seo-backlink-gap

seo-backlink-gap is a skill for Claude Code from amirjahfar1/automate-seo-with-claude. It costs 76 tokens per session (1,312 once invoked), scanned A, original, MIT.

A backlink-gap workflow that finds websites linking to competitors but not to the target site. A backlink is a link from another website to yours.

In plain words
What is it for?
It helps compare a target domain with three to five competitors, filter referring websites, assess relevance, and prepare a prospect list for outreach.
Why use it?
It turns competitor link data into a focused list of possible link-building contacts instead of requiring manual comparison. Each prospect can include authority, example anchor text, and an outreach idea.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the seo-skills plugin — 26 skills shipped together

Good fit It helps compare a target domain with three to five competitors, filter referring websites, assess relevance, and prepare a prospect list for outreach.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add amirjahfar1/automate-seo-with-claude --skill seo-backlink-gap
Clone the repo
git clone --depth 1 https://github.com/amirjahfar1/automate-seo-with-claude

Made for: Claude Code.

Or install seo-skills, the plugin that ships this one along with the rest of its 26 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for seo-backlink-gap

README.md
[![agentmods](https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap/github.svg)](https://agentmods.dev/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap)
Your own site
<a href="https://agentmods.dev/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for seo-backlink-gap

Your own site · 80×15
<a href="https://agentmods.dev/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap"><img src="https://agentmods.dev/badge/skills/amirjahfar1/automate-seo-with-claude/seo-backlink-gap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,312 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00076 $0.01312
Opus 5 $0.00038 $0.00656
Sonnet 5 $0.00015 $0.00262
Haiku 4.5 $0.00008 $0.00131

Measured 12d ago against content hash 5df068546e2e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

seo-backlink-gap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/seo-backlink-gap/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Example output: examples/seo-backlink-gap-linear-app-20260514/REPORT.md

Produce an actionable link-prospecting list: domains linking to your top competitors but not to you, filtered by authority and relevance, enriched with anchor samples and a suggested outreach angle.

Prerequisites

  • DataForSEO MCP server connected.
  • Claude's WebFetch tool available (used for prospect scoring and topical relevance checks).
  • User provides: (a) target domain, (b) 3 to 5 competitor domains, and optionally (c) minimum referring-domain rank (default: 250 on the 0–1000 scale), (d) dofollow-only filter (default: true), (e) minimum intersection count (default: linked by at least 2 of the N competitors).

Process

  1. Baseline target backlinks mcp__dataforseo__backlinks_referring_domains, mcp__dataforseo__backlinks_summary

    • Pull the target domain's existing referring domains to build an exclusion set.
    • Save count and summary metrics for the report.
  2. Competitor intersection mcp__dataforseo__backlinks_domain_intersection (with mcp__dataforseo__backlinks_competitors to validate competitor set)

    • Use backlinks_domain_intersection to pull referring domains that link to the competitors. Apply a dofollow filters clause and a rank floor of {threshold} to narrow.
    • Collect as a dict keyed by referring domain with the list of competitors linking to it.
  3. Intersection

    • Retain referring domains that link to at least the minimum number of competitors.
    • Exclude any domain already in the target's backlink set.
  4. Enrichment mcp__dataforseo__backlinks_bulk_ranks, mcp__dataforseo__backlinks_anchors

    • For each candidate, pull: domain rank, page rank of the linking page, linking-to-competitor anchor samples (top 3 anchors), and country if available.
    • Classify by link type: editorial, resource list, directory, forum/UGC.
  5. Relevance scoring

    • Score each candidate on: (a) topical overlap (use domain homepage title/meta via WebFetch), (b) rank, (c) intersection count, (d) link-type preference.
    • Suggest an outreach angle per row: local fit, topical fit, competitive parity, resource-list inclusion, broken link, etc.

Read the full file on GitHub · 100 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 100 lines · 76 tokens per session scan A 5df068546e2e

Subscribe to this mod's changes

seo-backlink-gap is a skill published in the GitHub repository amirjahfar1/automate-seo-with-claude (2 stars, last pushed 3mo ago), licensed MIT. It adds 76 tokens to every session and 1,312 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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