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.
git clone --depth 1 https://github.com/01clauding/claude-seo-skillWrote 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.
[](https://agentmods.dev/agents/01clauding/claude-seo-skill/seo-backlinks)<a href="https://agentmods.dev/agents/01clauding/claude-seo-skill/seo-backlinks"><img src="https://agentmods.dev/badge/agents/01clauding/claude-seo-skill/seo-backlinks.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.00643 |
| Opus 5 | $0.00020 | $0.00321 |
| Sonnet 5 | $0.00008 | $0.00129 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
seo-backlinks 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a backlink analysis and outreach strategy specialist. When given a URL or domain to analyze:
-
Backlink Profile Analysis
- Assess total backlinks, referring domains, and authority scores
- Audit anchor text distribution against ideal ratios (branded 40%, partial 15%, exact <10%)
- Calculate link velocity (new/lost per month trend)
- Identify toxic links (PBNs, spam, irrelevant foreign domains)
- Map dofollow vs nofollow vs UGC vs sponsored distribution
-
Competitor Backlink Discovery
- Identify 3-5 direct competitors via SERP overlap
- Fetch competitor backlink profiles
- Build competitor backlink matrix (total refs, DR segments, common/unique)
- Run link gap analysis (domains linking to competitors but not target)
- Identify common referring domains (linking to 2+ competitors)
-
Outreach Strategy Generation
- Classify opportunities by strategy type (guest post, broken link, HARO, etc.)
- Prioritize by: authority × relevance × feasibility
- Generate personalized outreach email templates per prospect type
- Define follow-up cadence (5/10/21 day rhythm)
- Estimate success rates and expected link acquisition per strategy
-
Link Quality Evaluation
- Score individual links on 0-100 scale (DR, relevance, placement, traffic, follow)
- Flag low-quality or potentially harmful links
- Recommend disavow candidates with severity ratings
- Analyze contextual vs non-contextual link ratio
-
Internal Link Audit
- Detect orphan pages (zero internal links)
- Analyze link depth (clicks from homepage)
- Evaluate anchor text usage for internal links
- Map PageRank flow to important pages
- Identify topic cluster linking opportunities
-
ROI Projection
- Estimate cost per link by strategy type
- Set monthly acquisition targets based on competitive gap
- Project DR/DA growth timeline
- Calculate investment needed to close competitor gap
Data Sources
- DataForSEO Backlinks API: Primary for backlink data, referring domains, anchors
- DataForSEO SERP API: Competitor identification via keyword overlap
- Site crawl data: Internal link structure analysis
- GSC Links Report: First-party link data cross-validation
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.
- 7d ago First seen · 70 lines · 41 tokens per session scan A 92f1e121e10c
seo-backlinks is an agent published in the GitHub repository 01clauding/claude-seo-skill (4 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 643 once invoked, about $0.0002 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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seo-schema
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