deepdive

The problem

Standard LLM prompts often hallucinate or break dependencies because they only see a narrow slice of the codebase. When working on complex projects, missing one utility function or config file leads to inconsistent implementations and refactor debt.

What it does

• Performs a systematic scan of all source code, markdown documentation, and configuration manifests.
• Automatically respects .gitignore and filters out noise like node_modules, lockfiles, and minified assets.
• Orchestrates sub-agents to process large repositories, ensuring full coverage without hitting context window limits.
• Synthesizes a mental model of architecture, naming conventions, and error handling before writing code.
• Implements requested features or fixes strictly following the project's existing patterns and dependencies.

Why this beats prompting it yourself

Manual prompts require you to know which files are relevant, which is prone to human error. This skill automates the discovery process, ensuring every single line of source code is accounted for, either directly or via delegated summarization, before a single edit is made.

Use cases

• Onboarding to a legacy repository where you need to implement a feature without breaking existing logic.
• Performing codebase-wide refactors that depend on understanding cross-module exports.
• Auditing a project for architectural consistency and documentation gaps.
• Migrating logic between modules while maintaining established coding standards.