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.
npx skills add lionkiii/claude-seo-skills --skill seo-ai-content-checkgit 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/skills/lionkiii/claude-seo-skills/seo-ai-content-check)<a href="https://agentmods.dev/skills/lionkiii/claude-seo-skills/seo-ai-content-check"><img src="https://agentmods.dev/badge/skills/lionkiii/claude-seo-skills/seo-ai-content-check/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/skills/lionkiii/claude-seo-skills/seo-ai-content-check"><img src="https://agentmods.dev/badge/skills/lionkiii/claude-seo-skills/seo-ai-content-check.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 | $0.00086 | $0.01438 |
| Opus 5 | $0.00043 | $0.00719 |
| Sonnet 5 | $0.00017 | $0.00288 |
| Haiku 4.5 | $0.00009 | $0.00144 |
Grade A, and why
seo-ai-content-check 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.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Content Check
Analyzes text for AI-generated content indicators using writing pattern analysis. No external API calls — pure local text analysis.
Inputs
target: URL or file path.- If URL: fetch content with WebFetch, extract plain text from HTML body.
- If file path: read directly with Read tool. Accepts .md, .txt, .html.
- Strip HTML tags, navigation, headers/footers before analysis. Analyze body copy only.
Execution
Perform all 6 analysis checks, then compute the confidence score.
Check 1: Sentence Structure Uniformity (weight: 20%)
Split text into sentences on ., !, ? (ignore abbreviations like "e.g.", "vs.", "etc.").
Calculate word count per sentence. Compute standard deviation of sentence lengths.
- SD < 3 words: FLAGGED (very uniform — AI pattern)
- SD 3-6 words: WARNING
- SD > 6 words: PASS (natural variance)
Score: 0 if SD > 6, 50 if SD 3-6, 100 if SD < 3 (higher = more AI-like)
Check 2: Vocabulary Diversity — Type-Token Ratio (weight: 20%)
Tokenize text into lowercase words (strip punctuation). For each 500-word sliding window: Calculate TTR = unique words / total words.
- TTR < 0.4: FLAGGED (low diversity — AI pattern)
- TTR 0.4-0.55: WARNING
- TTR > 0.55: PASS
If fewer than 500 words, analyze full text. Report worst window TTR. Score: 0 if TTR > 0.55, 50 if 0.4-0.55, 100 if < 0.4
Check 3: Repetition Patterns (weight: 20%)
Detect over-used AI phrases — flag any of these appearing in the text: "In conclusion", "It's worth noting", "It's important to note", "At the end of the day", "In today's digital landscape", "In today's fast-paced", "In today's modern", "Delve into", "Navigating the", "Leverage", "Cutting-edge", "Revolutionize", "Seamlessly", "Robust solution", "Comprehensive guide", "Game-changer", "Deep dive", "Unlock the potential", "Dive into"
Also detect 3+ word repeated phrases appearing 3+ times in text (using Bash grep -o). Score: 0 if 0 phrases flagged, 25 per flagged phrase (max 100)
Check 4: Hedging Language Rate (weight: 15%)
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 · 141 lines · 86 tokens per session scan A a9fc19a6293b
seo-ai-content-check is a skill published in the GitHub repository lionkiii/claude-seo-skills (20 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,438 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 2026-08-30.
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