Building an AI agent skill for the first time feels more complex than it is. Most developers overthink the AI part and underthink the interface part. This guide walks you through exactly how to create an AI agent skill — from defining scope to publishing a working skill that others can use.

Step 1: Define the Job, Not the Tech

Before writing a single line of code, answer this: what specific task does this skill do, and when does it trigger? Bad scope: “Helps with DevOps.” Good scope: “Monitors a GitHub Actions workflow run, detects test failures caused by timing issues, and comments on the PR with a diagnosis and fix suggestion.” The narrower your scope, the more reliable your skill.

Step 2: Map the Inputs and Outputs

Every agent skill needs a clear interface. Define inputs (what data does the skill receive?), outputs (what does it produce?), and side effects (what does it do — call an API, trigger a pipeline, create a file?). Write this down as a simple schema before coding.

Step 3: Choose Your Skill Runtime

Most AI agent skills run in one of three patterns: function-calling agents (the LLM decides which tools to call), prompt chains (a fixed sequence of LLM calls), or hybrid (fixed outer shell with LLM handling decision points). For your first skill, use a prompt chain — it’s easier to debug and test.

Step 4: Write the Skill Logic

Here’s a minimal structure for a GitHub Actions failure analysis skill: (1) Ingest the workflow run ID from the webhook payload, (2) Fetch the failed job logs via GitHub API, (3) Prompt the LLM to identify root cause and suggest a fix, (4) Parse the structured output, (5) Post a comment to the PR via GitHub API. Each step should be independently testable.

Step 5: Add a SKILL.md File

A SKILL.md file is the contract between your skill and the agent runtime. It documents trigger conditions, required permissions, configuration options, and example inputs/outputs. Think of it as your skill’s README, but with enough detail for another agent to use it autonomously.

Step 6: Test Against Real Failure Cases

Collect 10–20 real examples of the input your skill will receive and run them through manually. Check for truncated inputs, API failures, and consistent output format. Document every failure mode and add handling for the top three.

Step 7: Package and Publish

Package your skill with SKILL.md, core logic file, tests directory, and dependency manifest. Publishing to a verified marketplace gives your skill credibility and distribution.

Want to publish your first AI agent skill? Submit it to agentskillexchange.com — the marketplace for verified AI agent skills.