Skill Creator helps authors and teams design, test, and refine Agent Skills. It provides a step-by-step process for capturing intent, drafting SKILL.md metadata and body, creating realistic test cases, launching parallel evaluation runs (with and without the skill), and producing a reviewable benchmark. The skill also includes tooling guidance for packaging, viewer generation, and iterating until the skill reliably triggers and performs well.
Use Skill Creator when you need to: create a new Agent Skill from a concept; improve an existing skill's instructions or trigger description; build reproducible test cases and quantitative benchmarks; or run a description-optimization loop to improve invocation accuracy. It's for authors who want an end-to-end workflow (draft → test → review → improve).
Designed for multi-agent/test harness environments and authoring workflows that support spawning subagents and running parallel evaluations. Works with environments that can run Python-based viewers and scripts (e.g., Claude Code / Cowork-like systems).
A comprehensive skill-creator skill for authoring, testing, evaluating, and iterating on Agent Skills. Includes an eval framework with benchmarking, blind comparison, and description optimization loop. Scripts are well-written with argparse, docstrings, and error handling, but most fail when extracted to a temp dir due to cross-module imports using `from scripts.utils import ...` instead of relative imports. Only utils.py ran cleanly; the CLI entry points (aggregate_benchmark, generate_report) properly validate args but need input data.
Import path issue is a packaging/portability problem, not a security concern. The skill is from the Anthropic Sage project — high-quality, well-documented. The description is intentionally verbose/pushy per their design philosophy for triggering. The eval framework (run_eval, run_loop, improve_description) is sophisticated and well-architected.