BC pre-registration-writing
Writes a pre-analysis plan before data collection — registry selection (OSF, AsPredicted, AEA, EGAP), PAP document structure, an analytical strategy specified down to the model and the decision rule, analysis code pre-registered against simulated data, contingency planning for attrition, failed manipulations, and exclusions, deviation documentation, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to write or review a pre-registration or PAP, asks which registry to use, asks what to lock down versus leave exploratory, or asks how to handle a deviation later. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.
Writes a pre-analysis plan before data collection — registry selection (OSF, AsPredicted, AEA, EGAP), PAP document structure, an analytical strategy specified…
As a process C 58/100 · 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 58/100
- 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. 9 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 4177 tokens
- 85Steps. 46 steps, 2 vague phrases
- 100Tools and files. No external tools needed
- 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 706: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 46 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.