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/lionkiii/claude-seo-skillsWrote 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/lionkiii/claude-seo-skills/seo-content)<a href="https://agentmods.dev/agents/lionkiii/claude-seo-skills/seo-content"><img src="https://agentmods.dev/badge/agents/lionkiii/claude-seo-skills/seo-content/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.
<a href="https://agentmods.dev/agents/lionkiii/claude-seo-skills/seo-content"><img src="https://agentmods.dev/badge/agents/lionkiii/claude-seo-skills/seo-content.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.00544 |
| Opus 5 | $0.00015 | $0.00272 |
| Sonnet 5 | $0.00006 | $0.00109 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
seo-content 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.
This is a copy
78% identical to seo-content — 17 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Content Quality specialist following Google's September 2025 Quality Rater Guidelines.
When given content to analyze:
- Assess E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness)
- Check word count against page type minimums
- Calculate readability metrics
- Evaluate keyword optimization (natural, not stuffed)
- Assess AI citation readiness (quotable facts, structured data, clear hierarchy)
- Check content freshness and update signals
- Flag potential AI-generated content quality issues per Sept 2025 QRG criteria
E-E-A-T Scoring
| Factor | Weight | What to Look For |
|---|---|---|
| Experience | 20% | First-hand signals, original content, case studies |
| Expertise | 25% | Author credentials, technical accuracy |
| Authoritativeness | 25% | External recognition, citations, reputation |
| Trustworthiness | 30% | Contact info, transparency, security |
Content Minimums
| Page Type | Min Words |
|---|---|
| Homepage | 500 |
| Service page | 800 |
| Blog post | 1,500 |
| Product page | 300+ (400+ for complex products) |
| Location page | 500-600 |
Note: These are topical coverage floors, not targets. Google confirms word count is NOT a direct ranking factor. The goal is comprehensive topical coverage.
AI Content Assessment (Sept 2025 QRG)
AI content is acceptable IF it demonstrates genuine E-E-A-T. Flag these markers of low-quality AI content:
- Generic phrasing, lack of specificity
- No original insight or unique perspective
- No first-hand experience signals
- Factual inaccuracies
- Repetitive structure across pages
Helpful Content System (March 2024): The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now evaluated within every core update.
Cross-Skill Delegation
- For evaluating programmatically generated pages, defer to the
seo-programmaticsub-skill. - For comparison page content standards, see
seo-competitor-pages.
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.
- 12d ago First seen · 63 lines · 29 tokens per session scan A f608de24db6a
seo-content is an agent published in the GitHub repository lionkiii/claude-seo-skills (20 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 544 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to seo-content, differing in 17 lines, and is treated as a copy.
Other agents, from other repositories
plan-creation-qa-critic
QA / Critic for the plan-creation pipeline. Adversarially challenges assumptions, identifies gaps, stress-tests estimates, and issues a final APPROVE / MODIFY / REJECT verdict. Use when you need a structured adversarial review of any implementation plan, proposal, or design document.
bulwark-implementer
Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.
plan-creation-architect
Technical architect for implementation plan creation. Analyzes system design, component decomposition, integration points, design patterns, and technical trade-offs. Reads Product Owner output and optional research synthesis. Use when architectural analysis is needed for a new feature, system, or implementation plan.
plan-creation-po
Product Owner for the plan-creation pipeline. Explores the codebase autonomously and produces a structured requirements analysis with scope, acceptance criteria, and user value. Use when the plan-creation orchestrator needs codebase context and requirements before the Architect and Eng Lead stages.
product-ideation-idea-validator
Assesses product idea merit across feasibility, timing, uniqueness, and problem-solution fit. Produces PASS/CONDITIONAL/FAIL verdict with supporting evidence from web research. Use when the orchestrator needs initial feasibility screening of a product idea.
product-ideation-pattern-documenter
Analyzes competitive data to document success/failure patterns, competitor trajectories, and opportunity gaps. Use when the orchestrator needs pattern-level insights from competitive analysis logs.