────────────────────────────────────────────────────────┘

---

Two Context Strategies

1. Compressing (Summarize Long Trajectories)

Create context anchors every 10 operations:

=== CONTEXT ANCHOR ===
Current Task: Implement order return request feature
Completed:

- Created Return entity with validation
- Added SaveReturnCommand with handler
- Implemented entity event handler for notifications

Remaining:

- Create GetReturnListQuery
- Add controller endpoint
- Write unit tests

Key Findings:

- Returns 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

Pre-Compaction Preservation Checklist (canonical for /compact)

Before a manual /compact (or any context compaction), confirm these are saved so they survive the cut — this is the canonical checklist the user-facing /compact alias delegates to:

• Current branch + uncommitted-changes status
• Active file paths being modified
• Any error messages / stack traces (preserve verbatim when mid-bug)
• Key decisions and their rationale
• Pending items from the todo list

Preserve decisions, files modified, current task state. Drop redundant tool outputs, repeated searches, verbose logs. Compact at natural breakpoints (after commits/PR), not mid-task; after compacting, restate the current objective.

2. 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 return 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. Persist to a checkpoint (/checkpoint) 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 SaveOrderCommand', path: '<source-root>/' });

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' })];

---

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	Reuse earlier results
No context anchors	Write anchor every 10 ops
Not using sub-agents	Isolate exploration tasks
Forgetting discoveries	Save findings to a checkpoint

---

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

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:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

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 + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

Closing Reminders

IMPORTANT MUST ATTENTION — Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.

• Critical Thinking: traced file:line proof per claim, confidence >80% to act, never guess.

• 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.