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Establish acceptability standards through RAND/UCLA Appropriateness Method or Consensus Conference protocols.
Passed 20 of 21 test cases with the skill, and 11 of 21 without it.
and 13 more in the full report
11 fixed10 passed either way0 still failing1 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.
We need to run a single complete cycle of collecting panel judgments, distributing anonymous rating distributions, calculating panel agreement, and deciding whether to proceed. Which tactic governs this cycle?
| Checked | Without skill | With skill |
|---|---|---|
| Names iterative-convergence-round as the tactic | Missed | Passed |
The instructions your model reads when the skill loads.
Purpose: Determine what is appropriate, acceptable, or indicated for a given context. Uses the RAND/UCLA Appropriateness Method (rating + discussion + re-rating) or Consensus Conference (citizen jury) format to establish boundaries of acceptability.
When to use:
| Parameter | Constraint |
|---|---|
| Rounds | 2 (rate → discuss → re-rate) |
| Perspectives | ≥4 (ideally 7–15 for RAND/UCLA) |
| Rating scale | 1–9 (inappropriate to appropriate) |
| Agreement threshold | Median ≥7 without disagreement |
| Key | Type | Description |
|---|---|---|
| indications | array | List of scenarios to rate |
| perspectives | array | Panel member perspectives |
| round_1_ratings | array | Initial ratings per indication |
| discussion_notes | string | Key points from discussion |
| round_2_ratings | array | Post-discussion ratings |
| classifications | object | Appropriate/uncertain/inappropriate per item |
test cases passed, vs 11 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.