Top 5 Agent Skills Every Agent Builder Should Install

Your coding agent can write code, refactor functions, and debug errors. But can it design production-grade prompts? Build MCP servers that follow best practices? Evaluate whether your agent's outputs are actually good?

Agent Skills give your coding assistant specialized expertise on demand. They're folders containing a SKILL.md file with instructions, workflows, and references that your agent loads only when relevant. No context bloat, no manual setup. For a deep dive into how skills work and how to build your own, see How to Build and Deploy an Agent Skill from Scratch.

Here are 5 skills that cover the full agent development lifecycle, from designing prompts to evaluating outputs. Every skill listed works across Claude Code, Cursor, VS Code Copilot, Codex, and Gemini CLI.

To install any skill, run:

npx skills add <owner/repo> --skill <skill-name>

1. prompt-engineer

An expert prompt engineering skill that teaches your agent advanced techniques for designing effective LLM prompts. It covers system prompt architecture, few-shot example design, chain-of-thought patterns, output format specification, and context management. The skill identifies common pitfalls like imprecise language, missing format constraints, and prompt injection vulnerabilities.

Why it matters: Prompt design is the highest-leverage activity in agent development. A well-crafted prompt can be the difference between a prototype and a production-ready agent. This skill turns your coding assistant into a prompt engineering partner that catches issues before they reach users.

Best for: Any developer writing or refining prompts for LLM-powered applications and agents.

npx skills add davila7/claude-code-templates --skill prompt-engineer

GitHub: davila7/claude-code-templates

For Anthropic's approach to agent-specific prompting, see The Art of Agent Prompting.

2. skill-creator

Anthropic's official skill for creating, modifying, and evaluating skills. Instead of starting from a blank SKILL.md, this skill guides your agent through an iterative development cycle: define intent, draft the skill file, test with sample prompts, evaluate outputs, and refine. It adapts to different environments (Claude.ai, Claude Code, Cursor) and supports users across technical expertise levels.

Why it matters: Building skills manually is educational but slow. This skill automates the creation process and includes built-in evaluation with variance analysis, helping you ship higher-quality skills faster.

Best for: Developers who want to create custom skills for their team, product, or domain without starting from scratch.

npx skills add anthropics/skills --skill skill-creator

GitHub: anthropics/skills

For the manual approach that teaches you every component, see How to Build and Deploy an Agent Skill from Scratch.

3. mcp-builder

Anthropic's official guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. The skill covers the full development cycle in four phases: research and planning, implementation, review and testing, and evaluation creation. It supports both Python (FastMCP) and TypeScript implementations, and emphasizes concise tool descriptions and actionable error messages.

Why it matters: Skills give agents knowledge; MCP servers give agents capabilities. If you need your agent to interact with APIs, databases, or external services, this skill teaches it how to build MCP servers that follow best practices.

Best for: Developers building custom MCP servers to extend their agents' capabilities.

npx skills add anthropics/skills --skill mcp-builder

GitHub: anthropics/skills

For a quick hands-on start with MCP, see Create Your First MCP Server in 5 Minutes.

4. agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs through iterative refinement. This skill teaches your agent to implement self-critique loops, evaluator-optimizer pipelines, rubric-based assessment, and LLM-as-judge evaluation systems. Rather than relying on single-shot generation, it introduces systematic approaches to measuring and improving output quality.

Why it matters: Building an agent is half the challenge. Knowing whether it works reliably is the other half. This skill teaches your coding assistant how to evaluate agent outputs systematically, a practice that separates prototype-quality agents from production-ready ones.

Best for: Developers testing, debugging, or improving agent output quality, especially when building evaluation pipelines.

npx skills add github/awesome-copilot --skill agentic-eval

GitHub: github/awesome-copilot

5. openai-docs

Provides up-to-date OpenAI developer documentation with citations, covering the Responses API, Agents SDK, Chat Completions, Codex, Realtime API, model capabilities, and more. The skill uses OpenAI's MCP server to search, fetch, and browse official documentation pages, prioritizing MCP tools over general web search for accuracy.

Why it matters: LLM training data goes stale. If you are building with OpenAI APIs, this skill ensures your agent references the latest documentation rather than outdated knowledge. Every answer comes with a citation to the official source.

Best for: Developers building with OpenAI APIs who need accurate, current references without leaving their IDE.

npx skills add openai/skills --skill openai-docs

GitHub: openai/skills

Bonus: ai-sdk

The top 5 skills above are broadly useful to any agent builder regardless of stack. This bonus is more specialized, but for its audience, it may be the most valuable skill on this list.

The ai-sdk skill answers questions about the Vercel AI SDK and helps build AI-powered features. It covers core functions like generateText, streamText, ToolLoopAgent, embed, and tool calling. The skill checks local node_modules/ai/docs/ first, then falls back to ai-sdk.dev for the latest information.

Why it matters: The AI SDK is the most downloaded TypeScript AI framework with 2.8M weekly npm downloads. If you're building AI features in a Next.js or React application, this skill makes your coding assistant an AI SDK expert.

Best for: React and Next.js developers integrating AI features using the Vercel AI SDK.

npx skills add vercel/ai --skill ai-sdk

GitHub: vercel/ai

Quick Reference

Key Takeaways

• These 5 skills cover the full agent development lifecycle: design prompts, package expertise, build tools, evaluate quality, and reference documentation.
• All skills are cross-platform. They work across Claude Code, Cursor, VS Code Copilot, Codex, and Gemini CLI.
• Combine skills for compound effect. Use prompt-engineer to design your agent's prompts, skill-creator to package your expertise, and agentic-eval to verify quality.
• Vet skills before installing. Prefer skills published by known organizations like Anthropic, GitHub, or OpenAI. For community skills, check their detail page on skills.sh: every listed skill displays a Security Audit report so you know what you're installing.
• The skills ecosystem is growing fast. Browse skills.sh regularly to discover new skills as they are published.

What to Read Next

• How to Build and Deploy an Agent Skill from Scratch
• The Art of Agent Prompting
• Create Your First MCP Server in 5 Minutes

Enjoying content like this? Sign up for Agent Briefings, where I share insights and news on building and scaling AI agents.

Resources

• Agent Skills Specification
• Anthropic Skills Repository
• Skills.sh Registry