Demystifying Agent Skills
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Skills are usually assumed to inject knowledge the model lacks. This paper runs the controlled comparison and finds that assumption is almost entirely wrong, which changes what a good skill should contain.
Procedure beats facts by an order of magnitude: Across 8,135 normalized trial records, procedural anchoring accounts for 65.7% of cases where a skill helps and explicit knowledge injection accounts for 4.5%. Skills stabilize execution rather than supply information.
Precision collapses as the library grows: As the pool goes from 5 to 100 skills, actual-use precision falls from 29.6% to 3.3%. Every skill you add makes the rest harder to select correctly, which is the empirical version of the trigger scarcity problem.
They still beat the alternative: Skills outperform Workflow Memory by 6.06 points in matched comparisons, so the format earns its place even with the selection problem unsolved.
Why it matters: The failure modes are named and diagnosable, brittle assumptions, incompatible contexts, and insufficient adaptation. Combined with the precision curve, the practical read is to write skills as repeatable procedures, keep the active set small, and stop treating them as a place to dump reference material.
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