oracle

Bundle prompts with project files for AI-driven code review and debugging.

1|Updated Mar 30, 2022
One-click install
npx skills add https://github.com/fcoury/config --skill oracle-fcoury
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/fcoury/config/tree/main/ai/skills/oracle
Command: npx skills add https://github.com/fcoury/config --skill oracle-fcoury

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines code review, debugging, and refactoring by leveraging AI models to analyze code in context with your project files.

Core Features & Use Cases

  • Contextual AI Review: Get AI-driven feedback on code quality, potential bugs, and design patterns.
  • Bundled Analysis: Packages your prompt with relevant files for a comprehensive "one-shot" AI request.
  • Use Case: Debug a complex error by providing the error message, relevant code snippets, and project structure to the Oracle skill for an AI-assisted diagnosis and solution.

Quick Start

Use the oracle skill to preview the summary of a task with all files in the src directory.

Frequently Asked Questions about oracle

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

FAQPage Schema
How do I use AI for code review and debugging with my project files?

AI code review and debugging is performed by bundling your prompt with selected project files to create a comprehensive context for the model to analyze. This one-shot request method provides feedback on code quality, potential bugs, and design patterns.

Can I use this AI code analysis skill with browser-based models or do I need an API?

You can use this AI code analysis skill with either browser-based model interactions or a configured API. Running the skill via API requires explicit user consent beforehand due to potential costs associated with the requests.

Does AI refactoring require packaging my entire project directory?

AI refactoring does not require your entire project directory, as the skill supports configurable file attachments. You can select specific relevant files from directories like src to bundle with your prompt for targeted analysis.

Are there limitations when using an AI assistant for cross-validation of code?

A limitation of using an AI assistant for cross-validation is that API runs incur potential costs, requiring explicit consent before execution. Additionally, the analysis depends on a one-shot bundled request, which limits interactive back-and-forth debugging.