AI Guides › Skills & Agents
Testing A Skill Before You Trust It With Anything Real
By Nigel Guy · 2 min read
A skill that worked once feels trustworthy. That feeling is doing more work
than it's earned — one success tells you it can work, not that it reliably
will, and the gap between those two claims is where most skill-related
mistakes actually happen.
The rule: run a new skill on low-stakes, checkable cases before you trust
it on anything that matters — a handful of successful test runs is the
minimum evidence, not a formality.
The mechanism
- Pick test cases you already know the right answer to. This is the
only way to actually verify correctness rather than just plausibility.
- Include at least one edge case on purpose — an unusual input, a
missing field, something slightly outside the normal pattern. Most
skills that fail, fail here first.
- Run it more than once. A skill that depends on an AI model's output
isn't perfectly deterministic — one success is a data point, not a
guarantee, especially if the underlying model or connected tool can
change behaviour over time.
- Check the failure mode, not just the success case. When it does get
something wrong, does it fail obviously, or does it fail quietly with a
plausible-looking wrong answer? The second is far more dangerous and
worth knowing about before you rely on it.
- Only then expand to real, higher-stakes use — gradually, not all at
once.
What to skip
Skip testing only the easiest, most obvious case — that's the case least
likely to reveal a problem. And skip treating "it hasn't failed yet" as the
same claim as "it's reliable" — the first describes your sample size, not
the skill's actual behaviour.
Guardrails
- Testing reduces risk; it doesn't eliminate it. Keep reviewing output on
anything genuinely important, even after a skill has proven itself.
- Re-test after any change to the tools a skill depends on — an API update,
a new model version, a changed data format can all silently break
something that passed every test last month.
- A skill's test results are only as good as how honestly you evaluated
them — resist the pull to call a borderline result a pass because you're
eager to start using it.
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