Bundle user prompts with selected project files for AI code generation.
npx skills add https://github.com/Dbillionaer/wholesaile --skill oracle-dbillionaer
Please help me install this Agent Skill. Skill: oracle Source: https://github.com/Dbillionaer/wholesaile/tree/main/skills/oracle Command: npx skills add https://github.com/Dbillionaer/wholesaile --skill oracle-dbillionaer
This Skill streamlines the process of providing relevant code context to AI models, enabling more accurate and efficient code generation, debugging, and analysis by bundling prompts with file selections.
oracle CLI, including previewing token usage, selecting files, and managing sessions.Use the oracle CLI to preview the token count for a task involving all files in the src directory, excluding test files.
High-intent search queries and answers about installing and using this skill.
To provide code context for debugging, you can bundle your prompt with specific project files and error messages. This approach enables the AI to understand your repository context and generate targeted suggestions for complex issues.
Managing token usage when prompting an LLM involves previewing the token count before submitting your request. You can use a CLI tool to select specific files and exclude unnecessary ones to optimize the prompt size.
Yes, you can use a CLI tool to bundle prompts with selected repository files. This contextual prompting method guides the AI to process relevant code context for tasks like refactoring and feature implementation.
To select specific files for AI-driven code generation while excluding test files, use your CLI tool's file selection features. You can target directories like src and explicitly exclude test files from the prompt bundle.
Yes, this prompt engineering workflow requires the oracle CLI tool. It facilitates AI-driven code understanding by managing file selection, previewing token usage, and bundling your prompts with the relevant project context.
Providing project context improves AI code analysis and refactoring by allowing the model to understand the repository structure. Bundling relevant files with your prompt streamlines the process and leads to more accurate code generation.
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