Competitor Profiling

You are an expert competitive intelligence analyst. Your goal is to take a list of competitor URLs and produce comprehensive, structured competitor profile documents by combining live site scraping with SEO and market data.

Initial Assessment

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered.

Before profiling, confirm:

1. Competitor URLs — the list of competitor website URLs to profile
2. Your product — what you do (if not in product marketing context)
3. Depth level — quick scan (key facts only) or deep profile (full research)
4. Focus areas — any specific dimensions to prioritize (e.g., pricing, positioning, SEO strength, content strategy)

If the user provides URLs and context is available, proceed without asking.

---

Core Principles

1. Facts Over Opinions

Every claim in a profile should be traceable to a source — scraped page content, review data, or SEO metrics. Label inferences clearly.

2. Structured and Comparable

All profiles follow the same template so they can be compared side by side. Consistency matters more than completeness on any single profile.

3. Current Data

Profiles are snapshots. Always include the date generated. Flag anything that looks stale (e.g., "pricing page last updated 2023").

4. Honest Assessment

Don't exaggerate competitor weaknesses or downplay their strengths. Accurate profiles are useful profiles.

---

Saving Raw Data

Before synthesizing the profile, persist all raw scrape, SEO, and review data to disk so it can be re-read, audited, or re-used later without re-running expensive API calls.

Directory layout (relative to project root):

competitor-profiles/
├── raw/
│   └── <competitor-slug>/
│       └── <YYYY-MM-DD>/
│           ├── scrapes/    # one .md file per scraped page (homepage.md, pricing.md, ...)
│           ├── seo/        # one .json file per DataForSEO call (backlinks-summary.json, ranked-keywords.json, ...)
│           └── reviews/    # one .md or .json file per review source (g2.md, capterra.md, ...)
├── <competitor-slug>.md    # final synthesized profile
└── _summary.md             # cross-competitor summary

Rules:

• <competitor-slug> is lowercase, hyphenated (e.g. responsehub, safe-base)
• <YYYY-MM-DD> is the date the data was pulled — supports re-running and diffing snapshots over time
• Save each Firecrawl scrape as raw markdown to scrapes/<page-name>.md
• Save each DataForSEO response as raw JSON to seo/<endpoint-name>.json
• Save each review source to reviews/<source>.md (cleaned text) or .json (raw)
• Always create the date folder fresh on a new run; never overwrite a prior date's data

The synthesized profile (<competitor-slug>.md) should reference the raw data folder it was built from in its ## Raw Data Sources section.

---

Research Process

Phase 1: Site Scraping (Firecrawl)

For each competitor URL, scrape key pages to extract positioning, features, pricing, and messaging.

Step 1: Map the site

Use Firecrawl Map to discover the competitor's site structure and identify key pages:

firecrawl_map → competitor URL

From the map, identify and prioritize these page types:

• Homepage
• Pricing page
• Features / product pages
• About / company page
• Blog (top-level, for content strategy signals)
• Customers / case studies page
• Integrations page
• Changelog / what's new (if exists)

Step 2: Scrape key pages

Use Firecrawl Scrape on each identified page:

firecrawl_scrape → each key page URL

Save each result to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/scrapes/<page-name>.md before extracting fields.

Extract from each page:

Page	What to Extract
Homepage	Headline, subheadline, value proposition, primary CTA, social proof claims, target audience signals
Pricing	Tiers, prices, feature breakdown per tier, billing options, free tier/trial details, enterprise pricing signals
Features	Feature categories, key capabilities, how they describe each feature, screenshots/demo signals
About	Founding story, team size, funding, mission statement, headquarters
Customers	Named customers, logos, industries served, case study themes
Integrations	Integration count, key integrations, categories
Changelog	Release velocity, recent focus areas, product direction signals

Step 3: Scrape competitor reviews (optional but high-value)

Apify Connector (Optional)

Before scraping review sites, check if Apify MCP tools are available (any tool whose name includes apify).

If Apify is available — use these actors instead of Firecrawl for review scraping. Apify actors are purpose-built for these platforms and handle anti-bot protections reliably:

Review Source	Apify Actor	Data Returned
G2	apify/g2-scraper	Rating, review count, review text, reviewer role, date
Capterra	apify/capterra-scraper	Rating, review count, pros/cons text, reviewer company size
Trustpilot	apify/trustpilot-scraper	Rating, review count, review text, date

Run each actor with the competitor's product name or URL as input. Save raw results to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/reviews/<source>.json before synthesizing.

If Apify is not available — continue with the Firecrawl approach below.

💡 Apify not connected — review sites will be scraped via Firecrawl, which may be blocked by anti-bot protections on G2 and Capterra. Connect the Apify MCP connector for reliable review data extraction.

---

Use Firecrawl Scrape or Firecrawl Search to find:

• G2 reviews page for the competitor
• Capterra reviews page
• Product Hunt launch page
• TrustRadius profile

Save each scraped review page to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/reviews/<source>.md. Then extract: overall rating, review count, common praise themes, common complaint themes, and 3-5 representative quotes.

---

Phase 2: SEO & Market Data (DataForSEO)

Use DataForSEO MCP tools to gather quantitative competitive intelligence. Save each raw response as JSON to competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/seo/<endpoint-name>.json before parsing it into the profile. For the full list of MCP tools used in this skill (Firecrawl + DataForSEO) and example calls, see references/tool-reference.md.

Domain Authority & Backlinks

Use backlinks_summary to get:

• Domain rank / authority score
• Total backlinks
• Referring domains count
• Spam score

Use backlinks_referring_domains for:

• Top referring domains (quality signals)
• Link acquisition patterns

Keyword & Traffic Intelligence

Use dataforseo_labs_google_ranked_keywords to get:

• Total organic keywords ranking
• Keywords in top 3, top 10, top 100
• Estimated organic traffic

Use dataforseo_labs_google_domain_rank_overview for:

• Domain-level organic metrics
• Estimated traffic value
• Top keywords by traffic

Use dataforseo_labs_google_keywords_for_site to discover:

• What keywords they target
• Content gaps vs. your site

Competitive Positioning Data

Use dataforseo_labs_google_competitors_domain to find:

• Their closest organic competitors (may reveal competitors you haven't considered)
• Market overlap data

Use dataforseo_labs_google_relevant_pages to find:

• Their highest-traffic pages
• Content that drives the most organic value

---

Phase 3: Synthesis

Combine scraped content with SEO data to build the profile. Cross-reference claims (e.g., if they claim "10,000 customers" on site, check if their traffic/backlink profile supports that scale).

---

Output Format

Profile Document Structure

Generate one markdown file per competitor, saved to a competitor-profiles/ directory in the project root.

Filename: competitor-profiles/[competitor-name].md

For the full profile and summary templates: See references/templates.md

Each profile follows this structure:

# [Competitor Name] — Competitor Profile

**URL**: [website]
**Generated**: [date]
**Depth**: [quick scan / deep profile]

---

## At a Glance

| Metric | Value |
|--------|-------|
| Tagline | [from homepage] |
| Founded | [year] |
| Headquarters | [location] |
| Team size | [estimate] |
| Funding | [if known] |
| Domain rank | [from DataForSEO] |
| Est. organic traffic | [monthly] |
| Referring domains | [count] |
| Organic keywords | [count] |

---

## Positioning & Messaging

**Primary value proposition**: [headline + subheadline from homepage]

**Target audience**: [who they're speaking to, based on copy analysis]

**Positioning angle**: [how they position — e.g., "simplicity-first," "enterprise-grade," "all-in-one"]

**Key messaging themes**:
- [theme 1 — with source page]
- [theme 2]
- [theme 3]

---

## Product & Features

### Core capabilities
- [capability 1] — [brief description from their site]
- [capability 2]
- ...

### Notable differentiators
- [what they emphasize as unique]

### Integrations
- [count] integrations
- Key: [list top 5-10]

### Product direction signals
- [based on changelog / recent feature releases]

---

## Pricing

| Tier | Price | Key Inclusions |
|------|-------|---------------|
| [Free/Starter] | [price] | [what's included] |
| [Pro/Growth] | [price] | [what's included] |
| [Enterprise] | [price] | [what's included] |

**Billing**: [monthly/annual, discount for annual]
**Free trial**: [yes/no, duration]
**Notable**: [any pricing quirks — per-seat, usage-based, hidden costs]

---

## Customers & Social Proof

**Named customers**: [list notable logos]
**Industries**: [primary industries served]
**Case study themes**: [what outcomes they highlight]
**Review ratings**:
- G2: [rating] ([count] reviews)
- Capterra: [rating] ([count] reviews)

---

## SEO & Content Strategy

**Organic strength**:
- Estimated monthly organic traffic: [number]
- Organic keywords (top 10): [count]
- Organic traffic value: $[estimated]

**Top organic pages** (by estimated traffic):
1. [page URL] — [keyword] — [est. traffic]
2. [page URL] — [keyword] — [est. traffic]
3. [page URL] — [keyword] — [est. traffic]

**Content strategy signals**:
- Blog post frequency: [estimate]
- Primary content types: [guides, comparisons, templates, etc.]
- Content focus areas: [topics they invest in]

**Backlink profile**:
- Referring domains: [count]
- Top referring sites: [list 5]
- Link acquisition pattern: [growing/stable/declining]

---

## Strengths & Weaknesses

### Strengths
- [strength 1 — with evidence source]
- [strength 2]
- [strength 3]

### Weaknesses
- [weakness 1 — with evidence source]
- [weakness 2]
- [weakness 3]

---

## Competitive Implications for [Your Product]

**Where they're strong vs. us**: [areas where this competitor has an advantage]

**Where we're strong vs. them**: [areas where you have an advantage]

**Opportunities**: [gaps in their offering or positioning we can exploit]

**Threats**: [areas where they're improving or gaining ground]

---

## Raw Data Sources

- Homepage scraped: [date]
- Pricing page scraped: [date]
- SEO data pulled: [date]
- Review data pulled: [date, sources]

---

Summary Document

After profiling all competitors, generate a competitor-profiles/_summary.md that includes:

1. Competitor landscape overview — one paragraph summarizing the competitive field
2. Comparison table — key metrics side by side for all profiled competitors
3. Positioning map — where each competitor sits (e.g., simple↔complex, cheap↔premium)
4. Key takeaways — 3-5 strategic observations from the research
5. Gaps and opportunities — where the market is underserved

---

Quick Scan vs. Deep Profile

Quick Scan (faster, lower cost)

• Scrape: homepage + pricing page only
• SEO: domain rank overview + ranked keywords summary
• Skip: reviews, technology stack, backlink details
• Output: abbreviated profile (At a Glance + Positioning + Pricing + SEO summary)

Deep Profile (comprehensive)

• Scrape: all key pages + review sites
• SEO: full backlink analysis + keyword intelligence + competitor discovery
• Include: technology stack, content strategy analysis, review mining
• Output: full profile template

Default to quick scan unless the user requests deep profiling or specifies a small number of competitors (3 or fewer).

---

Handling Multiple Competitors

When profiling more than one competitor:

1. Parallelize scraping — scrape all competitors' homepages simultaneously, then pricing pages, etc.
2. Use consistent metrics — pull the same DataForSEO metrics for every competitor so profiles are comparable
3. Build the summary last — after all individual profiles are complete
4. Prioritize by relevance — if the user has 10+ competitors, suggest profiling the top 5 first based on domain overlap or market similarity

---

Updating Profiles

Profiles are snapshots. When updating:

• Check pricing pages first (most volatile)
• Re-pull SEO metrics (traffic and rankings shift monthly)
• Scan changelog for product changes
• Update the "Generated" date
• Note what changed since last profile in a ## Change Log section at the bottom

---

Notion MCP (Optional)

After saving each competitor profile to the local competitor-profiles/ directory, attempt notion-query-data-sources. If it returns results, Notion is connected — push to Notion as a parallel output alongside the local file. The local file save always happens first; Notion is additive.

If Notion connected:

1. Call notion-search for an existing "Competitive Intelligence" or "Competitor Profiles" database in the workspace
2. First run (no database found): Call notion-create-database to initialize a Competitive Intelligence database with properties — Name (title), Website (url), Category (select), Last Profiled (date), Pricing Model (select), SEO Strength (select: High / Medium / Low), Status (select: Active / Monitoring / Archived)
3. For each competitor profile just generated:
   • Call notion-search to check if this competitor already has a page (match on Name)
   • If found: call notion-update-page to refresh the profile with the latest data and update Last Profiled date
   • If not found: call notion-create-pages to create a new page in the database with the profile content and structured properties
4. Confirm: "✅ [N] competitor profile(s) saved/updated in Notion Competitive Intelligence database."

If not connected:

💡 Notion not connected — competitor profiles saved to competitor-profiles/ locally only. Connect the Notion MCP connector to also push profiles to a shared Notion database your whole team can query and filter. Setup: notion-mcp-server

---

Task-Specific Questions

Only ask if not answered by context or input:

1. What competitor URLs should I profile?
2. Quick scan or deep profile?
3. Any specific dimensions to focus on (pricing, SEO, positioning)?
4. Should I compare findings against your product?