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.
with Claire Vo
Create text-based Mermaid diagrams representing your application flows, database schemas, or user interactions. You can generate these from your existing codebase using an AI prompt or as part of your development lifecycle.
Store the Mermaid diagrams as markdown (.md) files in a dedicated directory within your project repository, for example: memory/ai/diagrams/.
Use a terminal command to find all diagram files, concatenate them, and append them to your AI's system prompt. This loads the entire context at the start of your session.
claude append-system-prompt "$(cat memory/ai/diagrams/**/*.md)"Ask the AI complex questions about your codebase. The AI can now answer instantly without searching files, as the information is in its preloaded context.
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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.
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.
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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