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oracle

Bundle prompts with selected project files for AI code analysis.

Updated Jan 26, 2026
Search Tags:#automation#development#context#ai#llm#code
One-click install
npx skills add https://github.com/caiazzop/clawdbot-personal-assistant --skill oracle-caiazzop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/caiazzop/clawdbot-personal-assistant/tree/main/skills/oracle
Command: npx skills add https://github.com/caiazzop/clawdbot-personal-assistant --skill oracle-caiazzop

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines complex development tasks by allowing AI models to understand and interact with your project's codebase and context, reducing the need for manual information gathering and context switching.

Core Features & Use Cases

  • Contextual Code Analysis: Bundles prompts with relevant files for AI to analyze code with full project context.
  • Automated Workflows: Facilitates long-running tasks like code reviews or refactoring suggestions by maintaining sessions.
  • Use Case: Debugging a complex issue by providing the AI with specific source files, error logs, and project structure to pinpoint the root cause and suggest a fix.

Quick Start

Use the oracle skill to preview the token count for the files in the src directory with the prompt 'refactor this code'.

Frequently Asked Questions about oracle

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

FAQPage Schema
How do I provide AI with full project context for code analysis?▼

To provide AI with full project context, bundle your specific prompt with selected project files for the AI to analyze. This enables contextual understanding of your codebase for tasks like debugging, code review, and refactoring.

Can I use AI to debug complex issues using my source files and error logs?▼

Yes, you can debug complex issues by providing the AI with specific source files, error logs, and project structure. This contextual information helps pinpoint the root cause and suggests targeted fixes.

How do I automate long-running code reviews and refactoring tasks with AI?▼

Automate long-running code reviews and refactoring tasks by maintaining AI sessions. This session management supports continuous operations, allowing the AI to process bundled prompts and files over extended periods.

Does contextual AI code analysis work with both API and browser engines?▼

Contextual AI code analysis works with both API and browser engines. You can configure optional engine and remote host settings to perform tasks like code review and debugging across different execution environments.

How do I check the token count before sending my code and prompt to an AI model?▼

Check the token count by previewing the selected files and prompt before execution. This helps manage context limits when bundling your codebase files for AI analysis and refactoring tasks.

Do I need to manually select files for AI codebase analysis, or is it automated?▼

You need to manually select specific files for AI codebase analysis. This requirement ensures the AI receives focused context, preventing token overflow and improving the accuracy of code review and debugging outputs.

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