Appropriateness Bounding
SkillDev toolsEstablish acceptability standards through RAND/UCLA Appropriateness Method
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Appropriateness Bounding skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/appropriateness-bounding/SKILL.md and read by ahel’s review.
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:
- Medical guideline development (appropriate indications)
- Regulatory standard setting
- Establishing acceptable thresholds for action
- Any question of the form "is X appropriate when Y?"
Budget
| 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 |
State Ledger
| 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 |
Available Tactics
- iterative-convergence-round — Two-round rate-discuss-rerate cycle
- threshold-calibration — Determine where appropriateness boundaries fall
Available SOPs
- judgment-collection
- feedback-distribution
- consensus-measurement
- round-decision
- threshold-sweep
- consensus-classification
- consensus-synthesis
Execution Guidance
- Define indications/scenarios clearly (clinical scenarios, use cases)
- Collect Round 1 ratings (1–9 scale) with brief rationale
- Distribute feedback showing distribution of ratings
- Facilitate structured discussion of disagreements
- Collect Round 2 ratings
- Classify each indication: appropriate (median 7–9), uncertain (4–6), inappropriate (1–3)
- Flag items with disagreement (where panel lacks agreement despite median)
Output Format
classifications:
appropriate: [{indication, median, agreement_level}, ...]
uncertain: [{indication, median, agreement_level}, ...]
inappropriate: [{indication, median, agreement_level}, ...]
disagreement_items: [{indication, reason}, ...]
panel_size: <int>
method: RAND/UCLA | Consensus Conference
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| iterative-convergence-round | Execute one full Delphi round — collect judgments, distribute anonymous feedback, measure consensus, decide whether to continue. |
| threshold-calibration | Systematically sweep consensus thresholds to observe which items achieve consensus at what level, producing a threshold-consensus curve. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| consensus-synthesis | Synthesize all rounds into a final consensus report documenting agreements, dissent, and process. |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
appropriateness-bounding- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine