AC questionnaire-codebook-maker
Turn questionnaire items into clean research codebooks, scoring rules, reverse-scoring checks, variable names, and analysis-ready TSV/Markdown tables.
Turn questionnaire items into clean research codebooks, scoring rules, reverse-scoring checks, variable names, and analysis-ready TSV/Markdown tables.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
edit-residuethe text marks something as outdated (lines 46): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (questionnaire-codebook-maker) differs from the folder (questionnaire-codebook-maker-mia956)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 30 steps
- 100Execution cost. Instruction body is 1039 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- +1No license
- +2Single-language instructions
- +3Description length 150: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.