Write tests that are (a) high-signal and not fragile, and (b) structured with standard pytest mechanisms. Apply: - Use fixtures instead of module/global clients, so each test has clean, controllable dependencies. - For new externally observable behavior, add a targeted test. If the output is complex, use a snapshot/recorded example. - For internal “plumbing” where deep inspection isn’t possible, start with a smoke test (no exceptions) and optionally mock the dependency to assert that key arguments are passed. - Use `pytest.MonkeyPatch.context()` (or equivalent) to ensure patches clean up at the right scope. - Skip conditionally with `pytest.mark.skipif(...)` instead of early-return branches. - Assert only the behavior that matters; avoid extra checks that are likely to be stripped/modified by proxies (e.g., don’t add confusing header assertions when you only need to verify `Authorization`). Example (focused assertion + fixture-friendly structure): ```python import re import pytest import respx import httpx from unittest.mock import Mock @pytest.mark.respx() def test_bearer_token_env_passes_authorization(monkeypatch: pytest.MonkeyPatch, respx_mock): respx_mock.post(re.compile(r"https://bedrock-runtime\.us-east-1\.amazonaws\.com/model/.*/invoke")).mock( return_value=httpx.Response(200, json={"ok": True}) ) monkeypatch.setenv("AWS_BEARER_TOKEN_BEDROCK", "test-bearer-token") # exercise code that triggers the request # sync_client.messages.create(...) calls = list(respx_mock.calls) assert len(calls) == 1 assert calls[0].request.headers.get("Authorization") == "Bearer test-bearer-token" ``` This approach keeps tests reliable, maintainable, and valuable as the code evolves.