Skill Evaluation Field Notes

Better agent work starts with better evidence.

We study how skills change real AI workflows. These are the methods, failures, and design decisions behind trustworthy evaluations.

MethodsAugust 28, 20269 min read
Replay the work, not the prompt
A skill changes the path an agent takes. A fair evaluation preserves the work around it, then changes one thing.

Latest

Notes from the evaluation loop

Skills8 min read
Longer instructions often hide the real interface. A useful skill states when it applies, what it owns, and how success can be checked.
Skill Evaluation team · August 26, 2026
Field notes7 min read
Tests can pass while the workflow gets slower, noisier, or less trustworthy. Review the evidence that sits around the score.
Skill Evaluation team · August 21, 2026
Methods10 min read
Test whether an agent chose the right tool, used it with the right scope, and turned its result into a defensible outcome.
Skill Evaluation team · August 18, 2026
Field notes8 min read
A useful failure label identifies the decision that broke, not just the symptom visible at the end of the run.
Skill Evaluation team · August 12, 2026
Methods7 min read
Preserve the smallest real context that reproduces the failure, then keep it in the suite after the immediate fix ships.
Skill Evaluation team · August 5, 2026

Replay real work

Use captured sessions instead of idealized prompts.

01

Change one thing

Freeze the context, runtime, and starting files.

02

Keep the evidence

Review output, tools, files, tests, cost, and failures.

03
Written for people who build and review agent workflows.