BC Calibrate
Calibrates any eval against the principal's own taste: takes a task in plain words, finds the eval suites and targets that cover it and offers them as options, then shows N real outputs (images or text, default 16, any count like 4, 24 or 64) on a local review page, collects a 0–10 score per output plus optional typed notes, measures how well the judge agrees, distills a taste profile the judge reads from then on, and rescores to prove the change. USE WHEN calibrate, calibrate 16, calibrate 64, calibrate social posts, calibrate the art judge, calibrate this eval, show me outputs to rate, rate these images, teach the eval what I like, tune the eval to my taste, what does the judge get wrong about my taste, calibration session. NOT FOR measuring a suite's judge against existing labels without a session (Assay Calibrate), recording one verdict on one delivered artifact (ArtDirection Calibrate), or producing the artifacts themselves (Art).
Calibrates any eval against the principal's own taste: takes a task in plain words, finds the eval suites and targets that cover it and offers them as…
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
frontmatter-keyunknown frontmatter key "evals"
Process rating: all ten parameters 57/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 6 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2275 tokens
- low The response is described with custom markup (5 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +3Description length 949: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.