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-log-analysisgit 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-log-analysis)<a href="https://agentmods.dev/skills/lionkiii/claude-seo-skills/seo-log-analysis"><img src="https://agentmods.dev/badge/skills/lionkiii/claude-seo-skills/seo-log-analysis/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-log-analysis"><img src="https://agentmods.dev/badge/skills/lionkiii/claude-seo-skills/seo-log-analysis.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 | $0.00085 | $0.01302 |
| Opus 5 | $0.00043 | $0.00651 |
| Sonnet 5 | $0.00017 | $0.00260 |
| Haiku 4.5 | $0.00009 | $0.00130 |
Grade A, and why
seo-log-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 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Server Log Analysis
Analyzes local server log files for crawl budget breakdown. No MCP or external calls required.
Inputs
file: Absolute path to server log file (Apache Combined, Apache Common, or Nginx access log). If user provides relative path, resolve withBash: realpath <path>.
Execution
Step 1: Format Detection
Read the first 10 lines of the log file to detect format:
- Apache Combined:
%h %l %u %t "%r" %>s %b "%{Referer}i" "%{User-agent}i"— 9+ fields, has referer and UA in quotes - Apache Common:
%h %l %u %t "%r" %>s %b— 7 fields, no referer/UA - Nginx: similar to Apache Combined with slight field order differences
- Check for compressed files (.gz) — if detected, inform user to decompress first
Step 2: Parse Log Lines
Use Bash awk to extract fields. For Apache Combined/Nginx format (9 fields):
awk '{
ip=$1; method_url=$7; status=$9; ua=$0
match($0, /"([^"]+)"$/, arr) # Extract UA from last quoted field
print ip, $7, $9, arr[1]
}' logfile
For Apache Common (7 fields): ip=$1, request=$7, status=$9, ua="unknown"
Step 3: Classify User-Agents
Group each request into categories:
- Googlebot:
Googlebot,Googlebot-Image,Googlebot-News,AdsBot-Google - Bingbot:
bingbot,BingPreview,MicrosoftPreview - Other search bots:
Slurp(Yahoo),DuckDuckBot,Baiduspider,YandexBot,Sogou - AI crawlers:
GPTBot,ClaudeBot,PerplexityBot,Bytespider,CCBot,anthropic-ai - Monitoring tools:
Pingdom,UptimeRobot,StatusCake,NewRelic,Datadog - Real users: everything else (browsers:
Mozilla,Chrome,Safari,Firefox,Edge) - Unknown: no UA or unrecognized
Step 4: Calculate Metrics
Using awk/grep on the log file:
- Total request count
- Requests by bot category (count per category, % of total)
- Requests by HTTP status code (200, 301, 302, 404, 500, etc.)
- Top 20 crawled URLs by frequency — sort by count descending
- Top 10 crawled path prefixes (first 2 URL segments, e.g.,
/blog/,/products/) — aggregate by prefix - Requests by hour-of-day (extract hour from timestamp field
[DD/Mon/YYYY:HH:MM:SS])
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 · 133 lines · 85 tokens per session scan A a48053bf4408
seo-log-analysis is a skill published in the GitHub repository lionkiii/claude-seo-skills (20 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,302 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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