oracle

Name: oracle
Availability: InStock
Author: softweek

Bundle prompts with selected files and directories for AI processing.

Updated Mar 7, 2026

Search Tags:#developer tools #prompt engineering #cli #ai #llm #code context

Ready to run

npx skills add https://github.com/softweek/openclaw --skill oracle-softweek

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/softweek/openclaw/tree/main/publish_output/skills/oracle
Command: npx skills add https://github.com/softweek/openclaw --skill oracle-softweek

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of providing relevant code and documentation context to AI models, enabling them to understand and act upon complex projects more effectively.

Core Features & Use Cases

Prompt + File Bundling: Packages prompts with selected files for AI processing, ensuring models have necessary context.
Contextual AI Interaction: Facilitates AI-driven tasks like code generation, debugging, and analysis by providing rich project information.
Use Case: When asking an AI to refactor a specific module, use this Skill to bundle the module's code, its tests, and relevant configuration files, ensuring the AI understands the module's place within the larger project.

Quick Start

Use the oracle skill to preview the payload and 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 bundle repository files with prompts for AI code generation?▼

To bundle repository files with prompts for AI code generation, package selected files and directories into a single request using file inclusion patterns. This ensures AI models process the full repository context needed for accurate code generation and debugging.

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

Providing repository context to an LLM for debugging requires bundling the target code, its tests, and configuration files into a single prompt. This method ensures the AI understands the module's place within the larger project architecture.

Can I use file inclusion patterns to select specific code directories for AI prompts?▼

Yes, you can use file inclusion and exclusion patterns to select specific code directories for AI prompts. This filters the repository context, allowing the AI to process only the relevant files needed for your specific prompt engineering task.

How do I check the token count before sending a prompt with repo files to an AI model?▼

You check the token count by previewing the payload before sending the prompt with repo files to an AI model. This feature allows you to manage the context size and ensure the bundled request fits within the LLM's limits.

Does prompt templating work with both API and browser-based AI interactions?▼

Yes, prompt templating works with both API and browser-based AI interactions. This allows you to standardize how repository context and prompts are bundled across different platforms for consistent code analysis and generation.

Why do I need to include configuration files when asking an AI to refactor a module?▼

You need to include configuration files when asking an AI to refactor a module to provide comprehensive project information. Bundling the code, tests, and configs ensures the AI understands the module's dependencies and context within the project.