oberprompt

Audit and improve prompts using FIX and REVIEW modes with validation checks.

60|3|Updated Dec 18, 2025
One-click install
npx skills add https://github.com/ryanthedev/oberskills --skill oberprompt
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: oberprompt
Source: https://github.com/ryanthedev/oberskills/tree/main/skills/oberprompt
Command: npx skills add https://github.com/ryanthedev/oberskills --skill oberprompt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompts often behave unpredictably across models and domains; oberprompt provides a structured approach to improve reliability and safety through two modes and a rigorous workflow.

Core Features & Use Cases

  • Dual-mode prompting: FIX for silent improvements and REVIEW for verbose analysis.
  • Emergency triage and validation: a step-by-step diagnostic flow before shipping prompts.
  • Guardrails and anti-pattern guidance: standardized checks to prevent common prompting pitfalls.
  • Use Case examples: prompt creation, auditing for code generation, data analysis, and research assistants.

Quick Start

Choose a mode (FIX or REVIEW), provide the prompt, and follow the guided triage and validation steps.

Frequently Asked Questions about oberprompt

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

FAQPage Schema
How do I reduce flaky outputs from LLM prompts?

To reduce flaky LLM prompt outputs, apply a structured two-mode workflow using FIX for silent improvements and REVIEW for verbose analysis, followed by a mandatory validation checklist before shipping.

What is the best way to audit prompts for safe and reliable behavior?

The best way to audit prompts for safe behavior is to run them through a verbose REVIEW mode that enforces guardrails, checks for anti-patterns, and applies a rigorous step-by-step triage sequence.

How do I validate code generation prompts before deployment?

Validate code generation prompts by applying an emergency triage sequence and a mandatory validation checklist to catch common prompting pitfalls and ensure reliable behavior across coding tasks.

Can I use this prompt engineering workflow for data analytics and research assistants?

Yes, you can use this dual-mode prompt engineering workflow for data analytics and research assistants, as it provides standardized checks to prevent common prompting pitfalls across these specific domains.

Why does my prompt behave unpredictably across different models?

Prompts behave unpredictably across models due to a lack of structured guardrails and validation, which can be resolved by enforcing a dual-mode workflow that standardizes checks and prevents common anti-patterns.

What is the difference between FIX and REVIEW modes for prompt analysis?

The difference is that FIX mode applies silent improvements to prompts, whereas REVIEW mode provides verbose analysis, both guided by an emergency triage sequence and validation checklist to ensure reliability.