ai-problems-detection

What problem does it solve?

It prevents common AI programming failure modes—overengineering, reinventing existing solutions, using undocumented APIs, duplicating code, and building monolithic files—by forcing a mandatory self-check before writing or changing code.

Core Features & Use Cases

• Pre-implementation anti-pattern checklist: Verifies simplicity, reuse, documentation knowledge, duplication risk, and modularity (e.g., file size and mixed responsibilities).
• API and library veracity guardrails: Requires confirming method/function existence and options against the project’s dependency versions and official docs.
• Copy/paste and monolith detection prompts: Helps identify repeated logic across the codebase and flags “everything in one place” growth before it becomes technical debt.
• Security-focused review cues: Adds a safety step to detect credential exposure, unsafe query patterns, and sensitive-data logging tendencies.

Quick Start

Ask an AI to apply the ai-problems-detection protocol to your planned change, then have it stop and propose corrections if any of the five mandatory checks fail.