Competitor Spy

Analyze competitor affiliate sites, YouTube channels, and social profiles to surface which programs they promote, what content drives their traffic, and which strategies are worth replicating. Outputs an actionable reverse-engineering report so you can skip years of trial and error.

Stage

This skill belongs to Stage S1: Research

When to Use

• User wants to know what programs are working in a specific niche
• User has a competitor site/channel in mind and wants to understand their strategy
• User is entering a new niche and wants a shortcut to what works
• User wants to find underserved content gaps a competitor hasn't covered
• User asks "how do top affiliates in [niche] make money?"

Input Schema

{
  competitor_url: string      # (optional) Direct URL to competitor site, channel, or profile
  niche: string               # (optional) Niche to analyze if no specific competitor given
  platform: string            # (optional) "blog" | "youtube" | "tiktok" | "twitter" | "newsletter"
  depth: string               # (optional, default: "standard") "quick" | "standard" | "deep"
  focus: string               # (optional) "programs" | "content" | "traffic" | "all"
}

Workflow

Step 1: Identify Competitors to Analyze

If competitor_url is provided, skip to Step 2.

If only niche is provided, find 3-5 top competitors:

1. web_search "best [niche] affiliate sites" — look for review/comparison sites
2. web_search "[niche] review site affiliate" — find review-first monetization models
3. web_search "[niche] blog affiliate income report" — income reports reveal programs
4. Note: YouTube — web_search "youtube [niche] affiliate site:youtube.com" to find channels

Pick 3 competitors that are clearly affiliate-driven (review pages, comparison tables, "best X" content, Amazon links, affiliate disclaimers visible).

Step 2: Identify Affiliate Programs They Promote

For each competitor site/channel:

Method A — Link analysis:

• web_fetch [competitor_url] and scan for outbound links
• Look for: ?ref=, ?via=, /go/, aff_id=, ?affiliate=, shareasale.com, impact.com, partnerstack.com, awin.com, cj.com, linktr.ee
• These patterns indicate affiliate links

Method B — Content analysis:

• Look at their top content: "Best X", "X vs Y", "X Review", "X Alternatives"
• Every product featured prominently = likely affiliate relationship
• Products mentioned with a CTA button ("Try X Free", "Get X") = strong affiliate signal

Method C — Disclosure scan:

• Search page for "affiliate", "commission", "sponsored", "partner" disclosures
• These legally required disclosures often appear at top/bottom and reveal programs

Method D — Income reports (if available):

• web_search "[site name] income report affiliate" — some affiliates publish earnings
• web_search "[creator name] how I make money affiliate" — creator transparency posts

Extract for each program found: name, estimated prominence (primary/secondary/mentioned), content type promoting it, and whether it appears on openaffiliate.dev.

Step 2.5: Analyze Competitor Content Engagement (data-driven)

For each competitor, scan their recent content performance across social platforms. This reveals not just WHAT they create, but HOW WELL it performs.

With API (optional — see shared/references/social-data-providers.md):

• Search YouTube/TikTok for competitor brand name or channel
• Get views, likes, comments, shares for their top 10-20 content pieces
• Calculate engagement_score for each: (likes × 2 + comments × 3 + shares × 5) / max(views, 1) × 1000
• Identify which content format gets them the highest engagement
• Compare their engagement against trending-content-scout benchmark (if available)

Without API (default):

• web_search "[competitor name] youtube channel" → find their channel
• web_fetch channel page → extract view counts from visible videos
• web_search "[competitor name] tiktok" → find top videos with view counts
• web_search "[competitor name] best video" → find their highest-performing content
• Note: approximate data, but reveals relative performance patterns

Extract for each competitor:

• Avg engagement score — how well does their content perform overall?
• Strongest platform — where do they get the most traction?
• Weakest platform — which platforms are they ignoring? (gap to exploit)
• Top performing content — their 3-5 best pieces by engagement
• Format that works for them — which content format gets them the most engagement?

Add these to the competitor assessment table in Step 5:

Dimension	Score (1-10)	Assessment
Content Engagement	—	How well does their content perform? High = proven demand, low = weak execution
Platform Strength	—	Which platform are they strongest on? Which are they ignoring?

Step 3: Analyze Their Content Strategy

For each competitor, extract:

Content patterns:

• Most common formats: listicles ("10 best X"), comparisons ("X vs Y"), tutorials, reviews, roundups, case studies
• Average content depth: shallow (<1000 words), standard (1000-3000), deep (3000+)
• Publishing frequency: estimate from visible dates or web_search "site:[domain] 2024"
• Content freshness: are articles updated? When?

Traffic indicators (from web search signals):

• web_search "site:[domain]" — rough page count
• Search for their brand name — how much branded traffic/discussion?
• Look for "X review" queries in their content — review content = high buyer intent

SEO and social signals:

• Do they rank for "[product] review" terms? (indicates SEO strategy)
• Active social profiles linked from site? Which platforms?
• Do they have a newsletter/email list? (footer signup forms)

Step 4: Find Content Gaps

Compare competitor content to what's NOT covered:

1. Products they promote but haven't done deep comparison posts for
2. Common user questions (from YouTube comments, Reddit threads, forums) they haven't answered
3. New product launches in the niche that competitors haven't covered yet
4. Angles competitors avoid (negative reviews, honest cons, "X is not for everyone")

Use web_search "reddit [niche] [product] problems" to find pain points no affiliate has addressed honestly — these make high-converting, low-competition content.

Step 5: Score Competitor Strategies

For each competitor, assess:

Dimension	Score (1-10)	Assessment
Program Quality	—	Are they promoting high-commission recurring programs or low-margin one-off?
Content Quality	—	Shallow listicles vs. deep genuine reviews
SEO Sophistication	—	Thin content vs. well-structured, keyword-targeted
Monetization Diversity	—	One program vs. multiple revenue streams
Replicability	—	How hard is it to do what they do, but better?

Higher replicability score = easier to beat them.

Step 6: Build the Intelligence Report

Synthesize findings into a 3-part report:

1. Programs worth stealing — top programs their strategy validates
2. Content formats that clearly work — patterns worth replicating
3. Gaps to exploit — angles they've missed that you can own

Step 7: Self-Validation

Before presenting output, verify:

• Confidence levels match evidence strength (confirmed = affiliate link found, likely = brand mention pattern, possible = inferred)
• Programs cross-checked on openaffiliate.dev where possible
• Replicability score accounts for barriers (domain authority, team size)
• No hallucinated competitor data — all claims traceable to web_search results

If any check fails, fix the output before delivering. Do not flag the checklist to the user — just ensure the output passes.

Output Schema

{
  output_schema_version: "1.0.0"  # Semver — bump major on breaking changes
  competitors_analyzed: [
    {
      url: string                   # Competitor URL
      niche: string                 # Their niche focus
      estimated_programs: string[]  # Programs they appear to promote
      top_content_formats: string[] # ["listicle", "comparison", "tutorial"]
      estimated_traffic: string     # "low" | "medium" | "high" (inferred from signals)
      replicability_score: number   # 1-10
      avg_engagement_score: number  # Average engagement across their content
      strongest_platform: string    # Platform where they perform best
      weakest_platform: string      # Platform they're ignoring — gap to exploit
      top_performing_content: string[] # Their 3-5 best pieces by engagement
    }
  ]
  validated_programs: [
    {
      name: string           # "ConvertKit"
      promoted_by: string[]  # Which competitors promote it
      confidence: string     # "confirmed" | "likely" | "possible"
      openaffiliate_url: string | null  # If found on openaffiliate.dev
    }
  ]
  content_gaps: string[]     # Opportunities to fill
  recommended_programs: string[]  # Top programs to prioritize based on analysis
  recommended_next_skill: string  # "affiliate-program-search"
}

Output Format

## Competitor Intelligence Report: [Niche]

### Competitors Analyzed

| Competitor | Programs Found | Content Focus | Replicability |
|-----------|---------------|---------------|---------------|
| [site1.com] | [Program A, B, C] | Best-of lists, comparisons | 7/10 |
| [site2.com] | [Program D, E] | YouTube reviews | 8/10 |

---

### Programs Worth Promoting (Validated by Competitors)

| Program | Promoted By | Evidence | On openaffiliate.dev |
|---------|------------|----------|---------------------|
| [Program A] | [2 competitors] | Prominent CTA buttons, review posts | Yes |
| [Program B] | [1 competitor] | Income report mention | Check manually |

---

### Content Formats That Work in This Niche

1. **[Format 1]:** [What it is, why it works, example from competitor]
2. **[Format 2]:** [...]
3. **[Format 3]:** [...]

---

### Content Gaps You Can Exploit

1. **[Gap 1]:** [What's missing, why it's valuable, how to fill it]
2. **[Gap 2]:** [...]
3. **[Gap 3]:** [...]

---

## Next Steps

1. Run `affiliate-program-search` to evaluate the top validated programs
2. Run `commission-calculator` to compare earnings potential across programs
3. Start with the highest-gap content angle: [Gap 1] for [Program A]

Error Handling

• Competitor URL blocked or paywalled: Fall back to web_search signals (Google cache, SimilarWeb mentions, blog posts about the competitor). Note limitations in report.
• No obvious affiliate links found: Competitor may use native ads or direct sponsorships instead. Flag this and look for brand mention patterns.
• Niche too broad: Ask user to narrow to a sub-niche or pick one platform to focus analysis on.
• No competitors found: Niche may be too new or too narrow. Broaden one step and re-search. If still empty, this itself is a signal — could be a gap opportunity.
• Competitor is a large media company (Forbes, Wirecutter): Scale down — these aren't replicable. Find indie affiliate sites instead (web_search "[niche] best [product] blog").

Examples

Example 1: User: "Spy on what affiliate programs income school recommends" → web_fetch incomeschool.com, look for affiliate disclosures and outbound links → Find: Bluehost, Ezoic, Rank Math, Jasper — extract with confidence levels → Map to openaffiliate.dev programs → Output intelligence report with content gaps in their niche

Example 2: User: "What affiliate strategy do top YouTubers use in the AI tools niche?" → Find 3-5 AI tools YouTubers via web_search → Analyze video descriptions for affiliate links (common pattern: "links below") → Extract: most promote 5-10 tools consistently, heavy on comparison content → Identify gap: no one doing "best AI tools for [specific job role]" content

Example 3: User: "I'm entering the email marketing niche, help me spy on competitors" → Find competitors: emailtooltester.com, emailvendorselection.com, etc. → Extract programs: ConvertKit, ActiveCampaign, GetResponse, Brevo → Content gap: all sites focus on features, none do "email marketing ROI by industry" → Recommend: start with ConvertKit (recurring, high commission), fill the ROI gap

References

• affiliate-program-search/references/openaffiliate-api.md — validate found programs on openaffiliate.dev
• shared/references/affiliate-glossary.md — affiliate link pattern reference
• shared/references/ftc-compliance.md — understanding competitor disclosures
• shared/references/flywheel-connections.md — master flywheel connection map

Flywheel Connections

Feeds Into

• trending-content-scout (S1) — competitor channels/profiles to scout for engagement data
• content-angle-ranker (S1) — competitor gaps as angle candidates
• viral-post-writer (S2) — competitor gaps reveal content opportunities
• purple-cow-audit (S1) — competitive landscape for product evaluation
• grand-slam-offer (S4) — competitive gaps to exploit in offers
• bonus-stack-builder (S4) — what competitors' affiliates offer (gaps to exploit)
• category-designer (S8) — competitive landscape to differentiate from

Fed By

• trending-content-scout (S1) — top creators and engagement data for competitor analysis
• performance-report (S6) — your performance data vs competitors
• seo-audit (S6) — ranking data showing where competitors outrank you

Feedback Loop

• Performance comparisons from S6 reveal where competitor strategies outperform → focus spy analysis on their winning tactics

chain_metadata:
  skill_slug: "competitor-spy"
  stage: "research"
  timestamp: string
  suggested_next:
    - "trending-content-scout"
    - "content-angle-ranker"
    - "purple-cow-audit"
    - "grand-slam-offer"
    - "affiliate-blog-builder"