| 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
- Context quality > quantity — High-signal tokens beat exhaustive content
- Attention is finite — U-shaped curve favors beginning/end positions
- Progressive disclosure — Load information just-in-time (sessionBaseline → keyword match → references)
- Isolation prevents degradation — Partition work across subagents
- Measure before optimizing — Know your baseline
IMPORTANT: Sacrifice grammar for concision. Pass these rules to subagents.
Quick Reference
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
- Write — Save context externally (scratchpads, files,
plans/)
- Select — Pull only relevant context (module scoping, skill activation)
- Compress — Reduce tokens while preserving info
- 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
