AC hypothesis-building
Turns theory into falsifiable, pre-registerable hypotheses. Covers DAGs and backdoor closure, design resolution of the FPCI, SATE versus PATE, counterfactual and directional framing, named theoretical and empirical estimands, justified SESOIs, NHST, interval, equivalence (TOST), and minimum-effect tests, scope conditions, and primary, secondary, and exploratory tiers across multi-experiment designs. Use when the user asks to turn a research question or theory into hypotheses, asks whether a prediction is falsifiable or testable, what the estimand is, or how to predict a null. Framing goes to narrative-building, planning to pre-registration-writing.
Turns theory into falsifiable, pre-registerable hypotheses.
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: 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 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 4053 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 656: 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.