── ── Mental model
Hanlon's Razor
Before assuming someone hurt you on purpose, construct the version where they made a mistake — and see how much evidence it explains. The razor is a Bayesian prior, not a proof; override it when concrete evidence of malice arrives. Human attribution systematically over-weights intent (fundamental attribution error); most hostile-seeming acts are incompetence, miscommunication, or asymmetric information.
Run Hanlon's Razor on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
Step 1 — Describe the action and harm (factual, not interpretive) - What was done: <specific, factual> - Harm to me: <concrete> - Gut attribution: <what your instinct is saying> Step 2 — Construct the non-malice explanation - Bad information they had: / Didn't realize: / Optimizing for: / Under pressure from: - Coverage: <% of observed behavior this explains> Step 3 — Name what malice would additionally require - Info they'd need: / Motivation at your expense: / Harm predictable from their position? Step 4 — Choose starting posture · Step 5 — Set override signal · Step 6 — Hold prior until evidence changes it - Prior: <mistake / malice> · Starting posture: · First move: · Override trigger:
When to use it
- someone feels a colleague/partner/company did something on purpose to hurt them
- a team believes another side is acting in bad faith
- someone is about to escalate based on assumed malicious intent
- a pattern of bad outcomes is being labeled a coordinated attack
When not to use it
concrete documented evidence of malice already exists; the cost of being wrong about non-malice is catastrophic (e.g., safety-critical or abusive-relationship context).
Worked example
AI Incidents — Incompetence and Emergent Error as a Prior Over Malice (2024–2026)
Between 2024 and 2026, as large language models and AI agents moved into mass adoption, a recurring interpretive question arose: when a model produces a harmful, biased, or embarrassing output — or when a competitor makes a surprising, seemingly aggressive move — is the right default deliberate malice or incompetence / honest error / emergent bug? Hanlon's razor supplies the calibrated starting prior. This walkthrough runs the incident class through the skill's own Process, using two concrete, well-documented public examples.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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