Skip to content
FunCoding

Search

Search docs, Skills and MCP

backlink-gap

Find referring domains that link to your competitors but not to you, ranked by an outreach-priority score (0.40 DR + 0.25 link-overlap + 0.20 traffic + 0.15 topical relevance) — outputs a four-gate quality scorecard, a 30-prospect outreach shortlist, broken-link candidates, and pre-filled outreach templates. Triggers on "/digital-marketing-pro:backlink-gap", "where are competitors getting links we aren't", "plan a link-building campaign", "quarterly backlink audit", "first 50 link targets for a new client". Consumes backlink CSV exports from the brand's connected backlink MCP, runs scripts/backlink_gap.py, reads the brand profile for DR thresholds and voice, and hands off to /digital-marketing-pro:digital-pr and /digital-marketing-pro:pr-pitch.

AI 与智能体856skills/backlink-gap/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/indranilbanerjee/digital-marketing-pro/backlink-gap/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Purpose

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.

Context efficiency

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.

When to Use

  • Quarterly backlink audit — "where did our competitors grow links this quarter and we didn't?"
  • Pre-launch link-building plan for a new product or content hub
  • Digital PR qualification — separating "would-link-to-anyone" prospects from "high-confidence-will-link-to-our-space"
  • Competitive recovery — a competitor displaced you and you want to know which links moved
  • Onboarding a new client and need a "first 50 link targets" backlog

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).

Brand context (auto-applied)

  1. Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json
  2. If no brand exists: ask "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults
  3. Apply skills/context-engine/industry-profiles.md for industry-specific link-quality thresholds (YMYL industries should set higher --min-dr)
  4. Apply skills/context-engine/compliance-rules.md to filter out blocked publishers (e.g., PBN-style or paid-link networks the brand has explicitly banned)

Inputs

InputSourceRequired?
Our backlinks CSVExport from connected backlink MCP (Ahrefs / Semrush / SE Ranking / Moz) for the brand's primary domainyes
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 filterCLI flag, brand-profile default, or industry standardoptional
Top-N countHow many prospects to surfaceoptional

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."

Process (10 steps, numbered-file output)

All outputs go to ${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{YYYY-MM-DD}/.

  1. 00-input.md — capture our domain, competitor list (with rationale: why these N?), filter parameters, run timestamp
  2. 01-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.
  3. 02-ours.csv — our backlink export (raw)
  4. 03-comp-{competitor}.csv — one CSV per competitor (raw)
  5. 04-gap-run.json — run the script:
    python "${CLAUDE_PLUGIN_ROOT}/scripts/backlink_gap.py" \
        --ours "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/02-ours.csv" \
        --competitors \
          "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/03-comp-competitor1.csv" \
          "${CLAUDE_PLUGIN_DATA}/{brand}/seo/backlink-gap/{date}/03-comp-competitor2.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"
    
    --competitors takes an explicit space-separated list of CSV paths (nargs="+") — enumerate each 03-comp-*.csv file; the script does not expand a * glob, so a quoted 03-comp-*.csv would fail with FileNotFoundError. List one path per competitor.
  6. 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.
  7. 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)
  8. 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.
  9. 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 the brand profile's voice fields + skills/context-engine/guidelines-framework.md.
  10. PLAN.md — single-page summary: stats + scorecard + top 10 prospects with outreach angle + recommended cadence (3-5 pitches/week for sustainable outreach quality).

Output format

${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

Quality scorecard (the four gates)

GateWhat it checksWhy it matters
data_freshnessAll input CSVs have mtime within 90 daysBacklink graphs decay fast — stale data sends you chasing dead links
sample_sizeEach input has ≥ 50 unique referring domainsBelow this, the gap math has too little signal to rank
competitor_coverage≥ 2 competitor CSVs suppliedThe "shared signal" is what separates real prospects from noise
link_overlap_signal≥ 5 referring domains link to ≥ 2 of the competitorsIf 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 score (0–1, displayed in 04-gap-run.json)

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.

After the audit

Ask: "Would you like me to:

  • Send the top 10 prospects to a digital PR workflow? (/digital-marketing-pro:digital-pr)
  • Draft pitches for the top 5 broken-link replacements? (/digital-marketing-pro:pr-pitch)
  • Schedule quarterly re-runs to track gains? (/digital-marketing-pro:seo-drift)
  • Open the prospect shortlist for review?"

Chain handoffs

This skill is a producer in a longer chain:

  1. /digital-marketing-pro:competitor-analysis — picks the right competitors
  2. /digital-marketing-pro:backlink-gap — this skill
  3. /digital-marketing-pro:digital-pr — consumes 06-prospect-shortlist.md + 08-outreach-templates.md
  4. /digital-marketing-pro:pr-pitch — drafts individual pitches per prospect
  5. /digital-marketing-pro:performance-report — quarterly re-runs of this skill feed the "links gained" KPI

Tips & caveats

  • More competitors ≠ better. Three to five focused competitors beats ten random ones. The "shared signal" gate works best when all competitors are in the same content space.
  • DR/DA from different exporters aren't comparable. Don't mix an Ahrefs export with a Moz export — the script doesn't know to normalise across exporters. Pick one provider per audit.
  • Topical relevance is the weakest signal in most exports because few exporters provide it well. The script defaults to 0.5 if absent, which is the right neutral. Override only if you have a curated topical-relevance score.
  • Don't outreach 100 prospects in one week. The output is a backlog, not a queue. Sustainable cadence: 3-5 highly personalised pitches per week per outreach lead.
  • Broken-link candidates tend to have the highest hit rate (broken-link replacement pitches typically out-reply cold pitches by a wide margin — the "30-60% vs 5-15%" figures are an illustrative rule of thumb, not measured; validate against your own outreach data) — always work the 07-broken-link-candidates.md list first.
  • Re-run quarterly, not monthly. Backlink data moves slowly enough that monthly runs mostly produce noise.
  • YMYL industries (health, finance, legal) should set --min-dr 40 to filter out low-authority publishers that could damage E-E-A-T.

Agents used

  • seo-specialist (primary) — interpretation of prospect quality
  • competitive-intel — competitor-set selection rationale (Step 1)
  • pr-outreach — outreach template drafting (Step 8)
  • brand-guardian — banned-publisher filter at Step 6

See also

  • /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 health
  • scripts/backlink_gap.py — the underlying gap engine

Similar Skills

brand-guidelines
anthropics/skills180k

brand-guidelines

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

AI & agents

internal-comms
anthropics/skills180k

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

AI & agents

template-skill
anthropics/skills180k

template-skill

Replace with description of the skill and when Claude should use it.

AI & agents

mcp-builder
anthropics/skills180k

mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

AI & agents

algorithmic-art
anthropics/skills180k

algorithmic-art

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

AI & agents

academy-guide
anthropics/skills180k

academy-guide

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

AI & agents