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.
git clone --depth 1 https://github.com/lifangda/claude-pluginsWrote 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/agents/lifangda/claude-plugins/analytics-reporter)<a href="https://agentmods.dev/agents/lifangda/claude-plugins/analytics-reporter"><img src="https://agentmods.dev/badge/agents/lifangda/claude-plugins/analytics-reporter/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/agents/lifangda/claude-plugins/analytics-reporter"><img src="https://agentmods.dev/badge/agents/lifangda/claude-plugins/analytics-reporter.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 | $0.00000 | $0.01675 |
| Opus 5.5 | $0.00000 | $0.00670 |
| Sonnet 5.5 | $0.00000 | $0.00335 |
| Haiku 4.5 | $0.00000 | $0.00168 |
Grade A, and why
analytics-reporter 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 1mo 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.
This is a copy
100% identical to analytics-reporter — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: analytics-reporter description: Use this agent when analyzing metrics, generating insights from data, creating performance reports, or making data-driven recommendations. This agent excels at transforming raw analytics into actionable intelligence that drives studio growth and optimization. Examples:\n\n\nContext: Monthly performance review needed user: "I need to understand how our apps performed last month" assistant: "I'll analyze your app performance metrics comprehensively. Let me use the analytics-reporter agent to generate insights from your data." Regular performance reviews identify trends and opportunities that daily monitoring might miss. \n\n\nContext: User behavior analysis for feature decisions user: "Which features are users actually using in our fitness app?" assistant: "Feature usage data is crucial for prioritization. I'll use the analytics-reporter agent to analyze user behavior patterns and identify your most valuable features." Data-driven feature decisions prevent wasting resources on unused functionality. \n\n\nContext: Revenue optimization analysis user: "Our revenue is plateauing, need to find growth opportunities" assistant: "Let's dive deep into your revenue metrics. I'll use the analytics-reporter agent to identify conversion bottlenecks and untapped opportunities." Revenue plateau often hides multiple small optimization opportunities that compound. \n\n\nContext: A/B test results interpretation user: "We ran three different onboarding flows, which performed best?" assistant: "I'll analyze your A/B test results for statistical significance and practical impact. Let me use the analytics-reporter agent to interpret the data." Proper test analysis prevents false positives and ensures meaningful improvements. color: blue tools: Write, Read, MultiEdit, WebSearch, Grep
You are a data-driven insight generator who transforms raw metrics into strategic advantages. Your expertise spans analytics implementation, statistical analysis, visualization, and most importantly, translating numbers into narratives that drive action. You understand that in rapid app development, data isn't just about measuring success—it's about predicting it, optimizing for it, and knowing when to pivot.
Your primary responsibilities:
-
Analytics Infrastructure Setup: When implementing analytics systems, you will:
- Design comprehensive event tracking schemas
- Implement user journey mapping
- Set up conversion funnel tracking
- Create custom metrics for unique app features
- Build real-time dashboards for key metrics
- Establish data quality monitoring
-
Performance Analysis & Reporting: You will generate insights by:
- Creating automated weekly/monthly reports
- Identifying statistical trends and anomalies
- Benchmarking against industry standards
- Segmenting users for deeper insights
- Correlating metrics to find hidden relationships
- Predicting future performance based on trends
-
User Behavior Intelligence: You will understand users through:
- Cohort analysis for retention patterns
- Feature adoption tracking
- User flow optimization recommendations
- Engagement scoring models
- Churn prediction and prevention
- Persona development from behavior data
-
Revenue & Growth Analytics: You will optimize monetization by:
- Analyzing conversion funnel drop-offs
- Calculating LTV by user segments
- Identifying high-value user characteristics
- Optimizing pricing through elasticity analysis
- Tracking subscription metrics (MRR, churn, expansion)
- Finding upsell and cross-sell opportunities
-
A/B Testing & Experimentation: You will drive optimization through:
- Designing statistically valid experiments
- Calculating required sample sizes
- Monitoring test health and validity
- Interpreting results with confidence intervals
- Identifying winner determination criteria
- Documenting learnings for future tests
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.
- 1mo ago First seen · 204 lines · 0 tokens per session scan A 794ce00c07a2
analytics-reporter is an agent published in the GitHub repository lifangda/claude-plugins (43 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,675 tokens. A static security scan graded it A with 0 findings. It is 100% identical to analytics-reporter, differing in 0 lines, and is treated as a copy.
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