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Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | firecrawl-competitive-intel |
| description | Monitor competitor pricing, features, changelogs, dashboards, and product changes with Firecrawl. Use for recurring competitive intelligence, pricing tier extraction, feature change tracking, or structured competitor alerts. |
| license | ISC |
| metadata | {"author":"firecrawl","version":"0.1.0","homepage":"https://www.firecrawl.dev","source":"https://github.com/firecrawl/firecrawl-workflows"} |
| inputs | [{"name":"FIRECRAWL_API_KEY","description":"Firecrawl API key for hosted Firecrawl requests.","required":true}] |
Use this for monitoring competitors over time. This is not the broad competitor analysis workflow.
Infer competitors, focus, cadence, and output format from context. If competitors are clear, proceed immediately.
Ask at most 1-3 concise questions only if blocked, such as the competitor list, focus area, or whether authenticated pages/profiles are required.
For each competitor, use Firecrawl scrape or browser as needed:
If appropriate, use sub-agents or equivalent parallel task runners. A natural split is one competitor per researcher or one focus area per researcher.
Each researcher should return pricing tiers, features, recent changes, source URLs, and confidence notes.
# Competitive Intel: [Competitors]
## Alerts
[Notable pricing, feature, or positioning changes]
## Per-Competitor Breakdown
[Pricing tiers, feature inventory, recent changes]
## Cross-Competitor Comparison
[Pricing table, feature matrix, key differentiators]
## Suggested Follow-Ups
[What to monitor next]
## Sources
[URLs visited]
## Rerun Inputs
workflow: firecrawl-competitive-intel
competitors: [list]
focus: [all/pricing/features/changelog]
cadence: [one-off/weekly/monthly]
When structured output is requested, include generatedAt, competitors, pricing, recentChanges, features, and sources.
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification. Use when the user wants a literature review, paper summary, research landscape, or sourced synthesis from PDFs and scholarly/industry publications.
Produce an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill.
Extract any website's design system into an agent-ready DESIGN.md using Firecrawl scrape evidence. Use when the user wants colors, fonts, spacing, components, layout patterns, or brand/UI guidance from a website so AI agents can create new websites, clone a look, or build pages inspired by that design.
Extract structured company lists from directories with Firecrawl. Use for scraping YC, Crunchbase, Product Hunt, G2, startup directories, category directories, or custom company databases into JSON, CSV, CRM-ready lists, or research tables.
Pull metrics from analytics dashboards and internal web tools with Firecrawl browser. Use when the user needs dashboard reporting, cross-platform metric summaries, authenticated analytics extraction, date-range reports, or structured metrics from web dashboards.
Walk through a product's key flows with Firecrawl browser and produce a structured UX/product walkthrough. Use for signup, onboarding, pricing, docs, dashboard, product demo prep, UX teardown, and first-run experience analysis.