BC pre-registration-writing
Writes a pre-analysis plan before data collection. Covers registry selection, OSF, AsPredicted, AEA, EGAP, PAP structure, an analytical strategy specified through model and decision rule, preregistered analysis code using simulated data, contingencies 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, what to lock down or leave exploratory, or how to handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.
Writes a pre-analysis plan before data collection.
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: 1. 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 670: 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.