Improve test coverage in the OpenAI Agents JS monorepo: run `pnpm test:coverage`, inspect coverage artifacts, identify low-coverage files and branches, propose high-impact tests, and confirm with the user before writing tests.
npx skills add https://github.com/openai/openai-agents-python --skill test-coverage-improver
Use this skill whenever coverage needs assessment or improvement (coverage regressions, failing thresholds, or user requests for stronger tests). It runs the coverage suite, analyzes results, highlights the biggest gaps, and prepares test additions while confirming with the user before changing code.
pnpm test:coverage (set CI=1 if needed) to regenerate coverage/.coverage/coverage-summary.json (preferred) or coverage/coverage-final.json, plus coverage/lcov.info and coverage/lcov-report/index.html for drill-downs.pnpm test:coverage, and then run $code-change-verification before marking work complete.CI=1 pnpm test:coverage at repo root. Avoid watch flags and keep prior coverage artifacts only if comparing trends.coverage/coverage-summary.json for file-level totals; fallback to coverage/coverage-final.json if the summary file is absent.coverage/lcov.info or coverage/lcov-report/index.html to spot branch- and line-level holes.packages/*/src before examples or docs.packages/<pkg>/test/*.test.ts) and avoid flaky async timing.scripts/, references/, or assets/ unless needed later.pnpm test:coverage instead of guessing.Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take openai/test-coverage-improver from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.