t1k:context
| Field | Value |
|---|---|
| Module | t1k-base |
| Version | 2.17.3 |
| Effort | medium |
| Tools | — |
Keywords: agent-context, context-window, degradation, injection, optimize, tokens
How to invoke
Section titled “How to invoke”/t1k:context[topic or question]T1K Context Engineering
Section titled “T1K 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 T1K workflows.
When to Activate
Section titled “When to Activate”- Designing/debugging T1K 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
Section titled “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
Section titled “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 |
| T1K Patterns | t1k-patterns.md |
Key Metrics
Section titled “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
Section titled “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
Section titled “Runtime Awareness”T1K hooks auto-inject usage awareness via PostToolUse. Thresholds: 70% WARNING, 90% CRITICAL. See runtime-awareness.md for configuration details.
Anti-Patterns
Section titled “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
Section titled “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.mdfor all context-handling operations