oracle

Name: oracle
Availability: InStock
Author: StoryClaw

Bundle prompts with selected files for AI code analysis.

Updated Feb 11, 2026

Search Tags:#development #documentation #context #ai #llm #code

Ready to run

npx skills add https://github.com/storyclaw-official/storyclaw-assistant --skill oracle-storyclaw-official

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/storyclaw-official/storyclaw-assistant/tree/main/skills/oracle
Command: npx skills add https://github.com/storyclaw-official/storyclaw-assistant --skill oracle-storyclaw-official

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines complex development tasks by providing AI models with precise project context, enabling them to understand and interact with your codebase and documentation effectively.

Core Features & Use Cases

Contextual Code Analysis: Bundles prompts with relevant files for AI-driven code understanding and problem-solving.
Efficient Workflow: Guides users through best practices for prompt engineering, file selection, and engine usage for optimal AI interaction.
Use Case: Debugging a complex issue by providing the AI with specific source files and error messages to generate a targeted solution.

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 AI models with real repository context for code analysis?▼

To provide AI models with real repository context, you bundle your prompts with specific selected files so the AI can understand and interact with your codebase. This requires choosing exact files and configuring the engine for optimal results.

What is the best way to debug complex code using LLMs?▼

The best way to debug complex code using LLMs is to bundle specific source files and error messages into a prompt. This gives the AI precise project context to generate a targeted solution rather than guessing at the issue.

Can I preview token counts before sending files and prompts to an AI model?▼

Yes, you can preview token counts using a dry-run preview. This checks the token count for your task by evaluating the selected files in your source directory before actual AI processing.

Does contextual AI for code require specific file selection for every task?▼

Yes, contextual AI for code requires specific file selection for every task. Bundling prompts with precisely chosen files ensures the AI model receives only the relevant repository context needed to generate accurate answers.

How does bundling documentation with source files improve AI-generated answers?▼

Bundling documentation with source files improves AI-generated answers by providing in-depth project context. This enables the AI model to understand the full scope of your codebase and documentation, resulting in more accurate and contextualized responses.