Skill comparison

Compare agent skills before installing.

Put high-signal skills side by side and inspect quality, adoption, freshness, install readiness, use-case fit, and warnings in one place.

Comparing 3 skills

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Decision summary

Browser Use is the strongest overall pick here because it has a 100/100 readiness score and fits Browser automation.

Strongest overall

Browser Use

Use this as a leading candidate, then validate the README and install path in your own agent stack.

Fastest prototype

Browser Use

Best first install candidate based on install readiness and adoption.

Freshest repo

Playwright Browser Skill

Most recent maintenance signal among this shortlist.

SignalEconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Playwright Browser Skill

Automate a real browser from the terminal for navigation, form filling, snapshots, screenshots, extraction, and UI-flow debugging.

Browser Use

Make AI agents interact with websites using natural language

Quality
97/100
Excellent
100/100
Excellent
100/100
Excellent
Decision verdict
100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

100/100
Production-ready

Use this as a leading candidate, then validate the README and install path in your own agent stack.

Adoption4.7K stars
Verified outcomes are shown on each skill page
25K stars
Verified outcomes are shown on each skill page
101K stars
Verified outcomes are shown on each skill page
FreshnessJun 15, 2026Jul 14, 2026Jun 20, 2026
Use-case fit
Workflow fit
Platform hintsJupyter Notebook, Machine Learning, Claude CodeCodex, Claude Code, Cursor, Playwright, OpenAI AgentsClaude, GPT-4, LangChain, OpenClaw, Claude Code
WarningsNo OpenAgentSkill engagement data yetBrowser automation can submit data; confirm the target environment and review actions before execution. · No OpenAgentSkill engagement data yetNo OpenAgentSkill engagement data yet
Best forRAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signalsBrowser automation workflows · Claude Code teams · teams that value GitHub adoption signalsBrowser automation workflows · Claude Code teams · teams that value GitHub adoption signals
Not ideal forteams that need a vendor-supported SLA · high-compliance environments without internal security reviewteams that need a vendor-supported SLA · production agents without a repository reviewteams that need a vendor-supported SLA · high-compliance environments without internal security review
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Install
$ npx skills add py-why/EconML
$ npx skills add openai/skills --skill playwright
$ npx skills add browser-use/browser-use