Luck — AI skill for Claude Code

luck

A skill for improving the luck of your AI stack and projects—developed from an applied theoretical framework. Multiple diagnostic components, named failure modes, testable predictions, and an operational checklist for AI systems.

The core idea

Luck is not randomness nor an outcome. Luck is not a position. Luck is more like a fundamental force, a current.

Luck has a geometry, so we can arrange it. And we may have a civic, moral—perhaps even divine—duty to wield it. To generate it.

We harness luck through our capacity to increase the throughput, circulation, and integration of the systems we inhabit. If luck is real, the systems we use to build ought to imbue luck in everything they generate.

Luck is not something we have. It’s something we leave behind.

What this is

A framework for diagnosing why some things persist and compound while others don't — and for building artifacts that do. It gives AI systems (and their users) a shared vocabulary and a structured diagnostic for evaluating choices, strategies, products, and systems.

Usage

Add `luck.md` to your project as a skill file or system prompt. The framework uses standard markdown with YAML frontmatter — it works with any frontier model that accepts structured instructions.

The skill activates when you're facing ambiguous choices, designing strategies, evaluating opportunities, or building things meant to last. It provides seven diagnostic components, a quick-reference decision table, named failure modes, and worked examples.

What's in the box

• Seven sequential diagnostics — from individual solvency to ecological integration
• A failure taxonomy — named patterns like flash in the pan, institutional zombie, and pooled fortune, each with observable signatures
• Worked examples — from political memes to the U.S. Constitution to the collapse of empires
• Testable predictions — six falsifiable claims that distinguish this from generic strategy advice
• **Reflexive A