Turn raw test results into readable reports with pass and fail totals, unstable tests, and coverage, meaning how much code tests exercise. Package results for releases, quality assurance, or reviews, with follow-up actions.
Generate structured test reports with pass/fail rollups, coverage summaries, and test artifacts. Use when the user is asking for test-result packaging or delivery, not for root-cause debugging or feature implementation.
Review the instructions the agent will receive before you install.
Use this skill when the next deliverable is a readable test report rather than another round of debugging.
Use this skill when:
pytest, JUnit, or coverage artifacts into a concise reportsystematic-debuggingcode-reviewertdd-guidesystematic-debugging before report packaging if failures are still unexplainedverification-before-completion when the report is part of a completion gateNo reviews yet. Tell others what you thought.
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Support experimental validation of designed proteins through a cloud laboratory. Prepare and optimize sequences, submit tests for binding, expression, thermal stability, or enzyme activity, track experiment progress, and retrieve results through the API.
Analyze time series, observations ordered over time, using aeon in Python. Build classification, prediction, and clustering workflows, detect unusual patterns or changes, and compare methods through an interface compatible with scikit-learn.
Create original generative art with p5.js, using code to produce patterns and motion. Receive a written artistic concept and an interactive HTML viewer with adjustable parameters, reproducible random variations, seed navigation, and PNG downloads.
Retrieve stock prices, exchange rates, cryptocurrency prices, company financial statements, and economic indicators through the Alpha Vantage API. It also covers historical data, news sentiment, and technical indicators, which summarize price and trading patterns.
Design backend systems with clear boundaries between business rules, databases, and external services. Use Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to organize new applications, refactor existing code, and make core logic easier to test.
Helps process astronomical data in Python by converting coordinates, units, and time scales, working with FITS observation files, matching object catalogs, and calculating distances using cosmological models.