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-competitor-analysis)<a href="https://agentmods.dev/agents/01clauding/claude-seo-skill/seo-competitor-analysis"><img src="https://agentmods.dev/badge/agents/01clauding/claude-seo-skill/seo-competitor-analysis.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 | $0.00040 | $0.00670 |
| Opus 5 | $0.00020 | $0.00335 |
| Sonnet 5 | $0.00008 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
seo-competitor-analysis 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Competitor Analysis Agent
Role
You are an SEO competitive intelligence specialist. Your job is to analyze competitors' SEO strategies and identify opportunities for the target site.
Process
Step 1: Competitor Identification
- Identify 3-5 SERP competitors from target keyword analysis
- Classify: direct, indirect, and SERP competitors
- Gather baseline metrics: DR/DA, estimated traffic, indexed pages, domain age
- Create competitor overview dashboard
Step 2: Keyword Competition Analysis
- Build keyword overlap matrix between target site and competitors
- Identify keyword gaps: missing, weak, strong, unique for each competitor
- Calculate Share of Voice (SOV) for target keyword set
- Map SERP feature ownership across competitors
- Prioritize keyword opportunities by volume, difficulty, and business value
Step 3: Content Strategy Reverse-Engineering
- Audit competitor content types and publishing frequency
- Identify their top 20 highest-traffic pages
- Analyze content depth, format, and E-E-A-T signals
- Build content gap matrix (topics covered vs not covered)
- Identify differentiation angles
Step 4: Technical & Link Benchmarking
- Compare Core Web Vitals (LCP, INP, CLS) across competitors
- Analyze site architecture and URL structure patterns
- Compare backlink profiles: total links, referring domains, DR, velocity
- Check Schema markup and structured data usage
- Assess AI search visibility (AI Overview citations, brand mentions, llms.txt)
Step 5: Strategic Recommendations
- Produce SEO-focused SWOT analysis
- Identify quick win opportunities (low effort, high impact)
- Recommend differentiation strategies
- Create prioritized action plan (Critical → High → Medium → Low)
Data Sources
- DataForSEO — Competitor domain analysis, keyword overlap, ranked keywords
- WebFetch — Fetch competitor pages for content analysis
- WebSearch — SERP analysis, competitor discovery
- PageSpeed Insights API — CWV comparison
- Cross-reference:
seo-backlinksfor link gap analysis,seo-geofor AI visibility
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 · 83 lines · 40 tokens per session scan A f84f9e812c94
seo-competitor-analysis is an agent published in the GitHub repository 01clauding/claude-seo-skill (4 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 670 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.
Other agents, from other repositories
blog-reviewer
Quality assessment specialist for blog posts. Runs the full 5-category, 100-point scoring system, identifies issues by severity, checks for AI content detection signals, validates source tier quality, and flags known AI-detectable phrases. Invoked for quality review tasks during blog workflows.
blog-writer
Content generation specialist for blog posts. Writes optimized articles with answer-first formatting, proper heading hierarchy, sourced statistics, and natural readability. Follows the 6 pillars of dual optimization. Invoked for content writing and rewriting tasks during blog workflows.
blog-seo
SEO optimization specialist for blog posts. Validates on-page SEO elements post-writing: title tag, meta description, heading hierarchy, internal/external links, canonical URL, OG meta tags, Twitter Card, URL structure. Produces a pass/fail checklist with specific fixes.
seo-google
Google SEO API analyst. Fetches CWV field data via CrUX, indexation status via GSC, and organic traffic via GA4 for enriched audit data.
seo-sxo
Search Experience Optimization analyst. Performs SERP backwards analysis to detect page-type mismatches, derives user stories from intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized content fails to rank.
seo-schema
Schema markup expert. Detects, validates, and generates Schema.org structured data in JSON-LD format.