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oracle

Bundle natural language prompts with specified files and directories for AI model context.

Updated Aug 27, 2026
Search Tags:#developer tools#prompt engineering#ai#llm#code context#file bundling
One-click install
npx skills add https://github.com/StanH123h/clawdbot-self-use --skill oracle-stanh123h
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/StanH123h/clawdbot-self-use/tree/main/skills/oracle
Command: npx skills add https://github.com/StanH123h/clawdbot-self-use --skill oracle-stanh123h

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of providing AI models with necessary context by bundling prompts with relevant files, enabling more accurate and context-aware responses, especially for complex coding and documentation tasks.

Core Features & Use Cases

  • Context Bundling: Combines natural language prompts with specified files and directories into a single request for an AI model.
  • Engine Flexibility: Supports both API-based and browser-based execution engines for different model interactions.
  • Use Case: When debugging a complex issue in a large codebase, use Oracle to bundle your error message, relevant source files, and configuration files into a single prompt for an AI to analyze and suggest a solution.

Quick Start

Use the oracle skill to preview the token count for a task involving all files in the src directory, excluding test files.

Frequently Asked Questions about oracle

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

FAQPage Schema
How do I bundle files and prompts for AI context?▼

To bundle files and prompts for AI context, combine natural language prompts with specified files and directories using explicit inclusion and exclusion patterns, creating a single comprehensive request for the model.

Does file bundling work with API and browser based LLMs?▼

Yes, file bundling supports both API-based and browser-based execution engines, allowing you to interact with different LLMs and provide comprehensive project context regardless of the model interface.

What is the best way to provide code context to an LLM for debugging?▼

The best way to provide code context for debugging is bundling error messages with relevant source files and configuration files into a single prompt, enabling the AI to analyze the comprehensive project context and suggest solutions.

Can I exclude test files when bundling source code for AI analysis?▼

Yes, you can exclude test files when bundling source code by specifying explicit file inclusion and exclusion patterns, allowing you to precisely control which directories and files are sent to the AI model.

Does prompt bundling support session management for long-running tasks?▼

Yes, prompt bundling supports session management for long-running tasks, allowing you to maintain context and facilitate detailed analysis and problem-solving across extended interactions with the AI model.

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