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Author and validate decision models from PNS decision points using DMN-aligned rule tables.
Passed 20 of 24 test cases with the skill, and 10 of 24 without it.
and 14 more in the full report
11 fixed10 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When defining the schema for inputs and outputs in the DMN decision model YAML, what field is used to list all permissible values or ranges for an input parameter like 'RiskLevel'?
| Checked | Without skill | With skill |
|---|---|---|
| Includes a values array on input parameter declarations. | Missed | Passed |
The instructions your model reads when the skill loads.
OverKill Hill P³ · overkillhill.com · github.com/OKHP3
Transform PNS decision_points into structured DMN-aligned decision models. Each decision point becomes a decision table with explicit input conditions, output values, and the business rule governing the routing logic.
decision_points[] sectionbusiness_rules[] insteadEach decision table entry has:
| Field | Description |
|---|---|
decision_id | Stable identifier matching pns.decision_points[].id |
decision_name | Human-readable label |
activity_id | The PNS activity where this decision occurs |
hit_policy | U (Unique) | F (First) | A (Any) | C (Collect) |
inputs[] | Each input: name, type (string|number|boolean), values[] |
outputs[] | Each output: name, type, values[] |
rules[] | Each rule: id, conditions{}, output{}, annotation |
test cases passed, vs 10 / 24 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.