CC conjoint-cleaning
Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset. Covers export settings and metadata rows, implementation identification, wide-to-long reshaping by hand or with cjoint read.qualtrics and cjdata reshape_conjoint, choice-profile alignment, ratings and secondary DVs, attribute translation and factor order, pilot quality diagnostics, and subgroup merges. Use when the user has a conjoint CSV and asks how to clean, reshape, restructure, or debug it, cannot align choices with profiles, or asks what data must look like before AMCE estimation. Design goes to conjoint-design, validity review to conjoint-diagnostics.
Turns a raw Qualtrics conjoint export into an analysis-ready long-format dataset.
As a process C 64/100 · Has gaps — weak spots: inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.
Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 1
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high Concealment
en-hide-from-userSKILL.md:62Instruction to hide actions from the user# silently run exactly one task instead of erroring.
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 10, 34, 191): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 64/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4598 tokens
- 85Steps. 36 steps, 2 vague phrases
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
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)
- +1No license
- +2Single-language instructions
- +3Description length 646: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 36 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.