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Context Optimization Techniques

Teaches an agent to cut token use and extend usable context via masking, caching, compaction and partitioning

Source Download skill
Repository
muratcankoylan/Agent-Skills-for-Context-Engineering
Stars
18,282
Licence
MIT
Files
3 files

What it does

When installed, the agent follows a set of guidelines for managing its limited working memory on long tasks: it replaces bulky tool outputs with short references, orders prompts so cached content can be reused, summarizes context when it gets too full, and suggests splitting work across sub-agents when needed. The skill is written as guidance with thresholds (for example, compaction around 70% utilization) and includes a Python utility file with helper functions for estimating tokens, categorizing messages, and tracking masked observations and cache metrics.

Great for

  • Developers building long-running agents that hit context or token-cost limits
  • Engineers tuning prompt structure for prefix or KV-cache hit rates
  • Teams deciding whether a task needs sub-agent partitioning

Skip it if

  • Simple, short chats where context limits never matter, since the overhead adds complexity without benefit
  • Situations needing exact token counts or real cache metrics: the bundled script uses a rough 4-characters-per-token estimate and illustrative cache numbers, which its own notes say must be replaced in production
  • Tasks where lossy summarization is unacceptable, such as legal or compliance records, because compaction can drop critical details

Try asking

  • “Our agent runs out of context halfway through long debugging sessions. How should we mask old tool outputs?”
  • “Review my prompt structure and tell me how to improve prefix cache hit rate.”
  • “Decide whether this multi-file refactor should be split across sub-agents.”
  • “Set up a context budget with triggers for compaction and masking in my agent loop.”
Step-by-step guide: how to use Context Optimization TechniquesInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    context-optimization.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip context-optimization.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip context-optimization.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Treat the thresholds (70% compaction, 60% partitioning, 70% cache hit target) as starting points and measure your own results before and after applying them.
  • Replace the script's character-based token estimate with your model's real tokenizer before relying on its budget numbers.
  • Apply masking before compaction, since compaction is lossy and masking removes low-value bulk first.

What's inside

  • references/optimization_techniques.md
  • scripts/compaction.py
  • SKILL.md

Redistributed unmodified from muratcankoylan/Agent-Skills-for-Context-Engineering under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.