Bundle prompts with selected files and directories for AI processing.
npx skills add https://github.com/softweek/openclaw --skill oracle-softweek
Please help me install this Agent Skill. Skill: oracle Source: https://github.com/softweek/openclaw/tree/main/publish_output/skills/oracle Command: npx skills add https://github.com/softweek/openclaw --skill oracle-softweek
This Skill streamlines the process of providing relevant code and documentation context to AI models, enabling them to understand and act upon complex projects more effectively.
Use the oracle skill to preview the payload and token count for a task involving all files in the src directory.
High-intent search queries and answers about installing and using this skill.
To bundle repository files with prompts for AI code generation, package selected files and directories into a single request using file inclusion patterns. This ensures AI models process the full repository context needed for accurate code generation and debugging.
Providing repository context to an LLM for debugging requires bundling the target code, its tests, and configuration files into a single prompt. This method ensures the AI understands the module's place within the larger project architecture.
Yes, you can use file inclusion and exclusion patterns to select specific code directories for AI prompts. This filters the repository context, allowing the AI to process only the relevant files needed for your specific prompt engineering task.
You check the token count by previewing the payload before sending the prompt with repo files to an AI model. This feature allows you to manage the context size and ensure the bundled request fits within the LLM's limits.
Yes, prompt templating works with both API and browser-based AI interactions. This allows you to standardize how repository context and prompts are bundled across different platforms for consistent code analysis and generation.
You need to include configuration files when asking an AI to refactor a module to provide comprehensive project information. Bundling the code, tests, and configs ensures the AI understands the module's dependencies and context within the project.
p-sree-sai-pavanBundle prompts with curated source files for advisory model reviews across local projects.
aRustyDevProfile Rust tests with Valgrind and Callgrind via Nextest.
pproencaAutomate Git branch completion with merge, PR, and cleanup options.
backengineeringDevelops custom tools using LLVM and Clang for source analysis, debugging, and IDE integration.
