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Optimise Claude

Checked
ralphcrisostomo·vlatest·ai-ml

Optimise Claude refines Claude’s responses for better clarity and relevance, saving you time and ensuring higher-quality outputs.

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 ralphcrisostomo/nuxt-development-skills --skill optimise-claude -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

ralphcrisostomo

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 ralphcrisostomo/nuxt-development-skills at bffd0cdfb2.

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

Commit
bffd0cdfb2
Manifest
skills/optimise-claude/SKILL.md
Scanned
2 files, 153 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, optimise-claude, refines prompts for use with the Claude AI model. It aims to improve response quality and reduce token usage through prompt engineering techniques. The skill is designed to be easily integrated into workflows requiring efficient and high-quality Claude outputs.

When to use it

  • You're experiencing inconsistent or low-quality responses from Claude.
  • You want to minimize the cost of using Claude by reducing token consumption.
  • You need to ensure Claude understands complex instructions clearly.
  • You are iterating on a prompt and want to quickly test different phrasing options.

Key capabilities

  • Prompt refinement for Claude AI model
  • Improved response quality
  • Token usage reduction
  • Easy integration into workflows

Example prompts

  • "Optimise this prompt for Claude: 'Write a short story about a cat.'"
  • "Refine the following instruction to be more effective with Claude: 'Summarize this article.'"
  • “Can you optimise this prompt to reduce token usage? [Long, complex prompt here]”

Tips & gotchas

The skill's effectiveness depends on the initial quality of the prompt. While it can improve existing prompts, a fundamentally flawed instruction may still yield suboptimal results.