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 samkawsarani/sams-product-plugins --skill analyze-metricsgit clone --depth 1 https://github.com/samkawsarani/sams-product-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/skills/samkawsarani/sams-product-plugins/analyze-metrics)<a href="https://agentmods.dev/skills/samkawsarani/sams-product-plugins/analyze-metrics"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-metrics/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/samkawsarani/sams-product-plugins/analyze-metrics"><img src="https://agentmods.dev/badge/skills/samkawsarani/sams-product-plugins/analyze-metrics.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 | $0.00095 | $0.01256 |
| Opus 5 | $0.00048 | $0.00628 |
| Sonnet 5 | $0.00019 | $0.00251 |
| Haiku 4.5 | $0.00010 | $0.00126 |
Grade A, and why
analyze-metrics 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 11d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dependency Check
Before starting, verify required dependencies:
- python3 (required): Run
command -v python3.- If missing: Tell the user: "Python 3 is required to run metric calculation scripts. Install it with
brew install pythonorbrew install uv && uv python install, then try again." Stop here.
- If missing: Tell the user: "Python 3 is required to run metric calculation scripts. Install it with
Product Metrics Analysis
Analyze product data to surface insights, identify patterns, and provide actionable recommendations. Apply appropriate PM frameworks based on the question and data at hand.
Core Capabilities
- Pattern Recognition - Identify trends, anomalies, and insights in product data
- Framework Application - Apply AARRR, North Star, PMF metrics, or cohort analysis as appropriate
- Metric Calculation - Calculate growth rates, conversion rates, retention, and other key metrics
- Actionable Insights - Surface specific, concrete recommendations based on the data
Analysis Workflow
1. Understand the Context
Before diving into data, clarify:
- What question is being asked?
- What type of data is available?
- What time period or cohorts are relevant?
- What product/feature is being analyzed?
2. Choose the Right Framework
Select the framework that best fits the question:
- AARRR (Pirate Metrics) - For full-funnel analysis or identifying bottlenecks
- Product-Market Fit Metrics - For evaluating early traction or new feature adoption
- Cohort Analysis - For understanding retention or behavior over time
- North Star Framework - For strategic alignment or prioritization decisions
→ Read references/frameworks.md for detailed framework guidance and when to use each.
3. Calculate Metrics
Use scripts/calculate_metrics.py for common calculations:
# Core product metrics
python scripts/calculate_metrics.py growth_rate <old_value> <new_value>
python scripts/calculate_metrics.py conversion_rate <conversions> <total>
python scripts/calculate_metrics.py retention_rate <active_users> <cohort_size>
python scripts/calculate_metrics.py churn_rate <churned_users> <starting_users>
python scripts/calculate_metrics.py dau_mau_ratio <dau> <mau>
python scripts/calculate_metrics.py ltv <arpu> <churn_rate>
python scripts/calculate_metrics.py ltv_cac_ratio <ltv> <cac>
python scripts/calculate_metrics.py arpu <total_revenue> <total_users>
python scripts/calculate_metrics.py funnel <step1> <step2> <step3> ...
# Fintech/Payments metrics
python scripts/calculate_metrics.py take_rate <revenue> <tpv>
python scripts/calculate_metrics.py payment_acceptance_rate <approved> <total_attempts>
python scripts/calculate_metrics.py chargeback_rate <chargebacks> <total_transactions>
python scripts/calculate_metrics.py atv <tpv> <num_transactions>
python scripts/calculate_metrics.py fraud_rate <fraudulent> <total_transactions>
python scripts/calculate_metrics.py net_revenue <gross_revenue> <processing_fees> <chargebacks> <refunds>
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 126 lines · 95 tokens per session scan A 893b558e70a4
analyze-metrics is a skill published in the GitHub repository samkawsarani/sams-product-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,256 once invoked, about $0.0005 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 2026-08-31.
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