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Aggregate probability judgments across perspectives using Real-Time Delphi or prediction market mechanisms.
Passed 20 of 21 test cases with the skill, and 11 of 21 without it.
and 13 more in the full report
10 fixed11 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which available tactic handles executing a single complete Delphi revision round—collecting judgments, distributing anonymous feedback, measuring consensus, and making the round decision?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies iterative-convergence-round as the tactic for executing a Delphi round | Missed | Passed |
The instructions your model reads when the skill loads.
Purpose: Aggregate probabilistic forecasts from multiple perspectives into calibrated predictions. Uses Real-Time Delphi continuous updating or prediction market scoring to produce well-calibrated probability estimates for future events.
When to use:
| Parameter | Constraint |
|---|---|
| Rounds | 2–3 (continuous updating preferred) |
| Perspectives | ≥4 independent forecasters |
| Calibration target | Brier score improvement across rounds |
| Key | Type | Description |
|---|---|---|
| question | string | The forecasting question |
| time_horizon | string | When the event would resolve |
| perspectives | array | List of forecaster perspectives |
| rounds | array | History of probability estimates |
| current_aggregate | float | Current aggregated probability |
| calibration_metrics | object | Brier score, log score tracking |
test cases passed, vs 11 / 21 without
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