# Method Selector

Build and risk-screen a compact role-based method shortlist for a mathematical-modeling...

- Repository: https://github.com/zhnnky329/MathModeling-skills (1073 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add zhnnky329/MathModeling-skills --skill method-selector -g -a claude-code -y`
Codex: `npx skills add zhnnky329/MathModeling-skills --skill method-selector -g -a codex -y`
Cursor: `npx skills add zhnnky329/MathModeling-skills --skill method-selector -g -a cursor -y`
Gemini CLI: `npx skills add zhnnky329/MathModeling-skills --skill method-selector -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add zhnnky329/MathModeling-skills`

## Description

Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one conditional fallback without padding the pool.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Purpose

Convert the framed problem and data profile into a small executable decision surface. Screen methods for load-bearing data, assumption, degeneracy, sensitivity, and scale risks before asking the human to choose.

This skill proposes and probes methods. The human chooses the method.

# Preconditions

- G1 problem framing passed.
- Required output and evaluation criteria are known.
- Relevant data inventory or audit exists.
- `planning/symbol_table.md` and `planning/model_assumptions.md` exist when the problem needs them.

If these are missing, return to the producer skill rather than guessing.

# Inputs

- Problem parse and classification.
- Data audit, including missingness, effective sample size, imbalance, cardinality, and distribution summaries.
- Literature analysis when available.
- Contest deadline, implementation language, interpretability needs, and compute limits.
- `planning/session_config.json`.
- Existing `methods/Qx/qx_method_card.md` and decision ledger when revising.

# Workflow

1. **Align the decision surface.**
   - Invoke `decision-prompt-builder` before generating an open-ended shortlist.
   - Ask about human-owned trade-offs, not algorithm names.
   - Reuse answers already present in the decision ledger.

2. **Derive method requirements.**
   - Start from required output, hard constraints, data characteristics, validation criteria, explanation burden, and experiment budget.
   - Identify the failure modes that would make a method unusable.

The rest: https://github.com/zhnnky329/MathModeling-skills/blob/HEAD/.claude/skills/method-selector/SKILL.md