Overview

Takeaway Know whether a skill is any good, and make it better. Use a review for fast feedback on the skill itself. Run an evaluation when you need proof it changes how an agent behaves, for example before you share it with your team.

This page is the place to start if you want to improve a skill. It walks through the two ways Tessl assesses skill quality, review and evaluation, and how you use each to make a skill better.

A review and an evaluation answer different questions.

Review	Reads the skill itself and scores how well it is written: whether the description is clear enough for an agent to load it at the right time, and whether the content is specific, actionable, and complete. Runs in under a minute and needs nothing but the skill.
Evaluation	Runs an agent on real tasks with and without the skill, then scores the difference. Tells you whether the skill actually changes what the agent does, not just whether it reads well. Takes longer and runs against a set of scenarios.

Building trust in a skill

Both reviews and evaluations help you assess different aspects of skill quality and guide improvement. In practice it helps to treat them as different levels of rigour, applied according to how much trust you need to build in the skill.

For example, if you are building a skill for yourself to use day to day, and you make quick ad hoc improvements as you go, a review may be enough. You will notice when a skill misfires and fix it, using review to guide your improvements.

If you are putting the skill into a shared repository to shape how other people's agents behave, you may decide this needs an extra level of rigour. An evaluation gets ahead of that: it proves the skill does what it claims, and the scenarios you generate are saved with the skill, so anyone who edits it later can re-run them and confirm it still works. They become a regression test for the skill.

Over the following pages we walk through both review and evaluation in depth.

Quick start: Skill Optimizer

Alternatively, Tessl's Skill Optimizer plugin runs this whole progression for you. It includes both review and evaluation and applies them as needed. Install it, point your agent at a skill, and it reviews the skill, applies quick win improvements, and escalates into evaluation when the skill is worth testing harder, generating evaluation scenarios, diagnosing failures, and re-running until the scores improve.

tessl install tessl/skill-optimizer

Then ask your agent to optimise the skill. Use Skill Optimizer when you want the full cycle handled for you. Use the commands directly, covered in the next pages, when you want control over each step.

Next

• Check a skill's quality using review - run a review, read the score, and apply fixes with tessl review.
• Prove a skill works using evaluation - generate scenarios and measure a skill's impact with tessl eval.
• To enforce a quality bar across your organisation, for example gating pull requests on a review score or scoring against your own standards, see Codifying and enforcing your skill standards.