agentleFS
Sign inStart free
typvtypv/nest-turbo-starter/.claude/skills/context-engineering/SKILL.md

context-engineering

Skill224 starsChanged 3 months agoMITView on GitHub

Check context usage limits, monitor time remaining, optimize token consumption, debug context failures. Use when asking about context percentage, rate limits, usage warnings, context optimization, agent architectures, memory systems.

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 114 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Ensure token efficiency while maintaining high quality.

    line 34
  2. Distinctive:

    No compaction triggers before limits

    line 71
  3. Distinctive:

    Place critical info at beginning/end of context

    line 77
  4. Distinctive:

    Use sub-agents for context isolation, not role-play

    line 79
  5. Distinctive:

    Start minimal, add complexity only when proven necessary

    line 84

“Common” is measured: kept by at least 6 in 10 of the 114 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Context Engineering

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

When to Activate

  • Designing/debugging agent systems
  • Context limits constrain performance
  • Optimizing cost/latency
  • Building multi-agent coordination
  • Implementing memory systems
  • Evaluating agent performance
  • Developing LLM-powered pipelines

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
  4. Isolation prevents degradation - Partition work across sub-agents
  5. Measure before optimizing - Know your baseline

IMPORTANT:

  • Sacrifice grammar for the sake of concision.
  • Ensure token efficiency while maintaining high quality.
  • Pass these rules to subagents.

Quick Reference

TopicWhen to UseReference
FundamentalsUnderstanding context anatomy, attention mechanicscontext-fundamentals.md
DegradationDebugging failures, lost-in-middle, poisoningcontext-degradation.md
OptimizationCompaction, masking, caching, partitioningcontext-optimization.md
CompressionLong sessions, summarization strategiescontext-compression.md
MemoryCross-session persistence, knowledge graphsmemory-systems.md
Multi-AgentCoordination patterns, context isolationmulti-agent-patterns.md
EvaluationTesting agents, LLM-as-Judge, metricsevaluation.md
Tool DesignTool consolidation, description engineeringtool-design.md
PipelinesProject development, batch processingproject-development.md
Runtime AwarenessUsage limits, context window monitoringruntime-awareness.md

Key Metrics

  • Token utilization: Warning at 70%, trigger optimization at 80%
  • Token variance: Explains 80% of agent performance variance
  • 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)
  2. Select: Pull only relevant context (retrieval, filtering)
  3. Compress: Reduce tokens while preserving info (summarization)
  4. Isolate: Split across sub-agents (partitioning)

Anti-Patterns

  • Exhaustive context over curated context
  • Critical info in middle positions
  • No compaction triggers before limits
  • Single agent for parallelizable tasks
  • Tools without clear descriptions

Guidelines

  1. Place critical info at beginning/end of context
  2. Implement compaction at 70-80% utilization
  3. Use sub-agents for context isolation, not role-play
  4. Design tools with 4-question framework (what, when, inputs, returns)
  5. Optimize for tokens-per-task, not tokens-per-request
  6. Validate with probe-based evaluation
  7. Monitor KV-cache hit rates in production
  8. Start minimal, add complexity only when proven necessary

Runtime Awareness

The system automatically injects usage awareness via PostToolUse hook:

<usage-awareness>
Claude Usage Limits: 5h=45%, 7d=32%
Context Window Usage: 67%
</usage-awareness>

Thresholds:

  • 70%: WARNING - consider optimization/compaction
  • 90%: CRITICAL - immediate action needed

Data Sources:

  • Usage limits: Anthropic OAuth API (https://api.anthropic.com/api/oauth/usage)
  • Context window: Statusline temp file (/tmp/ck-context-{session_id}.json)

Scripts

  • context_analyzer.py - Context health analysis, degradation detection
  • compression_evaluator.py - Compression quality evaluation

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

Reviews can't be read right now.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free