Run a manual test of the current change end-to-end and output reproducible test instructions for the PR "Test instructions" section.
npx skills add https://github.com/DataDog/browser-sdk --skill manual-testing
If you don't have the PR changes in memory, review all commits in the PR to identify what SDK behavior changed and what events/fields need to be verified.
The dev server serves the sandbox/ directory and proxies intake requests locally. Use yarn dev-server --help to list all available commands.
yarn dev-server start
Create sandbox/test-<topic>.html. See sandbox/index.html for a minimal example. Include only the elements needed to exercise the change. Always use proxy: '/proxy'.
cat > sandbox/test-<topic>.html << 'EOF'
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Test <topic></title>
<script src="/datadog-rum.js"></script>
<script>
DD_RUM.init({ clientToken: 'xxx', applicationId: 'xxx', proxy: '/proxy', trackUserInteractions: true })
</script>
</head>
<body>
<!-- elements needed to exercise the change -->
</body>
</html>
EOF
Get the dev server URL from yarn dev-server status. Clear any previous intake data, open the page, interact with it using CSS selectors, flush events by opening a new tab or reloading, then inspect the intake:
yarn dev-server intake clear
agent-browser open <dev-server-url>/test-<topic>.html
agent-browser click '#...'
agent-browser tab new
yarn dev-server intake <selector> | jq '<field>'
Use yarn dev-server intake --help to find the right selector.
To evaluate JavaScript on the page (e.g. to inspect window state set by beforeSend), switch focus back to tab 0 first, then use agent-browser eval:
agent-browser tab 0
agent-browser eval 'window.someValue'
Output a self-contained bash snippet with the exact commands run and the expected output. This goes directly into the PR "Test instructions" section.
yarn dev-server stop
rm sandbox/test-<topic>.html
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 datadog/manual-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.