plastic-skill-evaluating

# Evaluating Skills Eval methodology for Plastic skills, based on [agentskills.io](https://agentskills.io/skill-creation/evaluating-skills) and [Anthropic's eval guide](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents). ## Gotchas - Assertions written before observing output are almost always wrong — run the eval first, observe actual output, THEN write assertions - Near-miss negative test cases are the most valuable — prompts that share keywords with should-trigger cases but need a different skill entirely - Select the best skill iteration by validation pass rate, not the last one - Grade outcomes, not execution paths — if the agent solved the task via an unexpected route but produced correct output, that is a pass - Same skill can behave differently across agent frameworks — test on each target agent (Claude Code, Hermes, OpenClaw, Codex) ## Procedure ### Step 1: Choose eval scope Determine what you are evaluating: - **Description triggering** — does the agent activate the right skill for a given prompt? Tests the description field effectiveness. - **Output quality** — does the skill produce correct results when activated? Tests the skill body and references. - **Convention compliance** — does the output follow Plastic conventions? Read `references/convention-checks.md` for the full assertion library. Multiple scopes can apply to the same skill. Start with the scope that addresses your immediate concern, add others as needed. ###...