context-optimization

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---
name: context-optimization
description: >-
  Improve context efficiency through context budgeting, observation masking,
  prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval
  scoping, and extending effective context capacity without lowering answer
  quality. Use when token costs or context budgets constrain a task, tool
  outputs are verbose, cache hit rate is low, or context must be partitioned
  across agents.
metadata:
  version: "2.1.0"
  source: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/blob/main/skills/context-optimization/SKILL.md
  upstream_repo: muratcankoylan/Agent-Skills-for-Context-Engineering
  upstream_ref: main
  upstream_commit: cbc2c978133d
  last_synced: "2026-06-12"
  license: MIT
  tags: "context, optimization, agents"
---
# Context Optimization Techniques

Context optimization extends the effective capacity of limited context windows through compression, masking, caching, and partitioning, applied with measurement discipline. The techniques below are ordered by impact and risk.

## When to Activate

- Context budgets or token costs constrain task complexity
- Observation masking can replace verbose tool outputs with retrievable references
- Prefix or KV-cache hit rate needs improvement
- Retrieval scoping can reduce irrelevant loaded context
- Context partitioning can extend effective capacity across agents
- Budget triggers are needed for masking, compaction, or partitioning

Do not activate this skill for adjacent work owned by other skills:

- Explaining why attention or context windows behave this way: `context-fundamentals`.
- Diagnosing active lost-in-middle, poisoning, distraction, confusion, or clash: `context-degradation`.

## Core Concepts

Apply four primary strategies in this priority order:

1. **KV-cache optimization** — Reorder and stabilize prompt structure so the inference engine reuses cached Key/Value tensors. This is the cheapest optimization when the runtime supports prefix caching: low quality risk, immediate cost and latency savings. Apply it first when stable prefixes exist.

2. **Observation masking** — Replace verbose tool outputs with compact references once their purpose has been served. Tool outputs can dominate agent trajectories (claim-context-optimization-tool-output-dominance), so masking often yields the largest capacity gains. The original content remains retrievable if needed downstream.

3. **Compaction** — Summarize accumulated context when utilization exceeds 70%, then reinitialize with the summary. This distills the window's contents while preserving task-critical state. Compaction is lossy — apply it after masking has already removed the low-value bulk.

4. **Context partitioning** — Split work across sub-agents with isolated contexts when a single window cannot hold the full problem. Each sub-agent operates in a clean context focused on its subtask. Reserve this for tasks where estimated context exceeds 60% of the window limit, because coordination overhead is real.

The governing principle: context quality matters more than quantity. Every optimization preserves signal while reducing noise. Measure before optimizing, then measure the optimization's effect.

## Detailed Topics

### Compaction Strategies

Trigger compaction when context utilization exceeds 70%: summarize the current context, then reinitialize with the summary. This distills the window's contents in a high-fidelity manner, enabling continuation with minimal performance degradation. Prioritize compressing tool outputs first (they consume 80%+ of tokens), then old conversation turns, then retrieved documents. Never compress the system prompt — it anchors model behavior and its removal causes unpredictable degradation.

Preserve different elements by message type:

- **Tool outputs**: Extract key findings, metrics, error codes, and conclusions. Strip verbose raw output, stack traces (unless debugging is ongoing), and boilerplate headers.
- **Conv