trainer-election

name	trainer-election
description	Elect the strongest prompt or skill candidate from an existing evaluation workspace. Use this skill whenever a workflow already has multiple scored configurations and needs a separate leader-selection pass over grading, timing, or benchmark artifacts, especially when comparing optimize outputs without pushing that selection logic back into trainer-optimize.
license	MIT
compatibility	Requires Python 3.11+. Reads eval workspaces that follow the Agent Skills evaluation layout with eval metadata, grading.json, timing.json, and optional benchmark.json artifacts.
metadata	{"author":"your-org","version":"0.1.0"}

Election

Use this skill to elect a winner from existing evaluation artifacts. Treat it as the standalone selection step after candidates have already been run and graded. It does not generate candidates, perform research, synthesize evals, or re-run optimization.

When to use this skill

• A workflow already produced multiple candidate configurations and now needs a winner chosen from scored artifacts.
• Multiple candidate configurations already exist as with_skill, without_skill, old_skill, or other config directories inside a skill-eval workspace.