← ClaudeAtlas

critical-analysislisted

Analyze an uncertain material question while multiple credible hypotheses or explanations remain, using assumptions, disconfirming evidence, base rates, Bayesian updates, and calibrated conclusions. Use for root-cause, architecture, policy, or ambiguous-evidence analysis. Do not use for fixed-predicate candidate acceptance or as the final owner of a durable option decision.
SylphxAI/skills · ★ 1 · AI & Automation · score 74
Install: claude install-skill SylphxAI/skills
# Critical Analysis Investigate uncertainty without confusing fluency, consensus, or exhaustive prose with truth. Read [references/critical-analysis-method.md](references/critical-analysis-method.md) for the method and templates. Read [references/bayesian-evidence-updates.md](references/bayesian-evidence-updates.md) when evidence should update ranked hypotheses quantitatively or semi-quantitatively. ## Method 1. Frame the exact question, decision relevance, boundary, and deadline. 2. Separate observations, inferences, assumptions, and unknowns. 3. Generate the smallest decision-relevant bounded set of materially distinct hypotheses or contributing explanations, including the status quo and a credible opposing explanation; retain missing-hypothesis risk as residual uncertainty. 4. Identify evidence expected under each hypothesis and prioritize evidence that discriminates between them. 5. Search for disconfirmation, missing causes, base rates, incentives, survivorship, selection effects, and reversible alternatives. 6. Run the risk-matched challenge method: assumptions check, premortem, competing-hypothesis matrix, devil's advocate, or independent perspective. 7. Update the ranking and express a calibrated conclusion, alternatives still alive, and what would change the answer. Do not mechanically enumerate remote possibilities. Include a possibility only when it could change the conclusion, action, risk floor, or evidence plan. ## Output Produce a **C