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 agentmods add rules/thatrebeccarae/claude-marketing/google-analyticsgit clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote 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/rules/thatrebeccarae/claude-marketing/google-analytics)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/google-analytics"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/google-analytics.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.03162 |
| Opus 5 | $0.00022 | $0.01581 |
| Sonnet 5 | $0.00009 | $0.00632 |
| Haiku 4.5 | $0.00004 | $0.00316 |
Grade A, and why
google-analytics 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 5d 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 — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Analytics Analysis
Analyze website performance using Google Analytics data to provide actionable insights and improvement recommendations.
Quick Start
1. Setup Authentication
This Skill requires Google Analytics API credentials. Set up environment variables:
export GOOGLE_ANALYTICS_PROPERTY_ID="your-property-id"
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
Or create a .env file in your project root:
GOOGLE_ANALYTICS_PROPERTY_ID=123456789
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
Never commit credentials to version control. The service account JSON file should be stored securely outside your repository.
2. Install Required Packages
# Option 1: Install from requirements file (recommended)
pip install -r cli-tool/components/skills/analytics/google-analytics/requirements.txt
# Option 2: Install individually
pip install google-analytics-data python-dotenv pandas
3. Analyze Your Project
Once configured, I can:
- Review current traffic and user behavior metrics
- Identify top-performing and underperforming pages
- Analyze traffic sources and conversion funnels
- Compare performance across time periods
- Suggest data-driven improvements
How to Use
Ask me questions like:
- "Review our Google Analytics performance for the last 30 days"
- "What are our top traffic sources?"
- "Which pages have the highest bounce rates?"
- "Analyze user engagement and suggest improvements"
- "Compare this month's performance to last month"
Analysis Workflow
When you ask me to analyze Google Analytics data, I will:
- Connect to the API using the helper script
- Fetch relevant metrics based on your question
- Analyze the data looking for:
- Traffic trends and patterns
- User behavior insights
- Performance bottlenecks
- Conversion opportunities
- Provide recommendations with:
- Specific improvement suggestions
- Priority level (high/medium/low)
- Expected impact
- Implementation guidance
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.
- 5d ago First seen · 500 lines · 44 tokens per session scan A b30c4c6a5d5a
google-analytics is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (130 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 3,162 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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