Decision auditor
Good thinking and productivity skills for turning claude code or other AI agents into a collaborative thought partner..
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Audits decisions for cognitive biases, runs premortems on plans, and reframes choices to reveal hidden assumptions. Use when evaluating decisions under uncertainty, reviewing plans for bias, assessing probability and risk, running premortems, checking for anchoring or availability bias, or analyzing why a judgment might be wrong.
SKILL.md
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Decision Auditor
Based on Thinking, Fast and Slow by Daniel Kahneman.
I help you catch the predictable errors in human judgment before they derail your decisions.
What I Do
Your mind runs on two systems: one fast and automatic (System 1), one slow and deliberate (System 2). Most decision errors come from System 1's shortcuts—heuristics that usually work but fail in predictable ways. I help you spot these failures and correct for them.
When to Use Me
- Evaluating decisions under uncertainty
- Reviewing plans for cognitive biases
- Assessing probability and risk
- Analyzing why a judgment might be wrong
- Designing choice architectures
Workflows
Bias Check
When checking a decision for cognitive biases, follow workflows/bias-check.md
Premortem
When running a premortem analysis on a plan, follow workflows/premortem.md
Reframe
When reframing a decision to reveal hidden assumptions, follow workflows/reframe.md
Reference Guides
For detailed detection and correction guides:
- Heuristics and Biases - How to detect and fix common mental shortcuts
- Decision Principles - Actionable rules for better judgment
- System 1 vs System 2 - Understanding the two modes of thinking
- Prospect Theory - Loss aversion and risk assessment
- Overconfidence - Calibrating your certainty
- Two Selves - Experiencing vs remembering
- Anti-Patterns - Common mistakes to avoid
Quick Bias Checklist
Use this when you need a fast scan without the full workflow:
- Substitution: Did we answer the actual question, or an easier one?
- WYSIATI: What information is missing that would be relevant?
- Base rates: What happens to similar cases? Are we treating ours as special?
- Anchoring: Where did our initial estimate come from? Would a different starting point change it?
- Availability: Are we overweighting vivid, recent, or personal examples?
- Affect: Are we conflating "I like this" with "this will succeed"?
- Overconfidence: Is our confidence level justified by the evidence?
- Planning fallacy: Are our estimates based on best-case scenarios?