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Finds referring domains linking to competitors but not to you, ranked by outreach-priority score combining DR, link-overlap, traffic, and topical relevance. Use for link-building campaigns, digital-PR qualification, or quarterly backlink-gap audits.
npx claudepluginhub indranilbanerjee/digital-marketing-proHow this skill is triggered — by the user, by Claude, or both
Slash command
/digital-marketing-pro:backlink-gap [brand-name][brand-name]The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Identify the highest-leverage backlink prospects — domains that link to multiple competitors but not to you — and rank them by an opinionated priority score that combines authority, link-overlap signal, downstream traffic, and topical relevance. Produces a numbered output bundle ready for outreach handoff.
Finds referring domains linking to multiple competitors but not to the user's site, then enriches each prospect with authority metrics, anchor samples, and a suggested outreach angle. Produces a ranked CSV prospect list ready for outreach execution.
Analyzes backlink profiles including referring domains, anchors, link quality, link gaps, and competitor link profiles using DataForSEO.
Analyzes competitor backlinks, identifies toxic links, discovers unlinked brand mentions, and generates outreach plans and monthly SEO reports.
Share bugs, ideas, or general feedback.
Identify the highest-leverage backlink prospects — domains that link to multiple competitors but not to you — and rank them by an opinionated priority score that combines authority, link-overlap signal, downstream traffic, and topical relevance. Produces a numbered output bundle ready for outreach handoff.
Heavy skill. Grep before Read any referenced file, then Read only matched ranges with offset + limit. List ${CLAUDE_PLUGIN_DATA}/<brand>/ before opening files. On re-invocation mid-session, skip files already in context.
Don't use when you just need backlink quantity numbers (use the brand's connected backlink MCP directly) or when you need anchor-text analysis of your own profile (that's a separate audit — covered in seo-audit).
~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.jsonskills/context-engine/industry-profiles.md for industry-specific link-quality thresholds (YMYL industries should set higher --min-dr)skills/context-engine/compliance-rules.md to filter out blocked publishers (e.g., PBN-style or paid-link networks the brand has explicitly banned)| Input | Source | Required? |
|---|---|---|
| Our backlinks CSV | Export from connected backlink MCP (Ahrefs / Semrush / SE Ranking / Moz) for the brand's primary domain | yes |
| Competitor backlinks CSVs (2+) | Same exporter, one per competitor (2 minimum for the link-overlap signal; 3-5 is the sweet spot) | yes |
| Min DR / DA filter | CLI flag, brand-profile default, or industry standard | optional |
| Top-N count | How many prospects to surface | optional |
One competitor is allowed (the script warns rather than errors) but the resulting "shared signal" is noise — single-competitor gap analysis is really just "who links to them" rather than "who consistently links in our space."
All outputs go to ${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{YYYY-MM-DD}/.
00-input.md — capture our domain, competitor list (with rationale: why these N?), filter parameters, run timestamp01-data-pull.md — pull backlinks for {brand}.tld and each competitor via brand's connected backlink MCP. Budget guard: if the MCP exposes credit cost, sum estimated cost and ask "Continue? (y/N — default N)" before fetching when total > 200 credits.02-ours.csv — our backlink export (raw)03-comp-{competitor}.csv — one CSV per competitor (raw)04-gap-run.json — run the script:
python "scripts/backlink_gap.py" \
--ours "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/02-ours.csv" \
--competitors "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/03-comp-*.csv" \
--min-dr {brand.profile.min_link_dr or 20} \
--top 100 \
--out "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/04-gap-run.json"
05-quality-scorecard.md — read quality_scorecard from 04-gap-run.json. If status: needs_review, diagnose:
data_freshness: fail → input CSV(s) older than 90 days. Re-pull data; backlink graphs decay fast.sample_size: fail → any input < 50 unique referring domains. Either the domain is too new or the export was truncated. Re-export with no row limit.competitor_coverage: warn → only 1 competitor. Add at least 1 more for genuine overlap signal.link_overlap_signal: fail → fewer than 5 referring domains link to ≥2 competitors. Either competitors are poorly chosen (they don't share a content space with each other) or the data is incomplete. Re-choose competitors.06-prospect-shortlist.md — top 30 prospects, formatted for outreach handoff: domain, DR, link count across competitors, suggested outreach angle (guest post, broken-link, resource-page mention)07-broken-link-candidates.md — subset where one or more competitor links return 4xx (run a quick HTTP HEAD pass on competitor backlink URLs — use the brand's connected web-fetch MCP). These are "easy wins" — pitch your URL as the replacement.08-outreach-templates.md — three template variants: (a) cold-pitch resource-page, (b) broken-link replacement, (c) competitor mention. Each pre-filled with brand voice from skills/context-engine/brand-voice-controls.md.PLAN.md — single-page summary: stats + scorecard + top 10 prospects with outreach angle + recommended cadence (3-5 pitches/week for sustainable outreach quality).${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/2026-06-04/
├── 00-input.md
├── 01-data-pull.md
├── 02-ours.csv
├── 03-comp-{competitor1}.csv
├── 03-comp-{competitor2}.csv
├── ...
├── 04-gap-run.json
├── 05-quality-scorecard.md
├── 06-prospect-shortlist.md
├── 07-broken-link-candidates.md
├── 08-outreach-templates.md
└── PLAN.md
| Gate | What it checks | Why it matters |
|---|---|---|
| data_freshness | All input CSVs have mtime within 90 days | Backlink graphs decay fast — stale data sends you chasing dead links |
| sample_size | Each input has ≥ 50 unique referring domains | Below this, the gap math has too little signal to rank |
| competitor_coverage | ≥ 2 competitor CSVs supplied | The "shared signal" is what separates real prospects from noise |
| link_overlap_signal | ≥ 5 referring domains link to ≥ 2 of the competitors | If no domains shared, your competitors aren't actually competing in the same content space |
status: ready requires all four gates pass (competitor_coverage: warn does not block — it's a soft signal).
priority = 0.40 × DR_normalised
+ 0.25 × link_count_normalised (how many competitors this domain links to)
+ 0.20 × traffic_normalised
+ 0.15 × topical_relevance
Why link_count is weighted higher than traffic: a domain that links to 3/3 competitors is unambiguously in your space and willing to link. A high-traffic domain that only links to 1 might just be a tier-1 publisher who happens to have covered one of you in passing.
This skill is a producer in a longer chain:
/digital-marketing-pro:competitor-analysis — picks the right competitors/digital-marketing-pro:backlink-gap — this skill/digital-marketing-pro:digital-pr — consumes 06-prospect-shortlist.md + 08-outreach-templates.md/digital-marketing-pro:pr-pitch — drafts individual pitches per prospect/digital-marketing-pro:performance-report — quarterly re-runs of this skill feed the "links gained" KPI07-broken-link-candidates.md list first.--min-dr 40 to filter out low-authority publishers that could damage E-E-A-T.seo-specialist (primary) — interpretation of prospect qualitycompetitive-intel — competitor-set selection rationale (Step 1)pr-outreach — outreach template drafting (Step 8)brand-guardian — banned-publisher filter at Step 6/digital-marketing-pro:competitor-analysis — pick the competitors for this audit/digital-marketing-pro:digital-pr — runs the actual outreach/digital-marketing-pro:seo-drift — re-run quarterly to track delta/digital-marketing-pro:seo-audit — broader site-level audit including own-profile healthscripts/backlink_gap.py — the underlying gap engine