clawdbrunner/skill-agent-browser

Dernière activité source enregistrée: 5 février 2026 à 23:49

Catalogue SkillsMP mis à jour: 9 novembre 2026 à 14:22

skills collectés: 1

Étoiles GitHub: 4
Forks GitHub: 0

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/clawdbrunner/skill-agent-browser.

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# skill-agent-browser OpenClaw skill for [agent-browser](https://github.com/vercel-labs/agent-browser) — The best browser automation CLI for AI agents. ## Why This Is Better ### Deterministic Refs (Game-Changer for AI) Traditional browser automation uses **CSS selectors** or **coordinates** that break constantly. agent-browser uses **deterministic refs** from accessibility trees: ```bash # Get snapshot — each element gets a stable ref agent-browser snapshot -i --json # Output: - button "Submit" [ref=e2] # Use that ref — it NEVER changes for that element agent-browser click @e2 ``` **Why this matters:** - CSS selectors break when websites update - Coordinates fail on responsive layouts - Vision-based automation is slow and expensive - **Refs just work** — point to the exact element from your snapshot ### Built for AI Agents (Not Developers) | | Traditional Tools | agent-browser | |---|---|---| | **Element targeting** | Fragile selectors | Deterministic refs | | **Page understanding** | Raw HTML soup | Semantic accessibility tree | | **Output** | Text logs | Structured JSON | | **Speed** | Slow per command | Fast daemon architecture | | **Designed for** | Human developers | AI agents | ### Fast Architecture - **Rust CLI** — Native binary for instant command parsing - **Node.js Daemon** — Browser stays warm between commands - **First launch:** ~2 seconds - **Subsequent commands:** ~100 milliseconds ## Installation ### Prerequisites ```bash npm install -g agent-browser agent-browser install # Downloads Chromium (~30 seconds) ``` ### Install the Skill ```bash cd ~/.openclaw/skills # or ~/clawd/skills git clone https://github.com/clawdbrunner/skill-agent-browser.git agent-browser ``` ## The AI Workflow The pattern that makes agent-browser shine: ```bash # 1. Navigate agent-browser open example.com # 2. Get structured snapshot (AI "sees" the page) agent-browser snapshot -i --json # 3. AI picks refs, executes actions agent-browser click @e1 agent-browser fill @e2 "user@example.com" # 4. Verify state changed agent-browser snapshot -i --json # 5. Done agent-browser close ``` ## Quick Reference ### Essential Commands ```bash # Navigation agent-browser open <url> [--json] [--headed] # Snapshot (the killer feature) agent-browser snapshot [-i] [-c] [--json] # Interactive, compact, JSON # Interaction with refs agent-browser click @e1 agent-browser fill @e2 "text" agent-browser type @e2 "text" # Without clearing agent-browser press Enter # State checks agent-browser get text @e1 agent-browser get url agent-browser is visible @e2 # Session management agent-browser --session <name> open <url> # Isolated session agent-browser --profile <path> open <url> # Persistent cookies agent-browser close ``` ### Snapshot Options (Control What AI Sees) | Flag | Purpose | |------|---------| | `-i` | Interactive elements only — **recommended** | | `-c` | Compact (remove empty elements) | | `-d <n>` | Limit tree depth | | `-s <sel>` | Scope to CSS selector | | `--json` | Machine-readable JSON — **essential for AI** | **Best for AI:** `agent-browser snapshot -i -c --json` ### Selector Strategies (Ranked) 1. **Refs** (Best): `agent-browser click @e2` 2. **Semantic**: `agent-browser find role button click --name "Submit"` 3. **CSS**: `agent-browser click "#submit"` 4. **Text/XPath** (Last resort): `agent-browser click "text=Submit"` ## Example: Complex Form Automation ```bash # Open billing portal agent-browser open https://portal.aeronetpr.com # Login flow using refs from snapshot agent-browser fill @e1 "username" agent-browser fill @e2 "password" agent-browser click @e3 # Wait for navigation, get new state sleep 2 agent-browser snapshot -i --json # Add credit card agent-browser click @e24 # "Add new credit card" agent-browser fill @e10 "John Smith" # Name on card agent-browser fill @e11 "4111111111111111" # Card number (test) agent-browser fill @e12 "12/2030" # Expiration agent-browser fill @e13 "123" # CVV agent-browser fill @e14 "123 Main Street" # Address agent-browser fill @e260 "San Juan" # City agent-browser fill @e322 "00907" # ZIP agent-browser click @e324 # Submit # Verify agent-browser snapshot -i --json # Cleanup agent-browser close ``` ## Documentation See [SKILL.md](SKILL.md) for complete documentation including: - Detailed comparison to other tools - All commands and options - Tips for AI agents - When to use this vs built-in browser tools ## Comparison to Alternatives | Tool | agent-browser Advantage | |------|------------------------| | **Puppeteer/Playwright** | Purpose-built for AI, deterministic refs vs brittle selectors | | **Selenium** | 10x faster, modern architecture | | **browser-use** | Better refs system, faster CLI | | **Vision-based** | 100x cheaper, 10x faster, no API calls | | **OpenClaw browser** | Better for complex multi-step flows | ## When to Use This **Use agent-browser for:** - Multi-step web workflows - Complex form filling - Reliable, repeatable automation - Dynamic/modern web apps - Cost-sensitive operations (no vision APIs) **Use OpenClaw's built-in browser tool for:** - Simple single-page checks - Quick screenshots - Already-authenticated Chrome sessions ## Links - **Upstream project:** https://github.com/vercel-labs/agent-browser - **This skill:** https://github.com/clawdbrunner/skill-agent-browser - **Releases:** https://github.com/clawdbrunner/skill-agent-browser/releases ## License MIT

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Browser automation CLI for AI agents using Vercel's agent-browser. The best tool for AI-driven browser automation — uses deterministic refs from accessibility trees instead of fragile selectors. Optimized for LLMs with fast Rust CLI, JSON output, and…

Langue du texte source : anglais

2026-02-05

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