oracle

Bundle prompts with project files for AI code analysis and generation.

67|24|Updated Jan 31, 2026
One-click install
npx skills add https://github.com/openxjarvis/openclaw-python --skill oracle-openxjarvis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/openxjarvis/openclaw-python/tree/main/skills/oracle
Command: npx skills add https://github.com/openxjarvis/openclaw-python --skill oracle-openxjarvis

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines complex coding tasks by bundling prompts with relevant code files, enabling AI models to understand project context and provide accurate, actionable advice or generate code.

Core Features & Use Cases

  • Contextual Code Analysis: Analyzes codebases by including specific files and directories in prompts.
  • AI-Assisted Development: Leverages LLMs (like GPT-5.2 Pro, Gemini) for tasks ranging from debugging to feature implementation.
  • Use Case: Debug a complex bug by providing the AI with the relevant source files, error messages, and build commands, allowing it to pinpoint the issue 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 for AI-driven code analysis and generation?

For AI-driven code analysis, you bundle prompts with selected project files and directories. This provides the LLM with direct project context to generate accurate code suggestions or pinpoint bugs.

Can I use different LLMs like GPT and Gemini for AI-assisted development?

Yes, AI-assisted development supports various engines including direct API calls for models like GPT-5.2 Pro and Gemini, allowing you to choose your preferred LLM for coding tasks.

How do I optimize token usage when sending large codebases to an LLM?

You optimize token usage by previewing the payload before execution. The system provides detailed payload previews, letting you manage token counts and exclude unnecessary files like tests.

What is the best way to debug complex code using an AI coding assistant?

The best way to debug with an AI coding assistant is bundling relevant source files, error messages, and build commands into a prompt. This gives the LLM full context to suggest a targeted fix.

Does this approach handle long-running sessions for browser automation tasks?

Yes, the system manages long-running sessions for browser automation and direct API calls. This ensures stable LLM integration when processing complex, sustained code generation requests.