context-optimization
An agent skill by muratcankoylan, from muratcankoylan/Agent-Skills-for-Context-Engineering. Tags: automation, cli, developer-tools, performance, strategy.
What it does
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.
Install
With the skills CLI, which installs into Claude Code, Codex, Cursor and other agents:
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-optimizationOr copy the skill folder into Claude Code's skills directory by hand (~/.claude/skills for every project, or .claude/skills inside one):
git clone --depth 1 https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
cp -r Agent-Skills-for-Context-Engineering/skills/context-optimization ~/.claude/skills/context-optimizationSafety box score
Grade A, minimal risk: 9 of 100 risk points, rated 2026-10-19. The grade rates what the skill and its scripts can reach on the machine of whoever installs it, across eight categories from shell execution to secrets access. How the score works.
This skill is a local context-management utility: it summarizes, masks, budgets, and cache-stabilizes prompts, and ships one runnable Python demo script. It does not reach the network, filesystem, or privilege boundaries, but it can change what the agent keeps in context and how prompts are shaped, which affects transparency and behavior.
| Category | Level | Why |
|---|---|---|
| Code & shell execution | 1 (low) | `scripts/compaction.py` is a runnable Python module with a demo entrypoint (`if __name__ == "__main__":`), but it only does local string processing and prints results. |
| Network access | 0 (none) | No code downloads data or sends it to remote hosts; the repo contains only markdown and a local Python utility. |
| Filesystem reach | 0 (none) | The skill does not read, write, or delete project files, home dotfiles, or system paths; its `ObservationStore` keeps content in memory only. |
| Secrets & credentials | 0 (none) | It mentions session IDs and prompt metadata, but it does not access tokens, passwords, `.env` files, keychains, browser sessions, or CLI logins. |
| Privilege & persistence | 0 (none) | There is no use of sudo, service registration, global installs, or OS/shell configuration changes. |
| Instruction hijack surface | 1 (low) | The docs repeatedly instruct how to shape system prompts and stable prefixes (for example, `Never compress the system prompt` and `Design prompts for cache stability`), which is prompt-influencing guidance but not an instruction to bypass safeguards. |
| Irreversible actions | 0 (none) | It does not push, publish, send messages, deploy, pay, book, or delete anything; it only advises internal context-management steps. |
| Transparency | 0 (none) | All shipped files are readable markdown or Python; there are no binaries, minified blobs, base64 payloads, or remote installers. |
Before installing
- Read `scripts/compaction.py` and confirm you are comfortable with an in-memory store that can retain full tool outputs for later retrieval.
- Check whether your agent framework already handles prompt stability; this skill assumes you may rewrite prefixes and compact history.
- Run the demo script in a scratch environment first to see how its summarization and masking behave on your own text.
Rated 2026-10-19 by an automated review of 3 files from the skill's repository.
Source
- Repository
- muratcankoylan/Agent-Skills-for-Context-Engineering (all skills from this repository)
- Path
- skills/context-optimization/SKILL.md
- Branch
- main
- Updated
- 2026-09-19
Related skills
- context-compression — This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization.
- context-degradation — This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion.
- latent-briefing — This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context".
- reasoning-trace-optimizer — Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures.
- self-improvement-loops — This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops.
- cpp-pro — Writes, optimizes, and debugs C++ applications using modern C++20/23 features, template metaprogramming, and high-performance systems techniques.