--- name: affitor/traffic-analyzer source: https://app.decimal.ai/s/affitor-traffic-analyzer@1/SKILL.md source_sha256: 7eec01bff122 --- # Traffic Analyzer Analyze website traffic, engagement, and traffic sources for any domain. Goes beyond raw data — scores the domain, interprets what the traffic patterns mean for affiliate promotion, and recommends whether the program is worth your time. A tool returns numbers. This skill returns a verdict. **Use cases:** - Is this affiliate program's website healthy? (High traffic = more brand awareness = easier conversions) - Where does a competitor get their traffic? (Find channels they're ignoring) - Compare 2-3 affiliate programs by advertiser website strength - Validate a niche by checking traffic to the top programs in it ## Stage This skill belongs to Stage S1: Research ## When to Use - Before committing to promote an affiliate program — check if the advertiser is legit - When comparing multiple programs — traffic is a proxy for brand strength - When `competitor-spy` identifies competitor sites — analyze their traffic sources - When evaluating a niche — check if the top programs have healthy traffic - When an advertiser claims "millions of users" — verify with data ## Input Schema ```yaml domains: string[] # (required) 1-5 domains to analyze — "heygen.com", "synthesia.io" compare: boolean # (optional, default: true if 2+ domains) Side-by-side comparison focus: string # (optional, default: "affiliate") # "affiliate" — score from promoter perspective # "competitor" — analyze as a competitor site # "advertiser" — evaluate advertiser health ``` ## Workflow ### Step 1: Gather Traffic Data **With SimilarWeb API (see `shared/references/social-data-providers.md`):** If `social_data_config.similarweb` is configured: - Call SimilarWeb API for each domain - Returns: global rank, country rank, visits, pages/visit, avg duration, bounce rate, traffic sources **Without API (web_search fallback):** For each domain: 1. `web_search "[domain] traffic similarweb"` → often shows rank and visit estimates in snippets 2. `web_search "[domain] site traffic statistics"` → third-party reports 3. `web_search "site:[domain]"` → Google index count as proxy for content depth 4. `web_search "[domain] alexa rank"` OR `"[domain] semrush traffic"` → alternative sources 5. `web_fetch "https://www.similarweb.com/website/[domain]/"` → extract visible data from SimilarWeb free page (may be limited) Note: web_search data is approximate. SimilarWeb API provides exact metrics. ### Step 2: Analyze Core Metrics For each domain, analyze and interpret: **Traffic Volume:** ```yaml global_rank: number # Lower = better. <10K = major site, <100K = solid, <1M = niche country_rank: number # Rank in primary country monthly_visits: string # "1.2M", "350K", "45K" visits_trend: string # "growing" | "stable" | "declining" (if historical data available) ``` **Engagement Quality:** ```yaml pages_per_visit: number # >3 = good engagement, <2 = bouncy avg_visit_duration: string # ">3 min" = engaged, "<1 min" = low quality bounce_rate: number # <40% = excellent, 40-60% = normal, >60% = concerning ``` **Traffic Sources Breakdown:** ```yaml direct: number # % — brand strength indicator search: number # % — SEO strength social: number # % — social media presence referral: number # % — partnership/affiliate ecosystem paid: number # % — ad spend (high paid = advertiser invests in acquisition) ``` ### Step 3: Interpret for Use Case **For affiliate promoters (`focus: "affiliate"`):** Score the domain as an affiliate promotion target: | Signal | Good (8-10) | OK (5-7) | Red Flag (1-4) | |--------|-------------|----------|-----------------| | Monthly visits | >500K | 50K-500K | <50K | | Bounce rate | <40% | 40-60% | >70% | | Search traffic | >30% | 15-30% | <10% (overly dependent on ads) | | Brand (direct) | >30% | 15-30% | <10% (nobody knows them) | | Pages/visit | >4 | 2-4 | <2 | **Why this matters for affiliates:** - High traffic = people already search for this brand → easier to convert your referrals - Strong brand (high direct traffic) = trust → higher conversion rate - Good engagement = product delivers value → lower refund rate → your commissions stick - Healthy search traffic = sustainable business → long-term commission potential - High paid traffic = advertiser invests in growth → good sign for program longevity **For competitor analysis (`focus: "competitor"`):** - Identify their strongest traffic channel → where are they winning? - Find their weakest channel → opportunity for you - Compare bounce rate → are they retaining visitors better than you? - Check referral traffic → which sites link to them? (potential partnership targets) **For advertiser evaluation (`focus: "advertiser"`):** - Is the advertiser's website healthy? (declining traffic = risky to promote) - Do they invest in marketing? (paid traffic % shows ad budget) - Is their product sticky? (engagement metrics reveal product quality) - How established are they? (global rank trajectory) ### Step 4: Generate Traffic Score Calculate an overall **Traffic Health Score** (0-100): ``` traffic_score = ( rank_score × 0.20 + # Based on global rank volume_score × 0.25 + # Based on monthly visits engagement_score × 0.25 + # Based on bounce rate + pages/visit + duration diversity_score × 0.15 + # Traffic source diversity (not overly dependent on one channel) brand_score × 0.15 # Direct traffic % (brand recognition) ) ``` **Score interpretation:** - **80-100:** Excellent. Strong, established brand. Safe to promote long-term. - **60-79:** Good. Healthy traffic. Solid promotion candidate. - **40-59:** Fair. Growing or niche site. Evaluate other factors (commission, product quality). - **20-39:** Weak. Low traffic or declining. Proceed with caution. - **0-19:** Red flag. Very low traffic, new, or declining fast. Not recommended unless early-stage with high commission. ### Step 5: Compare (if multiple domains) If 2+ domains provided, create side-by-side comparison: - Which has more traffic? - Which has better engagement? - Which has more diverse traffic sources? - Which is growing faster? - Overall winner with reasoning ### Step 6: Self-Validation Before presenting output, verify: - [ ] Data source clearly stated (API vs web_search estimate) - [ ] Scores are calibrated (not all 8/10 — differentiate clearly) - [ ] Interpretation matches the focus (affiliate vs competitor vs advertiser) - [ ] Red flags explicitly called out, not buried - [ ] Recommendation is actionable and specific If any check fails, fix before delivering. Do not flag checklist to user. ## Output Schema ```yaml output_schema_version: "1.0.0" domains_analyzed: - domain: string data_source: "similarweb_api" | "web_search_estimate" metrics: global_rank: number | null country_rank: number | null country: string | null monthly_visits: string pages_per_visit: number | null avg_duration: string | null bounce_rate: number | null traffic_sources: direct: number | null # percentage search: number | null social: number | null referral: number | null paid: number | null traffic_score: number # 0-100 verdict: string # "excellent" | "good" | "fair" | "weak" | "red_flag" interpretation: string # 2-3 sentence analysis based on focus comparison: object | null # if 2+ domains winner: string reasoning: string recommended_next_skill: string ``` ## Output Format ```markdown ## Traffic Analysis: [Domain(s)] ### Data Source 📊 **[SimilarWeb API | Web search estimates (approximate)]** --- ### [domain1.com] — Traffic Score: [XX]/100 — [Verdict] | Metric | Value | Assessment | |--------|-------|------------| | Global Rank | #XX,XXX | [Good/Fair/Low] | | Monthly Visits | X.XM | [High/Medium/Low] | | Pages/Visit | X.X | [Engaged/Average/Bouncy] | | Avg Duration | Xm Xs | [Good/Low] | | Bounce Rate | XX% | [Healthy/Concerning/High] | **Traffic Sources:** ``` Direct: ██████████░░░░░░ 35% (strong brand) Search: ████████░░░░░░░░ 28% (good SEO) Social: ████░░░░░░░░░░░░ 15% (social presence) Referral: ███░░░░░░░░░░░░░ 12% (affiliate ecosystem) Paid: ██░░░░░░░░░░░░░░ 10% (moderate ad spend) ``` **What This Means for You:** [2-3 sentences interpreting metrics for the user's focus — affiliate/competitor/advertiser] --- ### [If comparing 2+ domains] ### Head-to-Head: [domain1] vs [domain2] | Metric | [domain1] | [domain2] | Winner | |--------|-----------|-----------|--------| | Traffic Score | XX/100 | XX/100 | [domain] | | Monthly Visits | X.XM | XXK | [domain] | | Engagement | X.X pg/visit | X.X pg/visit | [domain] | | Brand Strength | XX% direct | XX% direct | [domain] | | SEO | XX% search | XX% search | [domain] | **Verdict:** [domain1] is the stronger affiliate promotion target because [reasoning]. --- ### 🎯 Recommendation [Specific, actionable recommendation based on focus] ### Next Steps - `affiliate-program-search` — check commission details for [domain] - `competitor-spy` — deep dive into their affiliate strategy - `trending-content-scout` — find what content about [domain/product] is performing ``` ## Error Handling - **No API and web_search returns limited data:** Present what's available. Note: *"Limited data available via web search. For accurate metrics, configure SimilarWeb API — see `shared/references/social-data-providers.md`."* Still provide estimated score. - **Domain not found / too new:** Note: *"[domain] has insufficient traffic data. This could mean: (1) very new site, (2) very low traffic, (3) data not yet indexed. This is itself useful information — proceed with caution."* Score: 10/100. - **Domain is a subdomain:** Analyze the root domain instead. Note the adjustment. - **More than 5 domains requested:** Analyze top 5, suggest running again for the rest. - **SimilarWeb API rate limited:** Fall back to web_search for remaining domains. ## Examples **Example 1:** User: "Is HeyGen worth promoting? Check their traffic." → domain: "heygen.com", focus: "affiliate" → SimilarWeb or web_search → Global rank: ~15K, 2.1M monthly visits → Score: 82/100 — "Excellent. HeyGen has strong traffic with healthy engagement. 35% direct traffic shows strong brand recognition. Your referral links benefit from existing brand awareness." → Next: `affiliate-program-search` for HeyGen commission details **Example 2:** User: "Compare Notion vs ClickUp vs Monday.com traffic for my productivity niche" → domains: ["notion.so", "clickup.com", "monday.com"] → Analyze all 3, side-by-side comparison → Winner: Notion (highest traffic, best engagement) → But: ClickUp has highest referral % (12%) = strongest affiliate ecosystem → may convert better **Example 3:** User: "I found this small SaaS tool — screenpal.com. Is the advertiser legit?" → domain: "screenpal.com", focus: "advertiser" → Global rank: ~180K, ~300K monthly visits → Score: 55/100 — "Fair. Niche tool with moderate traffic. Growing steadily. Low paid traffic (2%) suggests bootstrapped. Engagement is good (3.8 pages/visit). Worth promoting if commission is strong, but don't expect brand-name conversion rates." ## Feedback & Issue Reporting When this skill produces unexpected, incomplete, or incorrect output, generate a `skill_feedback` block (see `shared/references/feedback-protocol.md` for full schema). **Skill-specific failure modes:** - **Domain not found in SimilarWeb:** Very new or very small site. Report as `data_quality`, note domain. - **All metrics null from web_search:** No traffic data findable. Report as `data_quality`, severity: medium. - **Traffic score seems wrong:** Score doesn't match known reality (e.g., Google.com scored 40/100). Report as `wrong_output`. **Auto-detect triggers:** - `traffic_score` is 0 or null for a well-known domain - All `traffic_sources` percentages are null - Comparison requested but only 1 domain returned data Report issues: [GitHub Issues](https://github.com/Affitor/affiliate-skills/issues/new?labels=skill-feedback&title=traffic-analyzer) | [Discussions](https://github.com/Affitor/affiliate-skills/discussions/categories/ideas) ## References - `shared/references/social-data-providers.md` — SimilarWeb API configuration - `shared/references/flywheel-connections.md` — master flywheel connection map - `shared/references/affiliate-glossary.md` — affiliate marketing terminology - `shared/references/feedback-protocol.md` — issue detection and reporting standard ## Flywheel Connections ### Feeds Into - `affiliate-program-search` (S1) — traffic score as program evaluation factor - `competitor-spy` (S1) — traffic sources reveal competitor strategy - `niche-opportunity-finder` (S1) — traffic data validates niche demand - `content-angle-ranker` (S1) — traffic source breakdown informs platform prioritization - `trending-content-scout` (S1) — social traffic % hints at which platforms to scout ### Fed By - `competitor-spy` (S1) — competitor domains to analyze - `affiliate-program-search` (S1) — program URLs to evaluate - `niche-opportunity-finder` (S1) — top program domains in a niche ### Feedback Loop - S6 `performance-report` shows your referral contribution to the advertiser → compare your traffic impact over time → prioritize programs where you move the needle ```yaml chain_metadata: skill_slug: "traffic-analyzer" stage: "research" timestamp: string suggested_next: - "affiliate-program-search" - "competitor-spy" - "trending-content-scout" ```