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prompt-self-improvement

Analyze AI assistant prompts and propose evidence-based improvements.

1|Updated Jan 2, 2013
Search Tags:#prompt optimization#AI#repository maintenance#evidence-based analysis#prompt diagnosis
One-click install
npx skills add https://github.com/mkiken/SettingFiles --skill prompt-self-improvement-mkiken
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-self-improvement
Source: https://github.com/mkiken/SettingFiles/tree/main/ai/common/skills/prompt-self-improvement
Command: npx skills add https://github.com/mkiken/SettingFiles --skill prompt-self-improvement-mkiken

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of improving AI assistant prompts by providing evidence-based diagnosis and opportunistic improvement proposals.

Core Features & Use Cases

  • Evidence-Based Analysis: Diagnose AI prompt issues with evidence from user corrections, failed outputs, and manual workflows.
  • Opportunistic Improvement Proposals: Surface improvement proposals during normal usage.
  • Use Case: When you want to enhance the prompts used by your AI assistant, this Skill can help you identify areas for improvement and provide a validation plan.

Quick Start

Run the prompt-self-improvement skill to analyze and improve the AI assistant prompts in your repository.

Frequently Asked Questions about prompt-self-improvement

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize AI assistant prompts using evidence from failed outputs?▼

You can optimize AI assistant prompts by running evidence-based analysis that diagnoses issues using user corrections, failed outputs, and manual workflows to generate targeted improvement proposals.

What is evidence-based prompt diagnosis and how does it work?▼

Evidence-based prompt diagnosis analyzes AI prompt issues by extracting concrete evidence from user corrections and failed outputs, then surfacing opportunistic improvement proposals during normal repository usage.

How do I improve AI prompts during normal repository maintenance?▼

You can improve AI prompts during repository maintenance by applying opportunistic analysis that surfaces improvement proposals and validation plans directly within your existing workflow.

Do I need access to repository source files to run prompt analysis?▼

Yes, you need access to repository source files and the ability to execute scripts, because the prompt analysis requires examining source files to diagnose issues and generate evidence-based improvement proposals.

What's the best way to diagnose AI prompt issues from user corrections?▼

The best way to diagnose AI prompt issues from user corrections is to run evidence-based analysis that validates problems against failed outputs and manual workflows, producing a concrete improvement and validation plan.

Can I validate prompt improvements before applying them to my repository?▼

Yes, evidence-based prompt analysis provides a validation plan alongside improvement proposals, allowing you to verify proposed prompt changes against actual user corrections and failed outputs before applying them.

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