critical-case-design
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/critical-case-designMaximizes inferential power by testing claims with critical cases
- Evaluates claims by designing most- and least-likely cases
- Uses parameter-space-mapping and extreme-value-generation tools
- Applies Flyvbjerg methodology to identify favorable and unfavorable conditions
- Reports confirmation or disconfirmation based on breakpoint and contradiction detection
SKILL.md
.github/skills/critical-case-designView on GitHub ↗
--- name: critical-case-design description: "Flyvbjerg critical case methodology: select most-likely and least-likely cases to maximize inferential power." type: strategy used-by: [adversarial-stress-testing] --- # Critical Case Design ## Tactics - boundary-probing - counterexample-heuristics ## Method 1. Identify the claim's scope and conditions 2. Design most-likely case: conditions maximally favorable to claim 3. Design least-likely case: conditions maximally unfavorable 4. If claim fails in most-likely case: strong disconfirmation 5. If claim holds in least-likely case: strong confirmation 6. Report inferential power of each test ## Budget | Size | Critical cases designed | Evaluation depth | |---|---|---| | S | 2 (one most-likely, one least-likely) | Surface | | M | 4 (two each) | Moderate | | L | 6 (three each) | Deep | ## Orchestration 1. Dispatch `parameter-space-mapping` to identify conditions 2. Dispatch `extreme-value-generation` for favorable/unfavorable extremes 3. Dispatch `breakpoint-detection` to test claim at critical cases 4. Dispatch `contradiction-detection` to evaluate outcomes ## Subagents - parameter-space-mapping - extreme-value-generation - breakpoint-detection - contradiction-detection