eval-skills

name

eval-skills

description

Eval and improve a skill against golden cases — run the target skill blind in a fresh, context-free subagent on each example input, grade the artifact against a per-case bar, and let the gaps drive the edits. Use when the user wants to test/eval/improve/harden a skill, says "this skill keeps producing X / keeps missing Y", or hands a skill plus cases with a bar for what good looks like. Pairs with write-skills (the authoring principles every fix obeys). Judgment-first and harness-free — prefer skill-creator for harness-based benchmarking, variance analysis, or description-optimization loops.

Eval Skills

Treat a skill like a function under test. Feed it example inputs in a clean room, check the artifacts against what good looks like, and let the failures drive the edits. The eval is only honest if the run is blind: the agent executing the skill must carry none of this conversation's context and must never see the expected output. Leak either and you are teaching to the test.

Inputs to settle before running

Confirm all three before spawning anything. If any is missing, do not run yet: tell the user exactly which one is missing and what a good version looks like, help them make it concrete, and echo it back. Do not invent cases, guess intent, or eval against a fuzzy wish.

• Target skill — must resolve to a real SKILL.md. If you can't find it, list the skills you can see and ask which one they mean.
• At least one golden case — a concrete input the skill will actually receive: a screenshot, a prompt, a file, a scene. "Improve write-spec" with no input attached is not a case.
• The bar per case — the outcome a good artifact achieves and the smells that would make it bad, not an exhaustive parts list. The skill's judgment is what's under test, so do not pre-enumerate every requirement — that turns the eval into a conformance check and stops testing whether the skill decides well. "Sliced so each piece is independently buildable and verifiable, at the granularity a competent practitioner would pick — a lazy mega-slice and pointless over-splitting are both failures" is a bar a judge can hold the work to; "slices it well" is too thin to grade and a fixed list of expected slices is too prescriptive. State the bar and the smells; let the judge apply them. The exception is a conformance-style skill that genuinely wants an exact task hit exactly — then the explicit criteria are the bar; match the bar's shape to the skill's nature, and if you can't tell which it is, ask. If the user gives only a fuzzy wish with no bar, draw the bar out of them and echo it back before spending agents.

Workflow

1. Validate inputs and surface first principles. Resolve the skill and read its first principles — what it's for and the standard it holds itself to; this is what the judge grades against, so if the skill doesn't make them clear, clarify with the user rather than inventing them. Settle the eval mode here too: judgment (a bar the judge applies) vs conformance (an exact task hit exactly) — ask the user if it's ambiguous. Then sharpen each case's bar — the outcome plus the smells, kept at the altitude the user cares about, never widened into a prescribed parts list unless the skill is conformance-style. Done when you can state the skill's first principles in a sentence and every case has a concrete input and a bar a competent judge could hold an artifact to.

2. Blind run, one fresh agent per case. Isolate every run so a misbehaving skill can't touch the live checkout and each case starts clean. Prefer capturing the artifact from the runner's final message — if the skill's output is a plan or text, ask for it inline and nothing hits disk to leak. When the skill must write files, give the runner a throwaway sandbox dir as its only writable root, not a worktree of the live repo (worktree isolation guards git state, not absolute-path or escaped writes). After every run, sweep the live checkout (git status) and clean anything the run leaked — isolation is best-effort, the sweep is the guarantee. Give the runner only the input and the instruction to use the target skill — never the bar, the smells, the other cases, or why you're asking. Done when you hold one artifact per case, each from a context-free run, and the checkout is clean.

3. Grade with a separate judge that applies judgment. Hand a fresh judge the artifact, the bar, and the skill's first principles — so it grades against the skill's own intent, not its personal taste — but never the expected output and never "make this pass." Grounded in those principles the judge is a competent practitioner: it decides whether the work clears the bar with defensible choices, and is explicitly free to fault both too-coarse and too-fine work. It must cite specific evidence for each verdict — a quote or pointer, not a number. Done when every part of the bar has a verdict grounded in the artifact.

4. Account for nondeterminism. Agents flicker. A single green is not proof. For any case that matters or any verdict that looks borderline, re-run the blind run 2–3× and report the pass rate. A skill that passes 1 of 3 is not fixed.

5. Diagnose each failure as skill-defect vs bad-case. A miss means either the skill failed to drive the behavior (fixable here) or the bar was wrong — it asked for something the skill should not do, can't express, or it punished a defensible judgment call the skill was right to make (tell the user; do not edit the skill to chase a wrong bar — that just encodes the wrong reality). Name the defect against the failure modes: premature completion, vague completion criterion, missing rule, no leading word, duplication, sediment, war story, no-op.

Output

A short report: per case, pass rate and the cited gap; the defect each failure mapped to; the edits you made (or, if the user asked to approve first, the diff you propose); and the re-eval result. Make the before/after movement legible — this is the evidence the skill actually improved.

Rules

• Blind is non-negotiable. The runner sees input only. The judge sees artifact + bar + the skill's first principles. The moment either sees the expected output, the eval is worthless.
• Test judgment, not conformance. The bar is a standard the work must clear, never a checklist of the answer. If you find yourself listing the exact pieces you expect, you've stopped evaluating the skill.
• Isolation is best-effort; the sweep is the guarantee. Always check the live checkout after a run and clean leaks, no matter how the run was sandboxed.
• One fresh agent per case per run — no shared context, so no cross-case learning inflates a later case.
• Grade against the bar, not against the other artifacts, and not on a numeric score that hides which part of the bar failed.
• Don't bend the skill to pass a case you can't defend. A failing case that exposes a bad bar is a finding, not a bug.