The function has built-in tuple normalization support ((result, perf_metrics)), but that normalization is unreachable when normalizer=None because the function raises immediately on the first ValidationError. If raw_result is a legacy (payload, metrics) tuple, it will always fail unless a normalizer is provided. Consider applying tuple normalization before raising (e.g., attempt _normalize_result_from_tuple(raw_result, route_name=...) and re-validate) or treating tuple normalization as the default normalizer when normalizer is not provided.

Mapping every non-ONLINE index state to CREATING can misclassify terminal or error states (e.g., FAILED) as “still creating.” If downstream logic relies on this cache to decide whether to wait/retry/use an index, this can cause incorrect behavior or endless “creating” semantics. Suggest explicitly mapping known states (e.g., POPULATING/ONLINE) and either preserving unknown states in the cache (as a string) or introducing a distinct cache state for failure/unknown so callers can handle it deterministically.

The exists() check + write_text() sequence is not atomic and can race across processes/threads materializing the same bundle directory (e.g., concurrent sessions starting). Consider using an atomic write strategy (write to a temp file then replace) or opening the file in exclusive-create mode so concurrent writers don’t interleave/partially write SKILL.md.

If the cache file exists but is corrupted/truncated, json.loads(...) will raise and break install_skill() entirely. Consider catching json.JSONDecodeError and either treating it as a cache miss (return {}) or raising a clearer error that instructs users how to delete/repair the cache file.

When cache_path is set to a location whose parent directory doesn’t exist, write_text() will fail. Consider ensuring cache_path.parent.mkdir(parents=True, exist_ok=True) before writing so custom cache paths work reliably in CI and container environments.

len(captured.out) >= 0 is always true, so this assertion can never fail (and the test won’t detect regressions). If results can legitimately vary, consider asserting something meaningful (e.g., unique_content in captured.out with retry/backoff; or at least captured.out.strip() != ""; or validating the JSON/line format and presence of any episodes).

When --config is not provided and OPENAI_API_KEY is unset, openai.OpenAI(api_key=None) will likely fail later with a less actionable error. Consider validating the env var here and raising a clear exception (e.g., “OPENAI_API_KEY must be set when --config is not provided”) so benchmark runs fail fast with an actionable message.

json and field are imported but not used in this module. Removing unused imports will keep lint/type-check output clean and avoid confusion about intended functionality.

async_with is imported but not used in the updated script. Removing it will avoid unused-import lint failures and keep the benchmark runner minimal.