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Use when analyzing fan-out coverage, sub-query coverage, topic coverage gaps, or keyword-only SERP fan-out without a domain. Simulates AI query fan-out and optional domain coverage checks for a keyword.
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Based on SOC occupation classification
Use when creating or reviewing an agent skill, adapting an external skill for public release, reviewing SKILL.md quality, or choosing handoff JSON and verify scripts. Right-sized packaging with predictable process; regression gates only when needed.
Use when auditing keyword cannibalization, same-domain page overlap, or hub-vs-hub conflicts before consolidation. Use for accidental hub vs intended hub, blog post outranking a landing page, or internal competition diluting rankings on one domain.
Use when auditing robots.txt, AI crawler permissions, GEO crawl policy, cornerstone path crawlability, or blocking/allowing GPTBot, PerplexityBot, OAI-SearchBot, and Google-Extended. Audits selective crawler rules per bot (training vs indexing crawl), validates Sitemap directives, and drafts policy-aligned robots.txt.
Use when recovering hub-and-spoke content architecture on a messy site — accidental hubs, hidden gems, legacy bloat, or onboarding a client without a designed pillar map. Use for organic architecture discovery, de facto pillar audit, GSC and GA4 triangulation, or bottom-up cornerstone recovery. Triggers: "hub and spoke audit", "discover my real pillars", "accidental cornerstone", "hidden gem pages", "performance matrix content audit", "triangulate GSC and GA4 with keyword data".
Use when running a cornerstone content audit, pillar content review, or content architecture audit. End-to-end: discover pages, identify cornerstones, health audit, cannibalization check, and enhancement plan.
Use when triaging landing pages with Google Analytics (GA4) and Google Search Console exports — query intent and landing-page discovery, optional conversion quadrants, accidental hubs, or prune review before hub-and-spoke recovery. Ask whether the audit should be discovery-only or discovery-plus-conversions. Works in GA4-only or GSC-only partial mode when one source is missing. Domain-agnostic; CSV or API-shaped tables.
| name | Fan-Out-Coverage-Analysis |
| type | composite |
| description | Use when analyzing fan-out coverage, sub-query coverage, topic coverage gaps, or keyword-only SERP fan-out without a domain. Simulates AI query fan-out and optional domain coverage checks for a keyword. |
| version | 2.12.0 |
Approximate query fan-out from SERP + Labs data. With a user-supplied domain, measure which sub-queries the site covers. Without a domain, run keyword-only SERP fan-out (no coverage checks).
Public skill: Domain-agnostic and client-agnostic. Use example.com and generic seeds in docs/fixtures only — real domains belong in user prompts at runtime, not committed artifacts.
Primary keyword — from the user only.
Domain — from the user only. Never infer from examples, workspace context, project config, or prior runs.
content_context (optional) — site or brand name for report framing (e.g. “content angle for example.com”). Does not enable domain mode or coverage checks.
Mode resolution: If the user supplies a domain (or says “on example.com”), run Mode A (domain_coverage). If they name a site only for context, ask once whether to measure coverage on that domain or run keyword-only.
If the user did not supply a domain, ask once:
Which domain should I check coverage for? (Optional: anchor URL. Or reply keyword only for SERP fan-out without a site.)
If still no domain (or user chooses keyword-only), run Mode B only. No ranked_keywords, no coverage %.
Confirm primary keyword (required).
Resolve domain per Input policy.
Steps 0 → 0.5 → 1 → 1b (both modes).
Mode A (domain given): Steps 2–6.
Mode B (no domain): Steps 5b–6b → stop.
Offer to save report or pass handoff JSON downstream.
Layperson chat summary per LAYPERSON-OUTPUT.md — paste only the stdout block from run-fanout.mjs --write-reports (or build-layperson-summary.mjs). Do not add tier tables, verifier lines, MCP logs, or handoff JSON in chat.
Reference: REFERENCE.md — API params, relevance gates, handoff schema v1.2
Requirements: REQUIREMENTS.md
Examples: EXAMPLES.md — mixed-SERP case study + regression scorecard
Layperson contract: LAYPERSON-OUTPUT.md
Ship bar:
node scripts/verify-all.mjs
Shell note (Windows): Use ; between commands in PowerShell — && is not valid. Example:
Set-Location marketing/skills/fan-out-coverage-analysis; node scripts/verify-all.mjs
Pipeline (after MCP):
node scripts/ingest-mcp-export.mjs --serp raw-serp.json --labs related-raw.json:related -o merged.json --intent-out intents.json
node scripts/run-fanout.mjs --seed "..." --merged merged.json --ranked-file ranked.json --partial partial.json --output handoff.json --write-reports
Merge MCP exports: scripts/ingest-mcp-export.mjs (raw MCP JSON) or scripts/merge-expansion-input.mjs (pre-flattened)
Coverage merge: scripts/apply-coverage.mjs (or --ranked-file on run-fanout.mjs)
Normalizer (required): scripts/normalize-fanout.mjs
Priority mapper (required after intent): scripts/apply-priority.mjs
Layperson summary: scripts/build-layperson-summary.mjs
serp_organic_live_advanced on the primary keyword. Extract PAA (dedupe nested), related searches, live AI Overview presence, top organic titles/domains. See REFERENCE.md.
Record ai_overview_present from live SERP only. If Labs index disagrees, note in limitations[].
Complete when: serp_context.people_also_ask is deduped, related_searches captured, ai_overview_present + ai_overview_source: live_serp set, and top organic domains recorded.
Classify top 8–10 organic results into lanes (see REFERENCE.md). Set serp_context.intent_lanes_detected, intent_split (single | mixed), and lane_counts.
If mixed, ask once which lane to prioritize — unless the user already stated intent in the prompt:
This SERP mixes [lane A] and [lane B] results. Which should I prioritize for fan-out and gaps? (Or reply report both.)
Store chosen lane in inputs.intent_lane when provided.
Complete when: every classified organic result has a lane label and intent_split is set; user prompted once if mixed and lane not yet chosen.
Expansion policy (v2.9):
| Source | When |
|---|---|
| SERP seed (Step 0) | Always |
related_keywords | Always |
keyword_suggestions | Skip when post-merge SERP + related count ≥ 15 or inputs.intent_lane === tool_discovery |
keyword_ideas | When serp_result_count < 200 and related yield < 10 (thin SERP category fan-out) |
Note skipped suggestions or ideas path in limitations[].
Parallel Labs calls per policy above. Merge Step 0 seeds into { "items": [ … ] } via ingest-mcp-export.mjs (raw MCP JSON) or merge-expansion-input.mjs (pre-flattened):
node scripts/ingest-mcp-export.mjs --serp raw-serp.json --labs related-raw.json:related --labs suggestions-raw.json:suggestions -o merged.json --intent-out intents.json
Thin-seed fallback: When Labs returns empty for the exact seed and seed_volume < 50 (or missing), retry expansion on a parent cluster phrase (e.g. go to market strategy for wordpress product go to market). Re-merge with SERP seeds; re-run normalize-fanout.mjs; note parent cluster in limitations[]. Do not import unrelated vertical terms without seed token overlap.
Run normalize-fanout.mjs with --seed and --lane (when inputs.intent_lane is set). Use script output as the authoritative tier list — do not re-tier in prose. Do not drop normalizer rejections (e.g. zero-overlap brands on serp_related) post-hoc.
Target 25–30 accepted sub-queries after dedupe on live runs. Keep facet-drift terms tagged, not silently dropped, unless zero seed-token overlap. Regression fixtures may use smaller subsets (see limitations[]).
Complete when: merged input passed through normalize-fanout.mjs; each accepted row has relevance_tier, dimension, and on_seed_serp.
dataforseo_labs_search_intent on the normalized keyword list. Attach search_intent to each row.
Run apply-priority.mjs with --intent-split and --lane from handoff inputs. Merge into sub_queries[] and build priority_sub_queries[] from script output — do not assign high|medium|low manually.
Complete when: every sub_queries[] row has search_intent and priority; priority_sub_queries[] (Mode B) matches apply-priority.mjs output.
Run only when a domain was supplied.
dataforseo_labs_google_ranked_keywords: bulk ilike + spot-check gaps. Not relevant_pages.
Filter field: use keyword_data.keyword (not keyword) in MCP filters — see REFERENCE.md.
Save MCP export, then run coverage merge:
node scripts/apply-coverage.mjs handoff-partial.json ranked.json -o handoff.json
# or in one pipeline pass:
node scripts/run-fanout.mjs ... --ranked-file ranked.json --partial handoff-partial.json --output handoff.json
Complete when: each high-priority sub-query has status, covering_url, and rank_group from ranked_keywords or spot-check (via script — do not hand-patch).
Hub / spoke / broken spoke / gap. WebFetch for broken-spoke check when anchor URL set.
Complete when: every sub-query with a ranking has hub/spoke/broken_spoke status; hub_spoke_map[] populated for hubs.
Coverage rate and hub/spoke/broken/gap counts. SSOT for user-facing %: coverage_summary.cluster_coverage_rate_pct (intent clusters). Set coverage_summary.labs_coverage_rate_pct as row-level Labs index; mirror coverage_rate_pct to cluster rate for v1.2 handoffs. When live SERP shows inputs.anchor_url ranking for the seed but Labs has no row, set serp_context.anchor_serp_rank and coverage_summary.coverage_story in plain language — do not let 0% stand alone without context. Use status: hub_serp_only when live rank exists but Labs index lags. Seed-synonym clusters count as covered when anchor ranks on seed.
Complete when: coverage_summary cluster counts match canonical rollup; Labs row rate internally consistent; live anchor rank recorded when known.
Chat: layperson summary only (LAYPERSON-OUTPUT.md). Detail file: technical sections below.
Required sections in detail report:
sub_queries (SSOT; supersedes shallow SERP snapshot)intent_split is mixed)hub_serp_only) when domain modeserp_native / core in chosen lane; list facet_drift separately if mixedComplete when: detail report written; chat matches layperson contract; gap table matches handoff gaps[].
Emit mode: "domain_coverage", handoff_version: "1.2", skill, skill_version per REFERENCE.md.
Complete when: node scripts/verify-handoff.mjs <path> exits 0.
Skip Steps 2–4.
Chat: layperson summary only. Detail file: technical fan-out map.
Required in detail report:
intent_split is mixed)relevance_tier: SERP-native → core → facet_drift → adjacentOptional: dataforseo_labs_google_serp_competitors for market context.
Complete when: fan-out map tier groups match handoff sub_queries[]; priority_sub_queries[] is tier-ordered.
Emit mode: "keyword_only", handoff_version: "1.2", skill, skill_version. domain is null.
Complete when: node scripts/verify-handoff.mjs <path> exits 0.
| When | Tool |
|---|---|
| GEO / citation | ai_opt_llm_ment_search |
| Competitor set | dataforseo_labs_google_serp_competitors |
| Empirical fan-out | GSC MCP when available |
All modes
normalize-fanout.mjs run on merged expansion inputapply-priority.mjs run after search intent (no manual priority labels)relevance_tier; facet drift tagged where applicablehandoff_version: "1.2", skill, skill_version, extended serp_contextnode scripts/verify-handoff.mjs passes on emitted handoffLAYPERSON-OUTPUT.md)Mode A additionally: coverage via ranked_keywords; priority gaps respect chosen lane; coverage_summary math consistent.
Mode B additionally: no ranked_keywords; priority_sub_queries[] populated and tier-ordered.
REFERENCE.md.node scripts/verify-all.mjs (S1–S12, G/R, LP1–LP30, CL1–CL13, SC1–SC4, NR1, EP1–EP5, C1–C22, DR1–DR9, AG1–AG4, PR1–PR7, PA1–PA8, I1–I9, XP1–XP4). Re-run live SERP on mixed-intent golden seeds quarterly.primary_topic lanes; hustle PAAs capped in tool_discovery write-next.tool_discovery has partial cluster coverage, commercial serp_native related ranks before hustle PAAs (AG4 / LP29).free, jobs, course, pdf, download → facet_drift when intent_split: mixed (PA8, R3, R15).pricing page wise rejected at normalize (NR1).related_keywords (I9).PR7).inputs.site_profile: encyclopedia; sibling spoke reconciliation; layperson strengthen/link copy (EP1–EP5).LP30, SC4).run-fanout.mjs resolves sibling *-intents.json when --merged provided.type 1 vs type 2 tokens no longer collapse to seed_synonym.broken_spoke layperson section — page 2+ ranks surfaced as strengthen, not net-new.intent_lane; intent_split normalized from lane_counts.serp_native related.seed_synonym clusters (refreshClusterCanonicals) — covered row wins over gap anagram alias.covered / weak / gap); hub-path copy for non-root anchors.reddit, years, redundant download) into parent seed_adjacent clusters.tool_discovery.gaps_shortlist_count / gaps_row_total in coverage_summary; detail gaps header labels shortlist vs totals.