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Context Optimization

Checked
eyadsibai·vlatest·ai-ml

eyadsibai's eyadsibai-context-optimization refines prompts for improved AI response accuracy and relevance based on nuanced contextual understanding.

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 eyadsibai/ltk --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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-core/skills/context-optimization/SKILL.md
Scanned
1 file, 122 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 manage and refine their context to improve response accuracy and relevance while maximizing the amount of information they can process. It achieves this through techniques like compaction (summarization), observation masking, KV-cache optimization, and context partitioning. These strategies can effectively increase usable context capacity by 2-3x without needing larger language models. The skill focuses on intelligently reducing token usage within the agent's context window.

When to use it

  • When approaching context limits: If your AI agent is consistently hitting its token limit, this skill can help extend its effective capacity.
  • With workloads dominated by tool outputs: Observation masking is particularly useful when tools are generating verbose responses that consume a large portion of the available tokens.
  • For complex multi-task scenarios: Context partitioning allows breaking down large tasks into smaller, more manageable sub-tasks with isolated contexts.
  • When experiencing quality degradation: If response quality declines due to context overload, this skill can help identify and address the root cause.

Key capabilities

  • Compaction: Summarizes message history, tool outputs, and retrieved documents while prioritizing key information.
  • Observation Masking: Replaces lengthy tool outputs with references and summaries.
  • KV-Cache Optimization: Caches frequently used context elements (system prompts, tool definitions) for faster retrieval.
  • Context Partitioning: Splits complex tasks into sub-agents each with their own focused context.
  • Budget Management: Allows setting token budgets for different context components to control usage and trigger optimization strategies.

Example prompts

  • "Agent, you're approaching your context limit. Please optimize the current conversation."
  • "Agent, summarize the key findings from the tool outputs in the last turn."
  • "Agent, can you partition this research task into smaller sub-tasks?"

Tips & gotchas

  • Prioritize stability: When using KV-Cache Optimization, ensure consistent formatting and stable structure across sessions to maintain cache effectiveness.
  • Avoid compressing system prompts: The system prompt should never be compressed as it contains critical instructions.
  • Monitor utilization: Pay attention to context token usage; start monitoring around 70% and apply compaction strategies when exceeding 80%.