evaluating-trade-offs

Installation
Summary

Structured frameworks for evaluating competing options and making clearer trade-off decisions.

  • Applies mental models from 40 product leaders covering decision context, constraint identification, cost quantification, and framework selection
  • Core principles include optimizing for order-of-magnitude over precision, applying the "would I start this today?" test to avoid sunk cost fallacy, and using weighted criteria matrices for multi-factor decisions
  • Helps surface hidden costs like maintenance burden and second-order effects, and identifies who wins and loses from each option
  • Includes decision tenets to eliminate recurring debates and guidance on separating feasibility ("can") from strategy ("should")
SKILL.md

Evaluating Trade-offs

Help the user make clearer decisions between competing options using frameworks and mental models from 40 product leaders.

How to Help

When the user asks for help evaluating trade-offs:

  1. Understand the decision context - Ask what they're optimizing for (short-term vs. long-term, growth vs. quality, speed vs. thoroughness) and what makes this decision difficult
  2. Identify the real constraints - Help distinguish between actual constraints and assumed ones. Ask "What would you do if [constraint] weren't an issue?"
  3. Surface hidden costs - Help quantify the full cost of each option, including maintenance burden, opportunity cost, and second-order effects
  4. Apply the right framework - Use weighted criteria matrices for complex multi-factor decisions, or simple "would I start this today?" tests for continuation decisions

Core Principles

Optimize for order-of-magnitude, not precision

Alex Komoroske: "It doesn't really matter if it's 1,000 or 1,001, who cares? It's orders of magnitude larger than the alternative, and so it is better." Don't waste effort on false precision in uncertain environments - focus on whether one option is dramatically better, not marginally better.

Apply the "would I start this today?" test

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1.2K
GitHub Stars
890
First Seen
Jan 29, 2026