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 bot-traffic-detectiongit 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/bot-traffic-detection)<a href="https://agentmods.dev/skills/surendranb/google-analytics-mcp/bot-traffic-detection"><img src="https://agentmods.dev/badge/skills/surendranb/google-analytics-mcp/bot-traffic-detection.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.00589 |
| Opus 5 | $0.00010 | $0.00295 |
| Sonnet 5 | $0.00004 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
bot-traffic-detection 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 9d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bot Traffic Detection
Identify and exclude bot, scraper, and spam sessions from GA4 data.
When to use
- Traffic has unexplained spikes that don't correlate with any activity
- Bounce rate is 0% or near-100% across many sessions
- Sessions show 0 engagement time but high pageview counts
- Referrers look suspicious (random domains, unrecognised TLDs)
- You want to establish a clean baseline before any analysis
How to detect
Step 1 — Check engagement time distribution
Query sessions with very low or zero engagement:
dimensions: ["sessionDefaultChannelGroup", "sessionSource", "sessionMedium"]
metrics: ["sessions", "userEngagementDuration", "bounceRate", "screenPageViewsPerSession"]
date_range: last 7–14 days
Bot signals: userEngagementDuration near 0, screenPageViewsPerSession exactly 1,
bounceRate at 1.0 (100%).
Step 2 — Inspect referrer sources
dimensions: ["sessionSource", "sessionMedium", "sessionDefaultChannelGroup"]
metrics: ["sessions", "userEngagementDuration", "newUsers"]
dimension_filter: sessionMedium = "referral"
Flag sources where userEngagementDuration / sessions < 2 seconds.
Step 3 — Check hostname
dimensions: ["hostname"]
metrics: ["sessions", "screenPageViews"]
Bot traffic often hits unexpected hostnames (staging domains, raw IPs, or hostnames you don't own). Filter to your known production hostnames.
Step 4 — Geographic anomalies
dimensions: ["country", "city", "sessionSource"]
metrics: ["sessions", "userEngagementDuration"]
dimension_filter: country = [countries with no expected traffic]
Clusters of sessions from unexpected countries with 0 engagement = bot signal.
Exclusion approach
GA4 does not have a native bot filter toggle beyond the automatic Google filter. To exclude suspected bot traffic from your analysis, add a dimension filter:
dimension_filter: {
"filter": {
"fieldName": "sessionDefaultChannelGroup",
"stringFilter": {"matchType": "EXACT", "value": "Direct"}
}
}
…combined with a metric filter on userEngagementDuration > 0.
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.
- 9d ago First seen · 78 lines · 21 tokens per session scan A 5f12bdee7080
bot-traffic-detection is a skill published in the GitHub repository surendranb/google-analytics-mcp (241 stars, last pushed 6d ago), licensed MIT. It adds 21 tokens to every session and 589 once invoked, about $0.0001 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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