AI-powered autonomous extraction of structured data from complex multi-page websites.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfirecrawl-agentExecute the skills CLI command in your project's root directory to begin installation:
Fetches firecrawl-agent from firecrawl/cli and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate firecrawl-agent. Access via /firecrawl-agent in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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AI-powered autonomous extraction. The agent navigates sites and extracts structured data (takes 2-5 minutes).
# 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
| 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 |
--wait to get results inline. Without it, returns a job ID.--schema for predictable, structured output — otherwise the agent returns freeform data.--max-credits to cap spending.scrape — it's faster and cheaper.Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: firecrawl-agent is focused, and the summary matches what you get after install.
Registry listing for firecrawl-agent matched our evaluation — installs cleanly and behaves as described in the markdown.
We added firecrawl-agent from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
firecrawl-agent reduced setup friction for our internal harness; good balance of opinion and flexibility.
firecrawl-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
firecrawl-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend firecrawl-agent for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
firecrawl-agent is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
firecrawl-agent fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: firecrawl-agent is focused, and the summary matches what you get after install.
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