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oracle

Bundle prompts with project file context for AI-driven code analysis and task execution.

Updated Jan 29, 2026
Search Tags:#developer tools#code analysis#prompt engineering#cli#ai#context bundling
One-click install
npx skills add https://github.com/douglasjs/clawdbot_plugin_tools --skill oracle-douglasjs
Or copy as Structured Prompt for Agentβ–Ό
Please help me install this Agent Skill.
Skill: oracle
Source: https://github.com/douglasjs/clawdbot_plugin_tools/tree/main/oracle
Command: npx skills add https://github.com/douglasjs/clawdbot_plugin_tools --skill oracle-douglasjs

SYSTEM DOCUMENTATION & REQUIREMENTS

πŸ’‘ This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines complex coding tasks by bundling prompts with relevant project files, enabling AI models to understand and operate within your codebase's context.

Core Features & Use Cases

  • Contextual AI Interaction: Provides AI with specific files and directories for accurate code analysis and generation.
  • Efficient Workflow: Supports dry runs for previewing token usage and offers session management for long-running tasks.
  • Use Case: Debug a complex bug by providing the AI with the error log, relevant source files, and test cases, 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 AI models with code context for accurate code analysis?β–Ό

You provide AI models with code context by bundling prompts with relevant project files and directories. This context bundling enables the AI to understand your codebase for accurate code generation and debugging.

How do I preview token usage before sending a large codebase to an AI model?β–Ό

You preview token usage by performing a dry run before executing your task. This estimates token consumption for included files and directories, helping manage efficient development workflows.

Can I use AI-driven code analysis with both API and browser-based model interactions?β–Ό

Yes, AI-driven code analysis supports both API and browser-based model interactions. This flexibility allows you to execute tasks ranging from debugging to code generation across different model interfaces.

What is the best way to debug a complex bug using AI with my source files?β–Ό

The best way to debug a complex bug is to bundle the error log, relevant source files, and test cases into a prompt. This provides the AI the necessary project context to pinpoint the issue and suggest a fix.

Does this code context bundler support session management for long-running tasks?β–Ό

Yes, the code context bundler supports session management for long-running tasks. This feature helps manage file attachments and token usage efficiently throughout extended development workflows.

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