firecrawl-agent

firecrawl/cli · updated Apr 8, 2026

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$npx skills add https://github.com/firecrawl/cli --skill firecrawl-agent
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summary

AI-powered autonomous extraction of structured data from complex multi-page websites.

  • Navigates sites intelligently to locate and extract data, returning results as JSON with optional schema validation
  • Supports custom JSON schemas for predictable structured output, or freeform extraction when schema is not provided
  • Offers two model tiers (spark-1-mini and spark-1-pro) with credit limits and optional waiting for inline results
  • Best suited for multi-page extraction tasks; use simple
skill.md

firecrawl agent

AI-powered autonomous extraction. The agent navigates sites and extracts structured data (takes 2-5 minutes).

When to use

  • You need structured data from complex multi-page sites
  • Manual scraping would require navigating many pages
  • You want the AI to figure out where the data lives

Quick start

# Extract structured data
firecrawl agent "extract all pricing tiers" --wait -o .firecrawl/pricing.json

# With a JSON schema for structured output
firecrawl agent "extract products" --schema '{"type":"object","properties":{"name":{"type":"string"},"price":{"type":"number"}}}' --wait -o .firecrawl/products.json

# Focus on specific pages
firecrawl agent "get feature list" --urls "<url>" --wait -o .firecrawl/features.json

Options

Option Description
--urls <urls> Starting URLs for the agent
--model <model> Model to use: spark-1-mini or spark-1-pro
--schema <json> JSON schema for structured output
--schema-file <path> Path to JSON schema file
--max-credits <n> Credit limit for this agent run
--wait Wait for agent to complete
--pretty Pretty print JSON output
-o, --output <path> Output file path

Tips

  • Always use --wait to get results inline. Without it, returns a job ID.
  • Use --schema for predictable, structured output — otherwise the agent returns freeform data.
  • Agent runs consume more credits than simple scrapes. Use --max-credits to cap spending.
  • For simple single-page extraction, prefer scrape — it's faster and cheaper.

See also

how to use firecrawl-agent

How to use firecrawl-agent 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 firecrawl-agent
2

Execute installation command

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

$npx skills add https://github.com/firecrawl/cli --skill firecrawl-agent

The skills CLI fetches firecrawl-agent from GitHub repository firecrawl/cli 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/firecrawl-agent

Reload or restart Cursor to activate firecrawl-agent. Access the skill through slash commands (e.g., /firecrawl-agent) 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.

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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

4.628 reviews
  • Ira Sethi· Dec 28, 2024

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

  • Ganesh Mohane· Dec 8, 2024

    Registry listing for firecrawl-agent matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aanya Liu· Dec 8, 2024

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

  • Rahul Santra· Nov 27, 2024

    firecrawl-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Amelia Harris· Nov 27, 2024

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

  • Henry Garcia· Nov 19, 2024

    firecrawl-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Oct 18, 2024

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

  • Kabir Li· Oct 18, 2024

    firecrawl-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Diya Choi· Oct 10, 2024

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

  • Piyush G· Sep 9, 2024

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

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