Top 10 Best Practices for Building Robust Agent Skills

Building an Agent Skill is easy; building a great Agent Skill that is reliable, efficient, and useful requires following some key engineering principles.

After analyzing the official skills and building dozens of our own, we've compiled the top 10 best practices for Agent Skill development.

1. Optimize for "Progressive Disclosure"

Don't dump everything into one giant file. Claude only reads your skill's description initially.

• Good: A concise description that clearly states when to use the skill.
• Bad: A description that tries to explain how to do the task. Save the "how" for the SKILL.md body.

2. Be Concise (Tokens Cost Money)

Once your skill is loaded, every word in SKILL.md is added to the context window.

• Avoid: Fluff, polite chatter, or repetitive explanations.
• Do: Use bullet points, clear imperatives, and standard formats.
• Why: It saves money (fewer tokens) and reduces the chance of Claude getting distracted by irrelevant text.

3. Use the "Template Pattern" for Outputs

If you need a specific output format, provide a template.

## Report Template
ALWAYS use this structure for your response:

# [Title]
## Executive Summary
[1 paragraph summary]
## key Findings
- [Point 1]

This is much more reliable than describing the format in prose.

4. Provide "Few-Shot" Examples

Abstract instructions can be misinterpreted. Concrete examples are grounding. Include a section in your SKILL.md with 2-3 User/Assistant turn examples showing exactly how you want edge cases handled.

5. Modularize with Scripts

Don't ask Claude to hallucinate complex logic or math. If a task is deterministic, write a script.

• Bad: "Calculate the fibonacci sequence up to 100..."
• Good: including a fib.py script and telling Claude to run it. This guarantees accuracy.

6. Define Tool Boundaries

Explicitly state what the skill should NOT do.

"This skill is for reviewing code only. Do not rewrite or refactor the code unless explicitly asked."

This prevents "scope creep" where an agent tries to do too much.

7. Version Your Skills

Use the version field in your frontmatter. As you iterate, you might have breaking changes. Being able to look back at code-reviewer v1.0 vs v2.0 is essential for debugging agents.

8. Handle Errors Gracefully

Teach your skill what to do when things go wrong.

"If the input file is missing, do not hallucinate content. Instead, report the error: 'Error: File [name] not found'."

9. Use Consistent Naming Conventions

Follow the official structure:

• Folders: kebab-case (e.g., data-analysis)
• Files: SKILL.md (always uppercase)
• Scripts: scripts/ directory

10. Test with Different Models

A skill that works great with Claude 3.5 Sonnet might behave differently with Claude 3 Haiku.

• Sonnet is smarter and needs fewer instructions.
• Haiku is faster but might need more explicit, step-by-step guidance. Test against the models you intend to use in production.

Conclusion

Treat your Agent Skills like code. They should be structured, concise, documented, and tested. By following these best practices, you'll build agents that are not just cool demos, but reliable productivity tools.

Next up: Learn when to use Skills vs standard MCP tools in our comparison guide: Agent Skills vs MCP Tools.