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ndpvt-webndpvt-web/prompt-improver/SKILL.md

prompt-improver

Skill86 starsChanged 6 months agoMITView on GitHub

Optimize prompts for better AI responses. Use when user asks to improve a prompt, refine a prompt, make a prompt better, optimize prompting, "Aristotelian", "first principles", or "proof-based" , review their prompt, or says "/improve-prompt". Transforms vague requests into clear, specific, actionable, logic-based prompts.

11 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Gather context: - Use AskUserQuestion as many times as needed to resolve any doubts or assumptions

    line 12
  2. Distinctive:

    Refine: - Ask if user wants adjustments

    line 20
  3. Distinctive:

    Always actived. produce a prompt that instructs the receiving LLM to reason from first principles when executing the task.

    line 24
  4. Distinctive:

    Gather context from user: - Ask what system capabilities, tools, and constraints exist.

    line 28
  5. Distinctive:

    Verify the result against the axioms at the end

    line 35

“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Prompt Improver

Transform vague prompts into clear, specific, actionable ones for better AI responses.

Workflow

  1. Gather context - Use AskUserQuestion as many times as needed to resolve any doubts or assumptions
  2. Analyze - Identify what's unclear, missing, or ambiguous
  3. Improve - Apply the Aristotelian framework
  4. Present - Show improved prompt with key changes explained
  5. Refine - Ask if user wants adjustments

Aristotelian Mode (First Principles)

Always actived. 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:

  1. 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.
  2. 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
  3. 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

  • 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
  • Framework details: See references/framework.md for the 6-principle improvement framework

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