AC hypothesis-building
Turns a theory into falsifiable, pre-registerable hypotheses — DAGs and backdoor closure, how the design resolves the FPCI, SATE versus PATE, counterfactual and directional framing, a named theoretical and empirical estimand, a justified SESOI, the choice among NHST, interval, equivalence (TOST), and minimum-effect tests, scope conditions, and primary, secondary, and exploratory tiers across multi-experiment designs. Use when the user has a research question or theory and asks to turn it into hypotheses, asks whether a prediction is falsifiable or testable, asks what the estimand is, or asks how to predict a null. Framing the paper around it goes to narrative-building, the plan to pre-registration-writing.
Turns a theory into falsifiable, pre-registerable hypotheses — DAGs and backdoor closure, how the design resolves the FPCI, SATE versus PATE, counterfactual…
As a process C 60/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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: 3. 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 60/100
- 0Result and completion. Does not say what the result is
- 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
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4054 tokens
- 85Steps. 58 steps, 3 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
- +1No license
- +2Single-language instructions
- +3Description length 715: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 58 items
- +4Has examples (0 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.