| name | context-optimization |
| description | Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | productivity |
| category | developer-experience |
| risk | unknown |
| source | community |
| tags | ["skill","productivity","developer-experience","context","optimization"] |
Context Optimization Techniques
Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.
When to Use
Activate this skill when:
- Context limits constrain task complexity
- Optimizing for cost reduction (fewer tokens = lower costs)
- Reducing latency for long conversations
- Implementing long-running agent systems
- Needing to handle larger documents or conversations
- Building production systems at scale
Core Concepts
Context optimization extends effective capacity through four primary strategies: compaction (summarizing context near limits), observation masking (replacing verbose outputs with references), KV-cache optimization (reusing cached computations), and context partitioning (splitting work across isolated contexts).
The key insight is that context quality matters more than quantity. Optimization preserves signal while reducing noise. The art lies in selecting what to keep versus what to discard, and when to apply each technique.
Detailed Topics
Compaction Strategies
What is Compaction
Compaction is the practice of summarizing context contents when approaching limits, then reinitializing a new context window with the summary. This distills the contents of a context window in a high-fidelity manner, enabling the agent to continue with minimal performance degradation.
Compaction typically serves as the first lever in context optimization. The art lies in selecting what to keep versus what to discard.
Compaction Implementation
Compaction works by identifying sections that can be compressed, generating summaries that capture essential points, and replacing full content with summaries. Priority for compression goes to tool outputs (replace with summaries), old turns (summarize early conversation), retrieved docs (summarize if recent versions exist), and never compress system prompt.
Summary Generation
Effective summaries preserve different elements depending on message type:
Tool outputs: Preserve key findings, metrics, and conclusions. Remove verbose raw output.
Conversational turns: Preserve key decisions, commitments, and context shifts. Remove filler and back-and-forth.
Retrieved documents: Preserve key facts and claims. Remove supporting evidence and elaboration.
Observation Masking
The Observation Problem
Tool outputs can comprise 80%+ of token usage in agent trajectories. Much of this is verbose output that has already served its purpose. Once an agent has used a tool output to make a decision, keeping the full output provides diminishing value while consuming significant context.
