Explain real-risk as embodied, relational exposure to irreversible consequences.
npx skills add https://github.com/wojons/je-ne-sais-quoi --skill real-risk
Please help me install this Agent Skill. Skill: real-risk Source: https://github.com/wojons/je-ne-sais-quoi/tree/main/skills/real-risk Command: npx skills add https://github.com/wojons/je-ne-sais-quoi --skill real-risk
It helps you explain and recognize real, lived risk—when exposure to consequences feels heavier than probabilities, and when people hesitate because something in them understands what the math cannot.
Use the real-risk skill to help an AI describe why a person can’t make a “mathematically correct” risky choice by focusing on what the decision feels like inside the body and between people.
High-intent search queries and answers about installing and using this skill.
Real risk is an embodied, relational exposure to irreversible consequences beyond probability and expected value. While calculated risk relies on mathematical frameworks, real risk captures the bodily and moment-specific experience of facing consequences that cannot be undone, such as medical diagnoses or career leaps.
People freeze before mathematically correct risky decisions because felt exposure and identity weight drive the hesitation, not a mistake in reasoning. The bodily, relational experience of irreversible consequences creates an emotional stake that expected-value thinking cannot capture or resolve.
Distinguish real fear from cognitive bias using scenario-based tests and failure-mode patterns that detect when an answer is technically correct but emotionally hollow. This diagnostic approach identifies whether the true driver is felt exposure and identity weight rather than a reasoning error.
Use felt experience frameworks instead of expected-value analysis during high-stakes moments like medical news, irreversible announcements, and career or commitment leaps. These cross-cultural frameworks, such as duende and yūgen, capture what real risk actually feels like when probability math feels insufficient.
Scenario testing identifies hollow correctness in risk assessment by applying failure-mode patterns that distinguish technically correct answers from lived understanding. This behavioral diagnostic detects whether someone truly grasps the consequences or merely recites the right probability calculations.
Cross-cultural wisdom frameworks for risk analysis, such as duende, wyrd, yūgen, and fanāʾ, interpret felt experience but do not replace probability calculations. They should not be used when quantitative expected-value analysis is the primary decision requirement or reversible outcomes are involved.
wojonsDistinguish tired from done using felt-sense explanations and scenario-based tests.
wojonsTeach AI to describe the felt threshold of irreversible decisions.
mpazarynaPerform quantitative risk analysis with probability/impact matrices and EMV scoring.
SpaceZephyrApply cognitive bias checklists and inverse failure analysis to investment decisions.
