Agent Skills for Context Engineering is a collection of reusable instructions that teach AI agents how to manage their context, coordinate multi-agent systems, and evaluate behavior. Developers use it when building or debugging production agent systems, and the catalogue entries are skills, agents, instructions, and a plugin from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-optimizationgit clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-EngineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/muratcankoylan/agent-skills-for-context-engineering/context-optimization)<a href="https://agentmods.dev/skills/muratcankoylan/agent-skills-for-context-engineering/context-optimization"><img src="https://agentmods.dev/badge/skills/muratcankoylan/agent-skills-for-context-engineering/context-optimization.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.03030 |
| Opus 5 | $0.00023 | $0.01515 |
| Sonnet 5 | $0.00009 | $0.00606 |
| Haiku 4.5 | $0.00005 | $0.00303 |
Grade A, and why
context-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- context-optimization — 100% identical, 0 lines differ
- context-optimization — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
-
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.
-
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.
-
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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 220 lines · 47 tokens per session scan A 8cecc30872ec
context-optimization is a skill published in the GitHub repository muratcankoylan/Agent-Skills-for-Context-Engineering (17,927 stars, last pushed 18d ago), licensed MIT. It adds 47 tokens to every session and 3,030 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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