How to Build an AI Agent Skill: A Step-by-Step Guide (2026)

Before you write anything: pick a task worth packaging

The most common mistake is building a skill that is too broad. “An assistant for marketers” is not a skill; it is a wish. “Turn a published blog post into five platform-specific social posts with hooks and hashtags” is a skill. The narrower the task, the easier it is to make the output reliable — and reliability is the entire value proposition.

A good test: can you describe the input, the output, and what “correct” looks like in two sentences each? If you cannot, narrow the scope until you can. For ideas on what is actually selling, see best AI automation skills in 2026.

Step 1 — Write the instructions

The instruction layer is the heart of the skill. Write it as if you are onboarding a capable but literal-minded new hire. Cover:

• The goal: one sentence on what success means.
• The steps: an explicit ordered procedure. Agents follow numbered steps far more reliably than implied ones.
• The format: exactly what the output should look like. Show the shape, not just describe it.
• The constraints: what to never do (don’t invent figures, don’t exceed 280 characters, don’t email anyone without confirmation).

Resist the urge to make the instructions clever. Clarity beats cleverness; a literal, boring instruction that the agent follows every time is worth more than an elegant one it follows 80% of the time.

Step 2 — Declare tools and permissions (least privilege)

If your skill only transforms text, skip this and you have a pure instruction skill. If it touches the outside world, declare each tool explicitly: which API, which scopes, which data. The golden rule is least privilege — request the minimum needed and nothing more. A lead-enrichment skill needs read access to your CRM and a web-search tool; it does not need send-email or delete permissions.

Never hard-code API keys or secrets into a skill. Secrets are injected at runtime by the host; a skill that ships with a baked-in token is a skill that leaks it. This is the single fastest way to fail a safety review.

Step 3 — Build worked examples

Ship at least five to ten examples in the form this input → this exact output. Examples do two jobs: they steer the model at runtime (few-shot grounding) and they document the contract for buyers. Choose examples that cover the easy case, two or three tricky cases, and at least one edge case where the right answer is “refuse” or “ask for clarification.”

Step 4 — Add an evaluation set (the part people skip)

This is what separates a product from a prompt. An evaluation set is a collection of inputs with a way to grade the output automatically or semi-automatically. For a classification skill, it’s inputs with expected labels. For a generation skill, it’s inputs with a checklist or a model-graded rubric.

Why bother? Because the moment you change the instructions — or the underlying model updates — you need to know whether you made the skill better or quietly broke it. Without evals you are flying blind, and a skill you cannot evaluate is a skill nobody should buy. We go deep on this in how to test skills before publishing.

Step 5 — Version it and write a changelog

Give the skill a semantic version (1.0.0) and a human-readable changelog. Because skills take actions, behaviour changes can surprise users — a buyer should always be able to read what changed before accepting an update. Bump the major version when behaviour changes in a way that could break a buyer’s workflow; bump minor for additive improvements; bump patch for fixes.

Step 6 — A copy-ready skill structure

Most skill formats, whatever the host, reduce to the same shape. A clean, portable layout looks like this:

my-skill/
  manifest.json        # name, version, permissions, compatible runtimes
  instructions.md      # the goal, steps, format, constraints
  tools/               # tool + API definitions (optional)
  examples/            # input -> output pairs
  evals/               # evaluation inputs + grading
  CHANGELOG.md         # human-readable version history

Keep the manifest declarative. Anything that describes what the skill needs (permissions, compatible runtimes, version) belongs in the manifest so the host can reason about it without executing the skill.

Step 7 — Test, then publish

Run your evaluation set. Fix the failures. Run it again. When it passes, publish. On GeraSkills the publish flow walks you through identity verification, payout onboarding via GeraCash / Stripe Connect, a safety review, and signing — after which the skill appears in the catalogue and earns you a revenue share on every install. The economics are covered in how to make money with AI skills.

Common mistakes to avoid

• Too broad. One skill, one task. Split anything bigger.
• No evals. You cannot maintain what you cannot measure.
• Over-permissioned. Asking for more access than the task needs scares buyers and fails reviews.
• Baked-in secrets. Inject at runtime, never ship them.
• No examples. The agent and the buyer both need to see the contract.

Where to go next

If you want the conceptual grounding first, read what are AI agent skills. If you want to understand how skills relate to plugins and raw APIs, read AI skills vs. plugins vs. APIs. When you’re ready, publish your first skill.