Improvement Evaluator

This evaluator appears to implement the stated function, but take these precautions before installing or running it: - Expect the evaluator to call the external claude CLI by default (unless you pass --mock). The registry metadata did not declare this binary dependency — ensure you only run it where 'claude' is intentionally available. - The runner reads SKILL.md (candidate or baseline) from disk and prepends it to prompts sent to the LLM. Do NOT run this on machines containing secrets or private files you wouldn't want to send to an external LLM; validate the 'target' paths the orchestrator will provide. - Pytest-based judges execute test code (via pytest) against AI outputs. Review any fixture/test files included in task suites to ensure they don't run malicious code or perform unwanted side effects. The PytestJudge includes checks that test_file must start with 'fixtures/' and resolves under the skill's tests/fixtures directory, but your task suites' fixture layout must match the implementation; otherwise tests may fail or be ignored. - If you need to run evaluations in CI or on sensitive data, use the --mock flag to avoid calling the claude CLI and to get deterministic, safe behavior. - Consider asking the skill publisher to: (1) declare the 'claude' dependency in metadata; (2) document explicitly that SKILL.md and candidate skill contents will be sent to an external LLM; (3) clarify where fixtures should live so pytest judges operate on the intended test files. If you can, audit the task suites you intend to run and run the evaluator in a restricted environment (no sensitive files, network-restricted or using --mock) until you are comfortable with its behavior.