compact

name	compact
version	1.0.0
description	[Utilities] Use when you need to compress context to optimize token usage.
disable-model-invocation	false

Quick Summary

Goal: Compress conversation context to optimize token usage while preserving critical information.

Workflow:

1. Analyze -- Identify essential vs. expendable context
2. Compress -- Remove redundant information, summarize findings
3. Verify -- Ensure critical decisions and progress are preserved

Key Rules:

• Preserve: decisions made, files modified, current task state
• Remove: redundant tool outputs, repeated searches, verbose logs
• Use when context window approaches limits

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

Compact Context

Proactively compress the current conversation context to optimize token usage.

When to Use

• Before starting a new task in a long session
• When working on multiple unrelated features
• At natural workflow checkpoints (after commits, PR creation)
• When context indicator shows high usage

Actions

1. Summarize completed work - What was done, key decisions made
2. Preserve essential context - Active file paths, current task, blockers
3. Clear redundant history - Old exploration, superseded plans
4. Update memory - Save important patterns to .claude/memory/

Best Practices

• Use /compact at natural breakpoints, not mid-task
• After compacting, briefly restate the current objective
• Check that critical file paths are still accessible
• If working on a bug, preserve error messages and stack traces

Context Preservation Checklist

Before compacting, ensure you've saved:

• Current branch and uncommitted changes status
• Active file paths being modified
• Any error messages or stack traces
• Key decisions and their rationale
• Pending items from todo list

Example Usage

User: /compact
Claude: Compacting context...

## Session Summary
- Implemented employee export feature
- Fixed validation bug in SaveEmployeeCommand
- Created unit tests for EmployeeHelper

## Active Context
- Branch: feature/employee-export
- Files: Employee.Application/Commands/ExportEmployees/
- Current task: Add pagination to export

## Cleared
- Exploration of unrelated notification code
- Superseded implementation approaches

Ready to continue with pagination implementation.

---

[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.