ForgingStation

Weighted Decision Simulation — Development skill for Claude Code

Development community

A Claude Code skill for modeling weighted, multi-factor decisions under uncertainty (e.g. buy-vs-rent, stocks, investments) via scenario/Monte Carlo simulation, sensitivity analysis, and backtested fo.

How to install Weighted Decision Simulation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open ForgingStation/weighted-decision-simulation and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Weighted Decision Simulation does

A Claude Code skill for modeling weighted, multi-factor decisions under uncertainty (e.g. buy-vs-rent, stocks, investments) via scenario/Monte Carlo simulation, sensitivity analysis, and backtested forecasts - engine stays generic, decisions live in config.

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README

Weighted Decision Simulation

A Claude Code skill for working through decisions that involve comparing a few discrete options, multiple weighted criteria, and uncertainty about the future. Examples: choosing between job offers, deciding how to allocate savings across investment options, or picking between competing plans where cost, risk, and personal priorities all pull in different directions.

Instead of a one-off script or spreadsheet, the skill sets up a small project with a clear split: a generic simulation engine, and a set of config files that describe your specific decision. Ask a new question by editing config.

What it does

Given a decision with multiple options, weighted criteria, and a time horizon, the skill:

  • builds an outcome grid across every option and every scenario (or Monte Carlo draw), instead of collapsing straight to one number
  • sweeps decision variables you specify, such as a contribution level or a term length
  • scores options against your weighted criteria (financial return, risk, convenience, or whatever applies to your decision)
  • runs sensitivity analysis and reports switching thresholds, for example "B overtakes A if its growth exceeds 4.1%/yr"
  • backtests any forward-looking prediction against historical data before reporting it as trustworthy, or marks it UNVALIDATED
  • keeps a history of past runs so later questions can be compared against earlier findings

Requirements

  • Claude Code
  • Python 3 with pytest for the generated project's test suite

How to use it

  1. Open this folder in Claude Code.
  2. Give Claude a prompt describing the decision you want to work through. See RUN_PROMPT.md for an example.
  3. Claude reads SKILL.md and follows its workflow: survey any existing config, gather missing facts, write config/question.json, build only the engine capability the question needs, validate, run, and report back using a fixed ou