rmyndharis/antigravity-skills

llm-application-dev-prompt-optimize

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

First seen Jan 23, 2026

Installation

$ npx skills add rmyndharis/antigravity-skills --skill llm-application-dev-prompt-optimize

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1.5K
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents antigravity

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,366 B
  • docs SUMMARY.md 243 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 22 installs

SKILL.md

Prompt Optimization

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimization.

Use this skill when

  • Working on prompt optimization tasks or workflows
  • Needing guidance, best practices, or checklists for prompt optimization

Do not use this skill when

  • The task is unrelated to prompt optimization
  • You need a different domain or tool outside this scope

Context

Transform basic instructions into production-ready prompts. Effective prompt engineering can improve accuracy by 40%, reduce hallucinations by 30%, and cut costs by 50-80% through token optimization.

Requirements

$ARGUMENTS

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed patterns and examples.