agent-browser

supercent-io/skills-template · updated Apr 8, 2026

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$npx skills add https://github.com/supercent-io/skills-template --skill agent-browser
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summary

Deterministic browser automation for AI agents with snapshot-based element references and multi-session support.

  • Interact with web pages using stable element refs (@e1, @e2, etc.) generated from snapshots, enabling reliable automation across DOM changes
  • Core commands cover navigation, form filling, clicking, waiting, screenshots, PDFs, and visual regression testing via baseline comparison
  • Supports parallel isolated sessions, network-aware waits (networkidle), and selector-based targe
skill.md

agent-browser - Browser Automation for AI Agents

When to use this skill

  • Open websites and automate UI actions
  • Fill forms, click controls, and verify outcomes
  • Capture screenshots/PDFs or extract content
  • Run deterministic web checks with accessibility refs
  • Execute parallel browser tasks via isolated sessions

Core workflow

Always use the deterministic ref loop:

  1. agent-browser open <url>
  2. agent-browser snapshot -i
  3. interact with refs (@e1, @e2, ...)
  4. agent-browser snapshot -i again after page/DOM changes
agent-browser open https://example.com/form
agent-browser wait --load networkidle
agent-browser snapshot -i
agent-browser fill @e1 "[email protected]"
agent-browser click @e2
agent-browser snapshot -i

Command patterns

Use && chaining when intermediate output is not needed.

# Good chaining: open -> wait -> snapshot
agent-browser open https://example.com && agent-browser wait --load networkidle && agent-browser snapshot -i

# Separate calls when output is needed first
agent-browser snapshot -i
# parse refs
agent-browser click @e2

High-value commands:

  • Navigation: open, close
  • Snapshot: snapshot -i, snapshot -i -C, snapshot -s "#selector"
  • Interaction: click, fill, type, select, check, press
  • Verification: diff snapshot, diff screenshot --baseline <file>
  • Capture: screenshot, screenshot --annotate, pdf
  • Wait: wait --load networkidle, wait <selector|@ref|ms>

Verification patterns

Use explicit evidence after actions.

# Baseline -> action -> verify structure
agent-browser snapshot -i
agent-browser click @e3
agent-browser diff snapshot

# Visual regression
agent-browser screenshot baseline.png
agent-browser click @e5
agent-browser diff screenshot --baseline baseline.png

Safety and reliability

  • Refs are invalid after navigation or significant DOM updates; re-snapshot before next action.
  • Prefer wait --load networkidle or selector/ref waits over fixed sleeps.
  • For multi-step JS, use eval --stdin (or base64) to avoid shell escaping breakage.
  • For concurrent tasks, isolate with --session <name>.
  • Use output controls in long pages to reduce context flooding.
  • Optional hardening in sensitive flows: domain allowlist and action policies.

Optional hardening examples:

# Wrap page content with boundaries to reduce prompt-injection risk
export AGENT_BROWSER_CONTENT_BOUNDARIES=1

# Limit output volume for long pages
export AGENT_BROWSER_MAX_OUTPUT=50000

# Restrict navigation and network to trusted domains
export AGENT_BROWSER_ALLOWED_DOMAINS="example.com,*.example.com"

# Restrict allowed action types
export AGENT_BROWSER_ACTION_POLICY=./policy.json

Example policy.json:

{"default":"deny","allow":["navigate","snapshot","click","fill","scroll","wait","get"],"deny":["eval","download","upload","network","state"]}

CLI-flag equivalent:

agent-browser --content-boundaries --max-output 50000 --allowed-domains "example.com,*.example.com" --action-policy ./policy.json open https://example.com

Troubleshooting

  • command not found: install and run agent-browser install.
  • Wrong element clicked: run snapshot -i again and use fresh refs.
  • Dynamic SPA content missing: wait with --load networkidle or targeted wait selector.
  • Session collisions: assign unique --session names and close each session.
  • Large output pressure: narrow snapshots (-i, -c, -d, -s) and extract only needed text.

References

Deep-dive docs in this skill:

Related resources:

Ready templates:

  • ./templates/form-automation.sh
  • ./templates/capture-workflow.sh

Metadata

  • Version: 1.1.0
  • Last updated: 2026-02-26
  • Scope: deterministic browser automation for agent workflows
how to use agent-browser

How to use agent-browser on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add agent-browser
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill agent-browser

The skills CLI fetches agent-browser from GitHub repository supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/agent-browser

Reload or restart Cursor to activate agent-browser. Access the skill through slash commands (e.g., /agent-browser) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.533 reviews
  • Ava Mensah· Dec 28, 2024

    We added agent-browser from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Evelyn Smith· Dec 20, 2024

    Solid pick for teams standardizing on skills: agent-browser is focused, and the summary matches what you get after install.

  • Shikha Mishra· Dec 8, 2024

    agent-browser has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aisha Okafor· Dec 4, 2024

    I recommend agent-browser for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Yash Thakker· Nov 27, 2024

    Keeps context tight: agent-browser is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ava Garcia· Nov 19, 2024

    agent-browser fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakshi Patil· Nov 7, 2024

    Solid pick for teams standardizing on skills: agent-browser is focused, and the summary matches what you get after install.

  • Chaitanya Patil· Oct 26, 2024

    I recommend agent-browser for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Dhruvi Jain· Oct 18, 2024

    We added agent-browser from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hassan Patel· Oct 10, 2024

    agent-browser has been reliable in day-to-day use. Documentation quality is above average for community skills.

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