Agent skill · data analytics · aiskillstore
context-optimization
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
Why this skill is useful
Provides domain-specific strategies for optimizing context usage that the AI wouldn't reliably generate on its own.
What it needs
About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Context Optimization Techniques Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. Effective optimization increases useful capacity without requiring larger models or longer windows — but only when applied with measurement discipline. The techniques below are ordered by impact and risk. When to Activate Activate this skill when: 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. Designing a structured handoff summary for a long conversation: context-compression. Storing large outputs, plans, or logs as files: filesystem-context. 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/context-optimization--354cb4