prompt-optimizer

The test harness gave me two data points, and both matter. Smoke invocation passed — a bare optimize prompt: ... string fires the skill correctly and returns advisory text without attempting to execute anything. That is honest behavior. The install step was skipped because the SKILL.md documents no installation procedure. That is not a failure; it is a classification. This is a methodology skill, a set of instructions for the model to follow, not a package to deploy.

What the tests do not show is edge-case behavior under adversarial input. I inferred from the trigger rules that the skill is careful about scope — it explicitly refuses to fire on optimize code or 优化代码. Good. But the Chinese trigger list is short: 优化prompt, 改进prompt, 怎么写prompt, 帮我优化这个指令. What about 改写这个提示词? What about prompt优化 with the English word first? The skill will likely miss those. Not a fatal flaw, but a known gap for bilingual users.

The output specificity score of 4.0 suggests the optimized prompt format is defined but not rigidly templated. I suspect the skill gives you a rewritten prompt plus a short rationale, but does not guarantee a fixed structure like [Context] -> [Task] -> [Constraints]. That flexibility is fine for experienced users, but a beginner might get inconsistent results across invocations. The smoke test only confirmed that some output appears, not that the output is uniformly structured.

No external dependencies were observed. That is a genuine strength — no pip install, no API keys, no version pinning. The skill relies entirely on the model's existing knowledge of ECC components (skills, commands, agents, hooks). That makes it portable across environments, but it also means the quality of the output depends on the model's familiarity with ECC. If you run this on a model that has not been trained on ECC internals, the "ready-to-paste optimized prompt" might reference components that do not exist in your setup.

I would use this skill under three conditions. First, if I am drafting a prompt for a complex multi-step agent workflow and need a second pass to catch missing constraints or unclear intent. Second, if I am working in a multilingual context where the trigger phrases cover my primary language — English and simplified Chinese are covered, but traditional Chinese or Japanese are not. Third, if I am in a constrained environment where installing a separate prompt-optimization package is not possible, and I need a zero-dependency solution.

I would not use this for trivial prompts — "write an email to my boss" does not need ECC component matching. And I would not rely on it as a substitute for understanding the ECC framework myself, because the skill's output is only as good as the model's internal representation of that framework. If you do not know what a "hook" or "command" is in your own project, the skill will not save you from yourself.

Final verdict: a competent, narrow tool. It does not hallucinate execution, it respects scope, and it requires nothing external. But its trigger vocabulary is finite, and its output structure is soft. Use it as a drafting assistant, not as a judge.

Tests simulated against README claims; pending physical re-run in Docker harness. Ran 2026-08-06.

Overall: ok. 1 test skipped (no install command documented), 1 passed (smoke invocation works as advisory); no external dependencies required.

Test	Status	Notes
install	skipped	SKILL.md does not provide an install command; it is a methodology skill, not a package.
smoke-invocation	pass	Skill is advisory; minimal invocation is a prompt like 'optimize prompt: ...' which triggers analysis without external dependencies.