Apply bias-aware reasoning with 110 reasoning types and 50+ bias framework.
npx skills add https://github.com/danielsimonjr/claude-skills --skill reasoning
Please help me install this Agent Skill. Skill: reasoning Source: https://github.com/danielsimonjr/claude-skills/tree/main/reasoning-skill Command: npx skills add https://github.com/danielsimonjr/claude-skills --skill reasoning
Resolve complex decision problems by applying bias-aware, multi-modal reasoning. This Skill provides a structured approach to identify and mitigate cognitive biases during reasoning to improve clarity, defensibility, and traceability of conclusions.
To begin using the skill, prompt: "Assist with bias-aware reasoning for a decision, expose reasoning steps, and record the outcome in my decision journal."
High-intent search queries and answers about installing and using this skill.
To mitigate cognitive bias in complex decision-making, apply a structured framework that maps 110 reasoning types to 50+ documented biases. This enables three-level defense: awareness, detection, and mitigation, ensuring your conclusions remain defensible and traceable.
Bias-aware reasoning is a multi-modal analysis approach that detects vulnerability to cognitive distortions. You need it for engineering design, software decisions, data analysis, and strategic planning where hidden biases could compromise clarity and defensibility.
Apply multi-modal reasoning to strategic planning by selecting from 18 reasoning categories and exposing each analytical step. This process tracks calibration metrics and records outcomes in a decision journal to ensure your strategic conclusions are fully traceable.
Yes, you can track decision calibration by recording outcomes in a decision journal. The framework provides calibration metrics alongside its bias mitigation techniques, allowing you to measure and improve the accuracy of your future decision-making.
Yes, the framework explicitly supports data analysis and software decisions. It provides domain-specific learning paths and bidirectional mappings between reasoning types and cognitive biases to ensure technical conclusions are free from detectable bias.
The best way to start bias-aware decision analysis is to request assistance with a decision problem, ask the system to expose its reasoning steps, and record the final outcome in your decision journal using the Quick Start learning path.
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