omg-context

name	omg-context
description	Check context usage limits, monitor time remaining, optimize token consumption, debug context failures. Use when asking about context window, token budget, or agent context sizing.

Codex Port Notice

This skill was ported from upstream reference material. Interpret command names, paths, and agent-routing guidance as Codex/Oh My Game Kit equivalents. Prefer active Codex tools and local project instructions over Codex-specific mechanics when they conflict.

OMG Context Engineering

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage across all OMG workflows.

When to Activate

• Designing/debugging OMG agent systems (cook, fix, debug, test, review)
• Context limits constrain subagent performance
• Optimizing cost/latency in multi-agent pipelines
• Building module-scoped context injection
• Implementing agent memory and cross-session persistence

Core Principles

1. Context quality > quantity — High-signal tokens beat exhaustive content
2. Attention is finite — U-shaped curve favors beginning/end positions
3. Progressive disclosure — Load information just-in-time (sessionBaseline → keyword match → references)
4. Isolation prevents degradation — Partition work across subagents
5. Measure before optimizing — Know your baseline

IMPORTANT: Sacrifice grammar for concision. Pass these rules to subagents.

Quick Reference

Topic	Reference
Fundamentals	context-fundamentals.md
Degradation	context-degradation.md
Optimization	context-optimization.md
Compression	context-compression.md
Memory	memory-systems.md
Multi-Agent	multi-agent-patterns.md
Evaluation	evaluation.md
Tool/Skill Design	tool-design.md
Pipelines	project-development.md
Runtime Awareness	runtime-awareness.md
OMG Patterns	omg-patterns.md

Key Metrics

• Token utilization: warning 70%, optimize 80%
• Multi-agent cost: ~15x single agent baseline
• Compaction target: 50–70% reduction, <5% quality loss
• Cache hit target: 70%+ for stable workloads

Four-Bucket Strategy

1. Write — Save context externally (scratchpads, files, plans/)
2. Select — Pull only relevant context (module scoping, skill activation)
3. Compress — Reduce tokens while preserving info
4. Isolate — Split across subagents (context partitioning)

Runtime Awareness

OMG hooks auto-inject usage awareness via PostToolUse. Thresholds: 70% WARNING, 90% CRITICAL. See runtime-awareness.md for configuration details.

Anti-Patterns

• Exhaustive context over curated context
• Critical info in middle positions
• No compaction triggers before limits
• Single agent for parallelizable tasks
• Injecting all installed module skills into every subagent
• Duplicating hook logic in AI responses

Security

• Never include secrets, tokens, or credentials in context passed to subagents
• Scope injected context to the minimum needed for the task
• Do not log or persist sensitive tool outputs in plans/ or memory files
• Follow rules/security.md for all context-handling operations