| name | umbraco-skill-evaluator |
| description | Run quantitative + qualitative evaluations on an existing Claude skill. Use this whenever the user wants to test, benchmark, eval, validate, regression-check, or measure the value of a skill they've already drafted — including phrases like "run evals on this skill", "is this skill actually helping", "compare with-skill vs without-skill", "iterate on my skill based on results". This is the lightweight cousin of skill-creator: it skips drafting, blind comparison, and description-tuning, and focuses purely on the eval loop with Haiku-powered grading. |
Skill Evaluator
A focused, low-cost skill for evaluating skills. Assumes the skill already exists — your job is to prove (or disprove) that it earns its keep, and feed the results back into the next iteration.
Why this exists separately from skill-creator
skill-creator is a full authoring + evaluation pipeline. That's expensive in tokens when all you want to do is run evals. This skill keeps just the evaluation parts:
- with-skill vs. without-skill (or vs. previous version) parallel runs
- Haiku-powered grader subagents
- The HTML viewer for qualitative review
aggregate_benchmark.py for quantitative summary- The iteration loop
It deliberately drops: skill drafting guidance, blind comparator/analyzer agents, description optimization, and the full schema reference. If you actually need to write a new skill, hand off to skill-creator. If you want to optimize triggering, see "Description optimization (deferred)" at the bottom.
The loop, at a glance
1. Confirm the skill + figure out the eval set (≤3 prompts to start)
2. Spawn with-skill and baseline runs in parallel (one turn)
3. While runs go: draft / refine assertions
4. As notifications arrive: capture timing.json
5. Grade each run with a Haiku subagent
6. Aggregate → benchmark.json → launch viewer
7. User reviews, leaves feedback, you read feedback.json
8. Improve the skill, rerun (iteration-2/, iteration-3/, ...)
9. After iteration 2 (or the final pass), mention description optimization
Working directory pattern: put results in <skill-name>-workspace/ as a sibling to the skill directory. Inside, iteration-1/, iteration-2/, etc., and inside each, one directory per eval (use a descriptive name, e.g. extract-tables/ not eval-0/).
Step 1 — Confirm the skill and the eval set
Find the skill directory. Read its SKILL.md so you know what it claims to do. Check whether evals/evals.json already exists; if so, read it and ask the user whether to use those prompts as-is, modify them, or replace them.
Writing eval prompts (this matters)
The single biggest mistake in skill evals is using prompts that are too easy. If a baseline run with no skill can solve the task in one tool call, the skill has nowhere to demonstrate value and the benchmark will look like noise.
Every prompt must be a complex, multi-step task where the skill should earn its keep. Specifically:
- The task should require at least 3-4 distinct steps that a naive agent might get wrong, skip, or do in the wrong order.
- It should involve specialized knowledge or tooling the skill provides — patterns, scripts, schemas, conventions — that an unequipped agent would have to invent or guess at.
- It should have at least one trap a baseline run is likely to fall into (a non-obvious format, an edge case, a constraint that's easy to overlook).
- It should be phrased like a real user request, including realistic context (file paths, names, partial information, casual phrasing). Not a sterile "do X to Y."
Bad prompt (too easy, baseline will pass): "Summarize this PDF"
Good prompt (multi-step, skill earns its keep):
I just got a 40-page contract from our vendor (it's at evals/files/vendor_contract.pdf).
Pull out every section that talks about liability or indemnification, group them by which
party they bind, and produce a markdown summary with direct quotes I can drop into our
risk register. The numbering in the contract is inconsistent — some sections use 4.1.a,
others use Roman numerals — so make sure you use the literal heading text, not just the
number.
If a candidate prompt could be answered correctly in a sentence or by a single search, rewrite it or throw it out.
How many prompts
Start with at most 3. Three runs each side (with vs. without) at one iteration is six subagent runs — that's enough signal to drive an iteration without burning tokens. The user can request more later if a particular failure mode isn't being captured. When they do, expand by 1-2 at a time and prefer adding prompts that probe a specific weakness over piling on similar tasks.
Save prompts to evals/evals.json (schema in references/schemas.md). Don't write expectations yet — draft those while the runs are in flight (Step 3).
Step 2 — Spawn all runs in the same turn
For every eval, spawn two subagents in the same turn: one with the skill, one without (or one with the previous version, if iterating on an existing skill). Don't stagger — launch all runs together so they finish around the same time and can be graded together.
With-skill run prompt (template):
Execute this task:
- Skill path: <absolute-path-to-skill>
- Task: <eval prompt>
- Input files: <files from eval, or "none">
- Save outputs to: <workspace>/iteration-<N>/<eval-name>/with_skill/outputs/
- Save your transcript to: <workspace>/iteration-<N>/<eval-name>/with_skill/transcript.md
- Outputs to save: <what the user actually cares about — the final file(s), not scratch work>
Baseline run prompt: identical, but no skill path. For improving an existing skill, snapshot the previous version first (cp -r <skill-path> <workspace>/skill-snapshot/) and point the baseline subagent at the snapshot; save to old_skill/outputs/ instead of without_skill/outputs/.
For each eval, also write eval_metadata.json (expectations can be empty until Step 3):
{
"eval_id": 0,
"eval_name": "extract-liability-sections",
"prompt": "...the full prompt...",
"expectations": []
}
Use the same descriptive name for the directory and eval_name. If this iteration tweaks any prompts, write fresh metadata files — don't assume they carry over from the previous iteration.
Step 3 — While runs are in flight, draft assertions
Don't idle. For each eval, draft 4-7 objectively verifiable assertions. These are what the grader will check.
Good assertions:
"The output groups sections by which party they bind (vendor / customer / mutual)""At least 5 distinct sections from the input PDF appear as direct quotes in the output""The skill's pdf-extraction script was invoked at least once (visible in transcript)"
Bad assertions (these read as passes regardless of skill):
"The output is a string" (always true)"The output is well-written" (subjective, push to qualitative review instead)
If evals.json already had assertions, review them and keep only the discriminating ones. Save the final list back into eval_metadata.json and evals.json.
Subjective qualities (writing voice, design taste) belong in qualitative review, not assertions. Don't force them.
Step 4 — Capture timing as notifications arrive
Each subagent task notification contains total_tokens and duration_ms. This is your only chance to capture this data — it isn't persisted anywhere else. As each notification arrives, write timing.json into the corresponding run directory:
{ "total_tokens": 84852, "duration_ms": 23332, "total_duration_seconds": 23.3 }
Process notifications as they arrive; don't try to batch.
Step 5 — Grade with Haiku subagents
Once a run's outputs and transcript are on disk, spawn a Haiku grader subagent for it. Read agents/grader.md and pass it as the subagent's instructions. Use Haiku (model: "haiku") — grading is structured evidence-checking, Haiku does it well, and it keeps the eval loop cheap.
For deterministic assertions ("file X exists with mime type Y", "transcript contains string Z"), prefer writing a tiny Python script over having the grader eyeball it — scripts are faster, free, and reusable across iterations. The grader can call the script and record the result.
The grader writes grading.json into each run directory. The viewer and aggregator depend on the exact field names expectations[].text, expectations[].passed, expectations[].evidence — don't accept renamed variants from the grader.
Step 6 — Aggregate and launch the viewer
Once every run has a grading.json:
python -m scripts.aggregate_benchmark <workspace>/iteration-N --skill-name <skill-name>
(Run from the skill-evaluator directory so scripts/ is on the path.) This produces benchmark.json and benchmark.md — pass rate, time, tokens per configuration with mean ± stddev and the delta. With one run per config per iteration (the default), the ± is eval-to-eval spread across the eval set, not run-to-run variance — read it as "how consistent is the skill across different tasks", not "how noisy is a single task". Order each with_skill run before its without_skill (or old_skill) counterpart in the runs list — the viewer reads order to pair them.
Do a quick analyst pass on benchmark.json:
- Assertions that pass 100% in both configurations — non-discriminating, flag for replacement.
- Evals with high variance — possibly flaky; note it.
- Time/token tradeoffs — does the skill's win on pass rate justify its cost?
Add observations to the notes[] field of benchmark.json so the viewer surfaces them.
Then launch the viewer:
nohup python <skill-evaluator-path>/eval-viewer/generate_review.py \
<workspace>/iteration-N \
--skill-name "<skill-name>" \
--benchmark <workspace>/iteration-N/benchmark.json \
> /dev/null 2>&1 &
VIEWER_PID=$!
For iteration 2+, also pass --previous-workspace <workspace>/iteration-<N-1> so users see prior outputs and feedback inline.
Headless / no-display environments: use --static <output_path> to write a standalone HTML file. Feedback downloads as feedback.json; copy it into the workspace yourself before reading.
Tell the user: "I've opened the results. The 'Outputs' tab lets you click through each test case and leave feedback; the 'Benchmark' tab shows the quantitative comparison. Tell me when you're done."
Step 7 — Read feedback and improve
When the user says they're done, read feedback.json:
{
"reviews": [
{"run_id": "extract-liability-sections-with_skill", "feedback": "missed mutual indemnification clause", "timestamp": "..."},
{"run_id": "extract-liability-sections-without_skill", "feedback": "", "timestamp": "..."}
],
"status": "complete"
}
Empty feedback = the user thought it was fine. Focus on runs with concrete complaints.
When improving the skill, think hard:
- Generalize from the feedback. The skill is going to run against many prompts — don't overfit to these three. If a fix only helps
extract-liability-sections, it's probably the wrong fix.
- Keep the prompt lean. Read the transcripts, not just the outputs. If the skill is making the model do unproductive work, cut the part that caused it.
- Explain the why. Today's models have good theory of mind. Heavy-handed
MUST/NEVER and rigid templates are a yellow flag — reframe as "here's why this matters." That generalizes better than rules.
- Notice repeated work across runs. If all three with-skill runs independently wrote a similar helper script, the skill should bundle that script. Write it once, drop it in
scripts/, point the skill at it.
Then kill the viewer, rerun all evals into iteration-<N+1>/, and loop.
kill $VIEWER_PID 2>/dev/null
Keep going until: the user says they're happy / feedback is all empty / progress has stalled.
Description optimization (deferred — mention on iteration 2+ or last pass)
This skill does not run the description-optimization loop. That's expensive and only worth doing once the skill itself is solid.
After the second iteration (or on what looks like the final pass), surface this to the user:
"The skill's behaviour is in good shape. The remaining lever is the description field in the frontmatter — that's what determines whether Claude actually invokes the skill at the right time. If you want to tune that for trigger accuracy, skill-creator has a full optimization loop (scripts/run_loop.py) that does it. Want me to hand off to that, or are we done?"
Don't pre-emptively run it. Don't generate trigger-eval queries. Just signpost.
Blind comparison (not supported)
Out of scope. If the user asks "is the new version really better?" beyond what with_skill vs. old_skill already shows, point them at skill-creator/agents/comparator.md.
Reference files
agents/grader.md — instructions for the Haiku grader subagentreferences/schemas.md — JSON shapes for evals.json, grading.json, benchmark.json, timing.jsonscripts/aggregate_benchmark.py — produces benchmark.json from a workspace iteration directoryeval-viewer/generate_review.py — launches the qualitative + quantitative viewer
