Appropriateness Bounding

SkillDev tools

Establish acceptability standards through RAND/UCLA Appropriateness Method

Available today. Use it from your connected AI after setup.

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

ParameterConstraint
Rounds2 (rate → discuss → re-rate)
Perspectives≥4 (ideally 7–15 for RAND/UCLA)
Rating scale1–9 (inappropriate to appropriate)
Agreement thresholdMedian ≥7 without disagreement

State Ledger

KeyTypeDescription
indicationsarrayList of scenarios to rate
perspectivesarrayPanel member perspectives
round_1_ratingsarrayInitial ratings per indication
discussion_notesstringKey points from discussion
round_2_ratingsarrayPost-discussion ratings
classificationsobjectAppropriate/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

  1. Define indications/scenarios clearly (clinical scenarios, use cases)
  2. Collect Round 1 ratings (1–9 scale) with brief rationale
  3. Distribute feedback showing distribution of ratings
  4. Facilitate structured discussion of disagreements
  5. Collect Round 2 ratings
  6. Classify each indication: appropriate (median 7–9), uncertain (4–6), inappropriate (1–3)
  7. 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.

TacticWhen to use
iterative-convergence-roundExecute one full Delphi round — collect judgments, distribute anonymous feedback, measure consensus, decide whether to continue.
threshold-calibrationSystematically 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.

SOPWhen to use
consensus-synthesisSynthesize 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