BD conjoint-design
Designs conjoint and factorial-vignette experiments end to end — attribute architecture and randomization restrictions, effective-N power from the closed-form AMCE standard error, treatment realism, estimand choice among AMCE, marginal means, and AMIE, design variants such as forced choice versus rating, PAP tiers for conjoint flexibility, and the regression models and R packages that implement each. Use when the user is planning a conjoint, drafting or critiquing an attribute table, asking how many respondents or tasks are needed, asking whether to report AMCEs or marginal means, or asking how to test interactions. Reviewing an existing design goes to conjoint-diagnostics, cleaning the export to conjoint-cleaning.
Designs conjoint and factorial-vignette experiments end to end — attribute architecture and randomization restrictions, effective-N power from the closed-form…
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 9630 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 40Execution cost. Instruction body is 9630 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 87 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 724: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 87 items
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.