happycapy-ai Prompt Improver
Transform vague prompts into clear, specific, actionable ones for better AI responses.
Workflow
Gather context - Use AskUserQuestion to clarify:
- Target platform (Claude Code, ChatGPT, API, image gen)
- Priority (accuracy, speed, depth, creativity)
- Missing context (technical stack, constraints, examples)
Analyze - Identify what's unclear, missing, or ambiguous
Improve - Apply the framework (see references/framework.md)
Present - Show improved prompt with key changes explained
Refine - Ask if user wants adjustments
AskUserQuestion Templates
Initial clarification:
questions:
- header: "Platform"
question: "What will you use this prompt for?"
options:
- label: "Claude Code"
description: "Coding, file ops, terminal"
- label: "ChatGPT/Claude.ai"
description: "General conversation"
- label: "API/Automation"
description: "Programmatic use"
- label: "Image gen"
description: "DALL-E, Midjourney, etc."
- header: "Priority"
question: "What matters most?"
options:
- label: "Accuracy"
description: "Correctness is critical"
- label: "Speed"
description: "Quick, concise"
- label: "Depth"
description: "Comprehensive"
- label: "Creativity"
description: "Novel approaches"
Post-improvement:
header: "Refine"
question: "Adjust the improved prompt?"
options:
- label: "Looks good"
description: "Use as-is"
- label: "More specific"
description: "Add constraints"
- label: "More concise"
description: "Shorten"
- label: "Different focus"
description: "Change emphasis"
Output Format
## Analysis
[Brief issues/opportunities]
## Improved Prompt
[Ready-to-use prompt]
## Key Changes
- [Change]: [Why]
Quick Mode
If user says "quick improve", skip questions and make reasonable assumptions. Note assumptions made.
Aristotelian Mode (First Principles)
Activated when user says "Aristotelian", "first principles", or "proof-based". Instead of the standard framework, produce a prompt that instructs the receiving LLM to reason from first principles when executing the task.
The prompt-improver does NOT do the Aristotelian reasoning itself. It crafts a prompt that tells the LLM to:
Gather context from user - Ask what system capabilities, tools, and constraints exist. Bake known context (root access, AI model, available tools, domain) directly into the prompt as given axioms.
Embed the reasoning directive - The improved prompt tells the LLM to:
- Identify the atomic, irreducible truths of the task before acting
- Interrogate each truth: "Can this be decomposed further? If removed, does the task break? Does it contradict anything?"
- Discard anything that is not strictly necessary
- Build the solution deductively, where every action traces to a stated axiom
- Verify the result against the axioms at the end
Structure the output prompt with these sections:
REASONING DIRECTIVE: [Instruct the LLM to use first-principles reasoning] GIVEN AXIOMS: [Known truths about system, capabilities, domain -- baked in] TASK: [What to accomplish] METHOD: [Tell LLM to discover task-specific axioms, interrogate them, then build deductively] VERIFICATION: [Tell LLM to check its result against its axioms]
Output format for Aristotelian mode:
## Analysis
[What context was embedded and why]
## Improved Prompt (Aristotelian)
[The complete prompt with reasoning directive, given axioms, task, method, and verification]
## What This Prompt Does
- Tells the LLM to [specific reasoning behavior]
- Bakes in [specific context] so the LLM does not hallucinate it
See references/aristotelian.md for the full methodology and prompt structure.
References
- Framework details: See references/framework.md for the 6-principle improvement framework
- Aristotelian mode: See references/aristotelian.md for the proof-based first principles methodology
- Examples: See references/examples.md for before/after transformations
- Anti-patterns: See references/anti-patterns.md for common issues to fix