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oracle

Bundle prompts with files and directories for AI model processing.

Updated Jan 26, 2026
Search Tags:#developer tools#debugging#ai#llm#code context#prompting
One-click install
npx skills add https://github.com/coltonbatts/Loubot --skill oracle-coltonbatts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/coltonbatts/Loubot/tree/main/skills/oracle
Command: npx skills add https://github.com/coltonbatts/Loubot --skill oracle-coltonbatts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of providing relevant project context to AI models by bundling prompts with selected files, enabling more accurate and context-aware responses.

Core Features & Use Cases

  • Contextual Prompting: Bundles prompts with specified files for AI processing.
  • File Attachment: Supports including files, directories, and globs with exclusion patterns.
  • Engine Flexibility: Offers both API and browser-based execution modes.
  • Session Management: Allows reattaching to long-running or detached sessions.
  • Use Case: When debugging a complex issue, use Oracle to bundle your prompt, error logs, and relevant source code files to get targeted assistance from an AI model.

Quick Start

Use the oracle skill to preview the token count for a task using 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 debugging?▼

To provide code context for debugging, you can bundle prompts with specified files and directories using contextual prompting. This ensures the AI model receives relevant project context, enabling more accurate and targeted assistance.

Can I include specific directories and exclude certain files when prompting an LLM?▼

Yes, you can include specific directories and files when prompting an LLM by using file attachment features. This supports globs and exclusion patterns to precisely control which project files are bundled with your prompt.

What is the best way to manage long-running AI sessions for code analysis?▼

The best way to manage long-running AI sessions for code analysis is using session management features. This allows you to reattach to detached or active sessions, ensuring continuous contextualized AI responses without losing previous project context.

Does this contextual prompting tool work with both API and browser engines?▼

Yes, this contextual prompting tool works with both API and browser-based execution modes. This engine flexibility allows you to bundle project files and prompts for AI processing across different execution environments.

How do I preview token counts before sending files to an AI model?▼

You can preview token counts before sending files to an AI model by bundling your prompt with selected project files. This contextual prompting approach ensures you know the context size before requesting AI-assisted development or debugging support.

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