Bundle user prompts with selected files into single LLM requests.
npx skills add https://github.com/spoonbobo/trinity --skill oracle-spoonbobo
Please help me install this Agent Skill. Skill: oracle Source: https://github.com/spoonbobo/trinity/tree/main/app/skills/oracle Command: npx skills add https://github.com/spoonbobo/trinity --skill oracle-spoonbobo
This Skill simplifies complex LLM interactions by intelligently bundling prompts with relevant files, ensuring models have the necessary context for accurate and efficient responses.
Use the oracle skill to preview the token count for a task using all files in the src directory.
High-intent search queries and answers about installing and using this skill.
File bundling for LLMs combines your prompt and selected project files into a single request payload. This approach gives the model complete project context upfront, enabling more accurate and context-aware responses for complex coding tasks.
The best way to package source files and error messages is by bundling them with your prompt into a single request. This optimizes LLM usage by ensuring all necessary project context, including build commands, is provided upfront for precise solutions.
Yes, you can preview token usage before sending a bundled prompt to a large language model. This feature helps you manage context limits and select files efficiently, guiding best practices for optimizing your LLM interactions.
Yes, this file bundling approach works with both API and browser-based LLM interactions. It supports comprehensive session management and file attachment patterns, ensuring context-aware responses across different large language model platforms.
You can manage long-running LLM sessions by using session management features to maintain continuous project context. This involves selecting relevant files, previewing token counts, and bundling everything with your prompt for each interaction.
NJX-njxBundle prompts with source files for AI code review tasks.
malue-aiBundle prompts with project files for AI-driven code analysis and problem-solving.
understudy-aiBundle prompts with files and manage engines for oracle CLI tasks.
brikkoAIBundle repository files and prompts into a single request for second AI models.
