oracle

Name: oracle
Availability: InStock
Author: luisvelmarketer

Bundle prompts with files for AI code analysis across engines.

1|1|Updated Mar 3, 2026

Search Tags:#developer tools #code analysis #debugging #context management #prompt engineering #ai assistant

Ready to run

npx skills add https://github.com/LuisvelMarketer/genome --skill oracle-luisvelmarketer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/LuisvelMarketer/genome/tree/main/skills/oracle
Command: npx skills add https://github.com/LuisvelMarketer/genome --skill oracle-luisvelmarketer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of providing code context to AI models, enabling them to understand and analyze codebases more effectively, and provides a fallback for manual prompt engineering.

Core Features & Use Cases

Context Bundling: Packages prompts with selected files for AI analysis.
Multi-Engine Support: Works with both API-based and browser-based AI engines.
Session Management: Allows reattachment to long-running or detached analysis sessions.
Use Case: When debugging a complex issue in a large codebase, use Oracle to bundle relevant files and a detailed problem description to get AI-driven insights and potential solutions.

Quick Start

Use the oracle skill to preview the token count for a task involving 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 provide code context to an AI model for codebase analysis?▼

To provide code context for AI codebase analysis, you can bundle relevant files and prompts together. This approach ensures the AI engine has the necessary context to understand, debug, or refactor your code effectively.

Can I use browser automation instead of an API for AI code analysis?▼

Yes, AI code analysis supports both API-based and browser-based AI engines. You can select your preferred engine to execute tasks like code understanding and debugging across different execution contexts.

How do I manage long-running AI debugging sessions?▼

You can manage long-running AI debugging sessions through session reattachment. This feature allows you to reconnect to detached analysis sessions and review ongoing code analysis outputs.

What is the best way to preview token counts for code files before prompting an AI?▼

The best way to preview token counts for code files is by using specific CLI commands to evaluate a task. This helps you manage context limits before bundling prompts and files for the AI engine.

How do I include or exclude specific files when bundling prompts for AI code analysis?▼

When bundling prompts for AI code analysis, you can use file inclusion and exclusion options via CLI commands. This ensures only relevant files are packaged for the selected AI engine.

Does AI code analysis work with detached sessions for complex refactoring tasks?▼

Yes, AI code analysis works with detached sessions for complex refactoring tasks. You can reattach to these long-running sessions to retrieve AI-driven insights and potential solutions for your codebase.