CanICode FAQs

Question 1

Does CanICode use AI tokens for every analysis?

Accepted Answer

No, CanICode's rule engine runs deterministically without consuming AI tokens for each analysis. AI was utilized during development to validate and calibrate rule scores through a 4-agent debate pipeline, ensuring accuracy without runtime cost.

Question 2

What platforms and integrations does CanICode support?

Accepted Answer

CanICode offers multi-platform access through a Web App (no install needed), a Figma Plugin, a CLI, a GitHub Action for CI/CD, and an MCP Server for integration with AI assistants like Claude Code and Cursor.

Question 3

What is CanICode and what does it do?

Accepted Answer

CanICode is a design linter that analyzes Figma designs to score their readiness for implementation by developers or AI, providing crucial insights before any code is written. It helps identify potential difficulties and risks.

Question 4

Can I customize CanICode's analysis for my specific project needs?

Accepted Answer

Yes, CanICode is highly customizable. You can tailor analyses using predefined presets (e.g., 'ai-ready', 'dev-friendly'), apply configuration overrides to adjust scores or exclude nodes, and even define your own project-specific custom rules.

Question 5

How does CanICode score design readiness?

Accepted Answer

CanICode evaluates designs using 39 rules across 6 quality categories, including Layout, Design Token, Component, Naming, AI Readability, and Handoff Risk. It generates a density-based score (S-F) indicating implementation difficulty, presented in detailed HTML reports.

CanICode

CanICode

Características Principales

Casos de Uso

Características Principales

Casos de Uso