Speed up your development workflow by creating shortcuts for frequent AI commands using shell aliases and building dedicated command-line tools to script complex, repetitive AI tasks.
with Claire Vo
In your shell configuration file (e.g., .zshrc), define short aliases for your most-used AI commands, such as setting a specific model or loading context files.
alias cdi='claude append-system-prompt "$(cat memory/ai/diagrams/**/*.md)"'Find a recurring task that involves a structured conversation with an AI, such as generating design mockups for a specific page type and style.
Write a small script that acts as a Command-Line Interface (CLI) tool. This tool will wrap an AI API (like Gemini's) to perform your specific task.
Design the tool to prompt the user for only the essential variables (e.g., 'What type of website?', 'What page?'). This constrains the input and makes the prototyping process faster and more repeatable.
The CLI tool should combine the user's inputs with a predefined prompt structure and send the final prompt to the AI via an API call to get the desired output, such as generated images.
Implement an automated 'fix and commit' loop by configuring an AI stop hook. This hook runs quality checks on AI-generated code and automatically prompts the AI to fix any detected errors.
Boost your AI coding assistant's performance by preloading your application's architecture using text-based Mermaid diagrams. This gives the AI deep context, resulting in faster, more accurate code-related answers.
Improve your AI's performance by moving from a single, massive context file to a modular library of small, focused markdown files. This system allows the AI to intelligently load only the most relevant information for each task, enabling you to use simple, 'lazy' prompts and get better results.
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