定义了使用浏览器开发者工具进行端到端(E2E)测试的工作流,测试用例以 Markdown 文件形式记录。
npx skills add https://github.com/cafe3310/public-agent-skills --skill browser-testing
本技能定义了一套端到端(E2E)测试流程,该流程不依赖传统的单元测试框架,而是通过 chrome-dev-tools 或类似的浏览器工具进行手动或半自动化的界面测试。
所有测试用例都以结构化的 Markdown 文件形式进行记录和管理。
main 分支,触发 Hugging Face Spaces 的自动部署。每个 Space 应用的测试用例都存放在其各自的 tests/ 目录下。例如:some-proj/tests/。
每个测试用例都是一个独立的 Markdown 文件,其内容结构由模板 templates/test-case.md.template 定义。
这些记录作为测试通过的凭证。
date +'%Y-%m-%d-%H-%M' 获取当前时间戳。git log -1 --pretty=%H%n%s 获取最新的 Commit 信息。browser-testing/templates/test-case.md.template 模板内容。tests/ 目录下。some-proj/tests/YYYY-MM-DD-HH-mm-测试名称/。templates/test-case.md.template: 用于创建新测试用例的 Markdown 模板。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 cafe3310/browser-testing 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.