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Runs Python unit tests with coverage, analyzes coverage reports, and implements meaningful tests to increase coverage by ~0.2%.
Passed 8 of 16 test cases with the skill, and 2 of 16 without it.
and 13 more in the full report
9 fixed3 passed either way11 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
An abstract interface method stub in Streamlit contains `raise NotImplementedError`. How should we handle coverage for this abstract definition?
| Checked | Without skill | With skill |
|---|---|---|
| Annotates abstract method stubs with `# pragma: no cover - abstract`. | Missed | Passed |
| Explains that abstract interface stubs do not require unit test coverage. | Passed | Passed |
The instructions your model reads when the skill loads.
Increase Python unit test coverage by ~0.2% through meaningful tests that add real value.
Be fully autonomous — Do NOT stop or pause to ask for confirmation. Keep iterating (analyze → implement → verify) until the 0.2% coverage target is reached. If you encounter ambiguities about what to test, make a reasonable choice and proceed.
Step 1: Run tests with coverage
make python-tests # ~3 min, creates .coverage file
Generate JSON report for analysis:
uv run coverage json -o coverage.json
The JSON contains per-file missing_lines arrays showing uncovered line numbers.
Step 2: Analyze and prioritize
Read coverage.json to find files with:
percent_covered (high impact)lib/streamlit/elements/ or lib/streamlit/runtime/Skip: >97% coverage, proto/*, vendor/*, static/*, test files.
Step 3: Implement tests (in subagent)
Launch a subagent to implement tests for each prioritized file. Provide the subagent with:
missing_lines from coverageThe subagent should:
lib/tests/streamlit/<path>/<module>_test.pytest cases passed, vs 2 / 16 without
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