Testing prompts before you use them saves time and prevents mistakes. This method uses a three step cycle called RED-GREEN-REFACTOR. You first watch an agent fail without the prompt. Then you write the prompt to fix the failures. Finally you improve the prompt to handle tricky cases.
Using a subagent to test your prompt gives you a clean starting point. It has no past conversations to bias the results. You can run many tests at once and see exactly how the prompt changes behavior.
This approach works for commands, instructions, skills, and even production prompts. It helps anyone who writes prompts for AI agents to make them reliable and robust.
Global
mkdir -p ~/.claude/skills/test-promptProject
mkdir -p .claude/skills/test-promptSource Repository
Cavemanjuliusbrussee/caveman
Talk like a smart caveman to save tokens without losing technical accuracy
Cavemanmattpocock/skills
Caveman mode for AI cuts token count 75% without losing technical accuracy
Enhance Promptgoogle-labs-code/stitch-skills
Turn vague UI ideas into clear, polished prompts with design system consistency
Humanizer Zhop7418/humanizer-zh
Remove AI writing patterns to make your text sound natural and human
Gws Modelarmorgoogleworkspace/cli
Filter harmful user content automatically with Google Model Armor for safety
Prompt Engineering Patternswshobson/agents
Master advanced prompt engineering patterns to get reliable and powerful AI outputs every time
Context Engineeringaddyosmani/agent-skills
Optimize your agent's context to improve output quality and focus
Writetw93/waza
Rewrite your prose to sound human and natural every time