bundled/skills/playwright/SKILL.md
Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.
npx skillsauth add foryourhealth111-pixel/vco-skills-codex playwrightInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Drive a real browser from the terminal using playwright-cli. Prefer the bundled wrapper script so the CLI works even when it is not globally installed.
Treat this skill as CLI-first automation. Do not pivot to @playwright/test unless the user explicitly asks for test files.
Before proposing commands, check whether npx is available (the wrapper depends on it):
command -v npx >/dev/null 2>&1
If it is not available, pause and ask the user to install Node.js/npm (which provides npx). Provide these steps verbatim:
# Verify Node/npm are installed
node --version
npm --version
# If missing, install Node.js/npm, then:
npm install -g @playwright/cli@latest
playwright-cli --help
Once npx is present, proceed with the wrapper script. A global install of playwright-cli is optional.
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export PWCLI="$CODEX_HOME/skills/playwright/scripts/playwright_cli.sh"
User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).
Use the wrapper script:
"$PWCLI" open https://playwright.dev --headed
"$PWCLI" snapshot
"$PWCLI" click e15
"$PWCLI" type "Playwright"
"$PWCLI" press Enter
"$PWCLI" screenshot
If the user prefers a global install, this is also valid:
npm install -g @playwright/cli@latest
playwright-cli --help
Minimal loop:
"$PWCLI" open https://example.com
"$PWCLI" snapshot
"$PWCLI" click e3
"$PWCLI" snapshot
Snapshot again after:
Refs can go stale. When a command fails due to a missing ref, snapshot again.
"$PWCLI" open https://example.com/form
"$PWCLI" snapshot
"$PWCLI" fill e1 "[email protected]"
"$PWCLI" fill e2 "password123"
"$PWCLI" click e3
"$PWCLI" snapshot
"$PWCLI" open https://example.com --headed
"$PWCLI" tracing-start
# ...interactions...
"$PWCLI" tracing-stop
"$PWCLI" tab-new https://example.com
"$PWCLI" tab-list
"$PWCLI" tab-select 0
"$PWCLI" snapshot
The wrapper script uses npx --package @playwright/cli playwright-cli so the CLI can run without a global install:
"$PWCLI" --help
Prefer the wrapper unless the repository already standardizes on a global install.
Open only what you need:
references/cli.mdreferences/workflows.mde12.eval and run-code unless needed.eX and say why; do not bypass refs with run-code.--headed when a visual check will help.output/playwright/ and avoid introducing new top-level artifact folders.development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.