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 surendranb/google-analytics-mcp --skill ai-referral-analysisgit clone --depth 1 https://github.com/surendranb/google-analytics-mcpWrote 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/surendranb/google-analytics-mcp/ai-referral-analysis)<a href="https://agentmods.dev/skills/surendranb/google-analytics-mcp/ai-referral-analysis"><img src="https://agentmods.dev/badge/skills/surendranb/google-analytics-mcp/ai-referral-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/surendranb/google-analytics-mcp/ai-referral-analysis"><img src="https://agentmods.dev/badge/skills/surendranb/google-analytics-mcp/ai-referral-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.00620 |
| Opus 5 | $0.00019 | $0.00310 |
| Sonnet 5 | $0.00008 | $0.00124 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
ai-referral-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 10d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Referral Analysis
Measure traffic arriving from AI tools — ChatGPT, Claude, Perplexity, Gemini, Copilot, and others — and understand how it behaves compared to other channels.
When to use
- You want to quantify how much of your traffic comes from AI assistants
- You're tracking whether AI-driven discovery is growing over time
- You want to compare AI referral quality (engagement, conversion) to SEO or direct
Known AI referral sources
These domains appear as sessionSource in GA4 when users click links from AI tools:
| Tool | Source domains |
|---|---|
| ChatGPT | chatgpt.com, chat.openai.com |
| Claude | claude.ai |
| Perplexity | perplexity.ai |
| Gemini | gemini.google.com |
| Copilot | copilot.microsoft.com, bing.com (when AI-driven) |
| You.com | you.com |
| Grok | grok.x.ai, x.com |
Step 1 — Volume and trend
dimensions: ["date", "sessionSource"]
metrics: ["sessions", "newUsers", "userEngagementDuration", "screenPageViews"]
dimension_filter: sessionSource contains "chatgpt.com" OR "perplexity.ai" OR
"claude.ai" OR "gemini.google.com" OR "copilot.microsoft.com"
date_range: last 30–90 days
Use date as a dimension to see the growth trend.
Step 2 — Quality comparison
Compare AI referral quality against your other top channels:
dimensions: ["sessionDefaultChannelGroup", "sessionSource"]
metrics: ["sessions", "userEngagementDuration", "screenPageViewsPerSession",
"keyEvents", "bounceRate"]
date_range: last 30 days
Then filter the results to AI sources and compare engagement metrics
against Organic Search and Direct.
Step 3 — Which pages AI drives traffic to
dimensions: ["sessionSource", "landingPage"]
metrics: ["sessions", "userEngagementDuration", "keyEvents"]
dimension_filter: sessionSource contains "chatgpt.com" OR "perplexity.ai" OR "claude.ai"
date_range: last 30 days
order_by: sessions DESC
This shows which content AI tools are citing and sending users to.
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.
- 10d ago First seen · 69 lines · 39 tokens per session scan A 07f24e0ed058
ai-referral-analysis is a skill published in the GitHub repository surendranb/google-analytics-mcp (241 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 620 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 2026-08-30.
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