Bundle prompts with selected files and directories for AI models.
npx skills add https://github.com/lehalpaul/agents --skill oracle-lehalpaul
Please help me install this Agent Skill. Skill: oracle Source: https://github.com/lehalpaul/agents/tree/main/skills/oracle Command: npx skills add https://github.com/lehalpaul/agents --skill oracle-lehalpaul
This Skill streamlines the process of providing code context to AI models, enabling them to understand and act upon your codebase more effectively, reducing the need for manual context-switching and lengthy explanations.
Use the oracle skill to preview the token count for the files in the src directory with the task 'refactor this code'.
High-intent search queries and answers about installing and using this skill.
To provide code context to an AI model, you can bundle prompts with selected files and directories into a single request. This method enables context-aware responses by packaging your source files and logs together for the AI to analyze.
Yes, you can use GPT and Gemini models to analyze your codebase. The system supports both API-based and browser-based execution for various AI models, allowing flexible workflows depending on your available engine access.
The best way to manage long-running AI code analysis sessions is using built-in session management features. This allows you to reattach to ongoing AI sessions, preventing data loss and avoiding redundant work during complex tasks.
You can preview token counts for source files by bundling your selected files and directories with your task prompt. This preview functionality helps you understand the context size before submitting the request to the AI model.
Yes, prompt templating works with file globbing to streamline code context bundling. You can use glob patterns to select specific files and directories, then apply templated prompts to generate targeted, context-aware AI requests.
Bundling files for AI code analysis streamlines the process of providing structured code context, reducing manual context-switching. It enables AI models to understand and act upon your codebase more effectively than pasting lengthy, unstructured explanations.
brainbrewlabsMap codebase structure and locate feature-related files via globbing and grep searches.
eng-vmessiahEnforce the Red-Green-Refactor cycle with failing tests before code implementation.
seasonmacAutomate oracle CLI prompts, file bundling, and engine/session choices.
daedalusAudit coding agent robustness across eight fault-tolerance dimensions.
