Individual skill
method-selector
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.
plugins/mathmodeling-skills/skills/method-selector/SKILL.md
Source commit 0b46e9c78335624ed3738b86087b6e8ff9ac1f0d
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Local scripts
- Last checked
- Nov 10, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,086
- Repository last updated
- Sep 24, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 6,362 bytesreferences/method-family-guide.mddoc · 2,209 bytesreferences/risk-probe-contract.mddoc · 3,070 bytes
Read excerpt
# 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.