Erold is an AI-native project management platform built specifically for developers and AI agents to collaborate. It features MCP server integration, REST API, CLI tool, and a knowledge base that gives AI persistent memory across sessions.

How does Erold integrate with Claude Code?

Erold provides a native MCP (Model Context Protocol) server with 18 tools that Claude Code can use directly. This allows Claude to create tasks, update progress, access project knowledge, and manage your projects without any copy-paste.

Yes! Erold offers a free tier with 1 project and unlimited tasks. Pro is $5/month with pay-as-you-go pricing for extra resources. Only pay for what you use beyond included allocations.

What AI tools does Erold support?

Erold works with any AI tool that supports MCP or REST APIs, including Claude Code, Claude Desktop, Cursor, Windsurf, GitHub Copilot, and custom AI agents. The API-first design ensures compatibility with current and future AI tools.

What is MCP (Model Context Protocol)?

MCP (Model Context Protocol) is a standard protocol that allows AI assistants to interact with external tools and services. Erold's MCP server provides 18 tools for task management, knowledge base access, and project operations that AI assistants can use directly.

How is Erold different from Jira, Linear, or Asana?

Erold is built API-first for AI agents, not humans-first with APIs added later. Traditional tools require copy-paste to share context with AI. Erold lets AI assistants read tasks, update progress, and access project knowledge directly through MCP, API, or CLI.

What is the Knowledge Base feature?

The Knowledge Base is persistent memory for AI assistants. Store project context, coding standards, architecture decisions, and any information AI should remember across sessions. AI can read and write knowledge entries, making it smarter about your specific project over time.

Does Erold have a CLI tool?

Yes! The Yet CLI (npm install -g @erold/cli) provides full task management from your terminal. Create tasks, list projects, mark items done, and integrate with git workflows. Perfect for developers who prefer command-line tools.

When should I use Color Systems Best Practices?

Setting up a color palette for a new project. Creating design tokens for colors. Checking or fixing color contrast issues. Implementing dark mode. Generating color variations programmatically

When should I NOT use Color Systems Best Practices?

Working with photography or image editing. Brand colors already strictly defined by client

UI/UX foundations critical

Color Systems Best Practices

Color contrast, OKLCH color space, semantic tokens, dark mode, and accessibility guidelines based on WCAG 2.2, Apple HIG, and Material Design 3.

Difficulty: intermediate
Read time: 1 min read
Version: v1.0.0
Confidence: established
Last updated: Updated Jan 8, 2026

Edit on GitHub Report Issue

Quick Reference

Use semantic color tokens (--color-primary, --color-error) not raw values. WCAG contrast: 4.5:1 for text, 3:1 for UI components. Use OKLCH for perceptually uniform palettes. Support dark mode with prefers-color-scheme and CSS variables.

Use When

Setting up a color palette for a new project
Creating design tokens for colors
Checking or fixing color contrast issues
Implementing dark mode
Generating color variations programmatically

Skip When

Working with photography or image editing
Brand colors already strictly defined by client

Color Systems Best Practices

Color contrast, OKLCH color space, semantic tokens, dark mode, and accessibility guidelines based on WCAG 2.2, Apple HIG, and Material Design 3.

Discussion

Layout Best Practices critical Spacing & Sizing Best Practices critical Web Accessibility Best Practices critical Responsive Design Best Practices critical

Color Systems Best Practices

Quick Reference

Use When

Skip When

Color Systems Best Practices

Discussion

Strictly Necessary

Analytics

Functional