Introduction

This article is for people who have already written a Skill or are about to write one.

If you have already built a Skill and tested it in a real environment, you have probably run into questions like these:

• I thought I had written everything clearly. Why does it still not behave the way I expected?

• I thought the trigger conditions were already clear. Why is the Agent not calling the Skill at all?

• Why is the Skill output inconsistent from one run to another?

• Why do some other Skills look much simpler than mine, yet still perform just as well, or even better?

The problem is often not a lack of effort. The deeper issue is that your definition of a high-quality Skill may be off from the start.

These are 3 of the most common misconceptions Skill authors run into early on.

Misconception 1: If it feels clear to you, it must be clear enough

At its core, a Skill is a written set of best practices, and sometimes a procedural one, for solving a task. When we write one, we usually understand the context very well ourselves.

In our heads, we know the background, the user, the real goal, what is feasible in practice, and what is not.

What we think we need is simply an executable plan. As a result, when we write SKILL.md, we often focus mostly on "how to do it."

But AI models are not human. They do not automatically fill in missing context, and they do not understand the constraints you have in mind for a specific real-world situation.

That is why many Skills start showing problems as soon as they go through their first serious test.

For example:

• the trigger condition is too broad, so the model invokes the Skill when it should not, or fails to invoke it when it should
• the steps exist, but there is no clear execution order or branching logic
• the Skill says what the Agent should do, but does not give enough structure to the output
• the output requirements look complete, but there is no real acceptance standard

These issues may not be obvious when you read the document yourself. But once the Skill enters real use, they directly affect reliability.

Takeaway: a strong SKILL.md needs a clear and stable structure, one that leaves the model as little room for guesswork as possible.

Misconception 2: The longer the Skill, the better it will perform

Another common misconception is that a longer document must mean a better Skill.

Not necessarily.

When I first started writing Skills, I liked putting a lot of domain background into SKILL.md: what certain metrics meant, how specific terms should be understood, even what counted as best practice in a given field.

Then I came across Claude's article, Skill authoring best practices.

The first principle is simple: keep it concise. For a lot of general knowledge, you should assume the model already knows it. You do not need to repeat all of that material inside SKILL.md.

Writing down information the model already knows, or may even know better than a human writer, is often wasteful. Every time the Skill is loaded, that extra material takes up context window space and increases token cost.

Also, when a SKILL.md gets very long, it is often because the task itself has many branches and edge cases, so the author tries to pack every possibility into one document. In most cases, the better approach is to split it up.

That means keeping the main problem-solving framework in SKILL.md, while moving more complex branch logic into separate reference files that can be loaded when needed.

So with SKILL.md, longer is not automatically better. But it should not be vague or under-specified either. Writing a clear framework first, then moving implementation details into references, is a habit you build over time.

Misconception 3: If the Skill can run, it must already be fine

Many Skill authors make a very natural assumption: I have run it successfully a few times, so it must already be in good shape.

But "it runs" and "it is good" are two very different things.

Take the previous misconception as one example. If another author's SKILL.md solves the same task in a more concise way and uses fewer tokens, it may already run more efficiently and cost less than yours.

Here is another example from my own work. I once wrote a Skill to analyze resumes. It was designed to extract structured information from candidate resumes and help me judge how well someone matched a role.

I got the Skill working fairly quickly. But the real problem showed up just as fast: the decision framework, evaluation criteria, and output template were not consistent from one run to the next.

That is the difference between "it can complete the task" and "it can deliver stable results." The first is merely usable. The second is much closer to a reusable, maintainable level of quality.

Even though SKILL.md is just a text file, it is really a decision framework that shapes how an Agent works. If you want a Skill to behave reliably across different scenarios, you need to treat it more like software:

• constrain the output so the quality stays more consistent
• test and refine the Skill across different scenarios before publishing it

My recommendation: give your Skill one professional checkup before you publish it

If a tool could review a Skill before you publish it, show you what this SKILL.md already does well, and point out what still needs improvement, would that save you time and rework later?

That is the reason I built bestskills.dev.

I recently released a new feature there: a full quality audit for a SKILL.md, based on 63 review checks, that returns a structured report.

Those 63 checks span 4 broad areas:

• Standards: whether the frontmatter and structure follow the expected rules, which directly affects whether a Skill can even be loaded properly. This is something many authors overlook.
• Effectiveness: whether the Skill can actually achieve the author's intended result and produce high-quality outputs
• Safety: whether the Skill introduces risky operations and how serious those risks are
• Conciseness: whether the SKILL.md stays compact enough to avoid wasting context window space and driving up cost

What I want to emphasize is this: writing a Skill always involves personal judgment, but evaluating the quality of a Skill can still be grounded in a set of relatively objective standards.

• Standards: 24 checks
• Effectiveness: 21 checks
• Safety: 10 checks
• Conciseness: 8 checks

In the end, each SKILL.md gets a score out of 100, and each range comes with a recommendation:

• 90 - 100: Excellent, ready to use or publish
• 70 - 89: Good, with limited but meaningful room for improvement
• 50 - 69: Fair, important revisions recommended
• Below 50: Not ready, major rewriting needed

More important than the score

The score is an objective number. But what matters more is what you learn from the review report.

• What problem-solving ideas can you learn from someone else's SKILL.md audit report?
• If you were writing that same SKILL.md, how could you make it better?

A strong SKILL.md feels a lot like well-structured code: clear, readable, and satisfying to work through. A weak SKILL.md usually does the opposite and leaves you guessing.

If you have written a Skill, try it once

If you have recently finished a Skill, or are about to make one public, I strongly recommend doing a quick quality check first.

Paste your SKILL.md URL into bestskills.dev.

Click the checkup button, wait a moment, and you will get a scored report with issue-level feedback.

The score itself is useful, but the bigger benefit is seeing where your Skill is strong, where it is weak, and what is worth improving next.

Before you publish it, run one checkup first. It may save you a lot of unnecessary rework.

One last thing: this feature is free to use.

If you have suggestions or complaints about this feature, feel free to email me at deepnotes.org@gmail.com.