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Context Optimization

Checked
guanyang·vlatest·other·MIT

Guanyang's context optimization intelligently refines conversation history for improved AI response accuracy and relevance.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add guanyang/open-agent-hub --skill context-optimization -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

guanyang

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned guanyang/antigravity-skills at 0d6f1e7d3f.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
0d6f1e7d3f
Manifest
skills/context-optimization/SKILL.md
Scanned
3 files, 1,038 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill, Context Optimization, helps AI agents work effectively even with limited context windows. It achieves this by strategically compressing, masking, caching, and partitioning conversation history. The goal is to improve response accuracy and relevance without needing larger models or longer context windows – ultimately increasing efficiency and reducing costs.

When to use it

  • When task complexity is constrained by context limits.
  • To reduce the cost of running an AI agent (fewer tokens used).
  • For long-running agent systems where latency needs to be minimized.
  • When handling larger documents or conversations.
  • When building production systems at scale.

Key capabilities

  • KV-cache optimization: Reorders and stabilizes prompts for improved inference engine efficiency.
  • Observation masking: Replaces verbose tool outputs with compact references, preserving retrievability.
  • Compaction: Summarizes context when utilization exceeds 70%, prioritizing compression of tool outputs, old conversation turns, and retrieved documents (excluding the system prompt).
  • Context partitioning: Splits work across sub-agents with isolated contexts for tasks exceeding a certain size threshold.

Example prompts

  • "Optimize the current conversation history to reduce token usage."
  • "Apply context masking techniques to shorten this exchange."
  • "Can you summarize the key points from our previous discussion?"

Tips & gotchas

  • Always apply KV-cache optimization first, unconditionally.
  • Context quality is more important than quantity; measure before and after optimizations.
  • Avoid compressing the system prompt as it anchors model behavior.