oracle

Name: oracle
Availability: InStock
Author: badbsallyy

Bundle prompts with selected project files for AI-driven code analysis.

Updated Feb 15, 2026

Search Tags:#developer tools #code analysis #documentation #prompt engineering #cli #ai

Ready to run

npx skills add https://github.com/badbsallyy/openclaw-source-code --skill oracle-badbsallyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/badbsallyy/openclaw-source-code/tree/main/openclaw-sourcecode.zip/skills/oracle
Command: npx skills add https://github.com/badbsallyy/openclaw-source-code --skill oracle-badbsallyy

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 selected files for AI to analyze with full repository context.
Efficient Workflow: Guides users on best practices for using the oracle CLI, including prompt engineering, file selection, and engine/session management.
Use Case: Debugging a complex issue by providing the AI with relevant source files and error logs to pinpoint the root cause and suggest a fix.

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 provide project context to an AI for code analysis?▼

To provide project context for code analysis, bundle prompts with selected project files. This gives the AI full repository context to effectively debug complex issues and suggest fixes.

How do I optimize AI prompts for complex development tasks?▼

Optimize AI prompts for development tasks by enforcing best practices for prompt engineering and context selection. This involves carefully selecting relevant files to ensure the AI receives precise contextual understanding.

Can I use CLI to manage file attachments and sessions for LLM interactions?▼

Yes, you can use a CLI to manage API and browser-based LLM interactions. It handles session management and file attachments, allowing you to preview token counts and exclude specific files like tests.

Does AI-driven documentation analysis work with browser-based LLMs?▼

AI-driven documentation analysis works with both API and browser-based LLM interactions. It manages file attachments and sessions to help the model understand and interact with your docs effectively.

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

The best way to debug complex code issues using AI is providing relevant source files and error logs. This contextual bundling allows the AI to analyze the full repository context and pinpoint root causes.

How do I preview token counts for selected files in a CLI?▼

Preview token counts for selected files in a CLI by specifying your project directory and exclusion rules. This workflow helps optimize context selection and manage token limits for AI tasks.