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improve

Diagnose root causes in agent prompts and suggest universal improvements.

Updated Mar 24, 2026
Search Tags:#debugging#root cause#prompt engineering#prompt templates#prompt improvement
One-click install
npx skills add https://github.com/RintaroMasaoka/theoretical-physics-agents --skill improve-rintaromasaoka
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: improve
Source: https://github.com/RintaroMasaoka/theoretical-physics-agents/tree/main/.claude/skills/improve
Command: npx skills add https://github.com/RintaroMasaoka/theoretical-physics-agents --skill improve-rintaromasaoka

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users identify and fix underlying issues in agent prompts and behaviors, avoiding superficial patches that produce recurring problems.

Core Features & Use Cases

  • Root Cause Diagnosis: Deeply analyze complaints to identify fundamental causes rather than surface symptoms.
  • Prompt Refinement: Suggest universal improvements to prompt templates ensuring scalability and robustness.
  • Use Case: When an AI repeatedly fails in specific scenarios, use this Skill to diagnose the core issue, draft an improved prompt, and verify coherence before deployment.

Quick Start

Ask the AI to analyze an agent prompt and suggest improvements to resolve persistent issues.

Frequently Asked Questions about improve

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

FAQPage Schema
How do I find the root cause of persistent AI agent failures?▼

Root cause diagnosis deeply analyzes agent complaints and behaviors to identify fundamental issues rather than surface symptoms. This approach prevents superficial patches that produce recurring problems in specific scenarios.

How do I improve prompt templates to make AI agents more robust?▼

Prompt refinement suggests universal improvements to prompt templates ensuring scalability and robustness. You diagnose the core issue, draft an improved prompt, and verify coherence before deployment.

Why does my AI agent keep failing in specific scenarios despite prompt updates?▼

Superficial prompt patches often cause recurring failures because they mask the underlying issue. Root cause analysis identifies the fundamental problem in the prompt engineering logic to ensure permanent resolution.

What is the best way to debug an AI prompt that produces inconsistent outputs?▼

In-depth diagnosis debugs the AI prompt by analyzing the root cause of inconsistencies and suggesting targeted prompt improvements. This enhances agent robustness across diverse scenarios before deployment.

When should I use root cause analysis instead of patching a prompt template?▼

Use root cause analysis when an AI repeatedly fails in specific scenarios, as patching only addresses surface symptoms. This method ensures prompt templates are universally improved for long-term scalability.

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