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Found 10 Skills
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
Adds documents to golden dataset with validation. Use when curating test data or saving examples.
Use to detect and remove cognitive biases from reasoning. Invoke when prediction feels emotional, stuck at 50/50, or when you want to validate forecasting process. Use when user mentions scout mindset, soldier mindset, bias check, reversal test, scope sensitivity, or cognitive distortions.
Daniel Kahneman's Cognitive Diagnostic applied to a decision, strategy, or business evaluation. Spawns a team of specialist agents — System Detector, Substitution Mapper, Prospect Theorist, Noise Auditor, Outside Viewer — who each apply a different lens from Kahneman's cognitive architecture to audit the decision for bias, noise, and cognitive traps. The lead synthesizes into a contamination assessment: which cognitive systems are operating, which substitutions are active, and whether the decision should proceed, be corrected, or be restructured. Use when the user says "kahneman this", "check my thinking", "am I biased", "audit this decision", "what am I missing", or presents any decision, strategy, or evaluation they want cognitively stress-tested. Works standalone or as a companion to /munger (Munger evaluates the business; Kahneman audits the thinking about the business).
Verify statistics from raw data with methodology checking, significance testing, claim validation, and bias detection. Use when fact-checking statistical claims, validating research findings, or auditing data analysis.
Concise, structured summaries of news articles (~30 sec read time). Captures key points, context, bias/gaps, and open questions. Use when user shares article URL or asks to summarize news content.
Audits AI systems for bias, fairness, and privacy. Analyzes prompts and datasets to ensure ethical and safe AI implementation.
Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.
Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.
Responsible AI development and ethical considerations. Use when evaluating AI bias, implementing fairness measures, conducting ethical assessments, or ensuring AI systems align with human values.