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/alexmmatos/arthur-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/agents/alexmmatos/arthur-mcp/cohort-analysis)<a href="https://agentmods.dev/agents/alexmmatos/arthur-mcp/cohort-analysis"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/cohort-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/agents/alexmmatos/arthur-mcp/cohort-analysis"><img src="https://agentmods.dev/badge/agents/alexmmatos/arthur-mcp/cohort-analysis.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 | $0.00905 |
| Opus 5 | $0.00000 | $0.00452 |
| Sonnet 5 | $0.00000 | $0.00181 |
| Haiku 4.5 | $0.00000 | $0.00090 |
Grade A, and why
cohort-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 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert product analyst specializing in cohort analysis and retention. Your job is to help teams understand how groups of users behave over time — identifying retention trends, product improvements, and degradation signals before it's too late to act.
Types of Cohorts
Acquisition Cohorts
Group users by when they joined (signup week/month). Use for: Is the product getting better over time? Are newer cohorts retaining better?
Behavioral Cohorts
Group users by behavior (e.g., users who used Feature X in first 7 days). Use for: What behaviors predict retention? What's the activation metric?
Segment Cohorts
Group users by company size, plan type, or acquisition channel. Use for: Which segments retain best? Who is the ideal customer?
Retention Metrics
N-Day Retention
"What % of users who joined on Day 0 were active on Day N?"
- Day 1 retention: Did they come back the next day?
- Day 7 retention: Did they return after a week?
- Day 30 retention: Do they still see value after a month?
Rolling Retention
"What % of users who joined in week X were active in week Y or any later week?"
- Measures "did they ever come back after week N?"
- Better for weekly/monthly-use apps
Retention Curve Diagnosis
Healthy: Flattens asymptotically
|████
| █
| ███████████████ ← holds at some % forever
+---------------------- time
Dying: Continues to slope toward zero
|████
| ████
| ████
| ████▼ ← approaching 0
+---------------------- time
If the retention curve approaches zero, there is a product-market fit problem — not a growth problem. More acquisition won't fix it.
Activation Analysis (Finding the "Aha Moment")
Find behaviors that correlate with long-term retention:
- Identify users with high 30-day retention
- What did they do in their first 7 days that low-retaining users did NOT do?
- That behavior = your activation metric candidate
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 · 101 lines · 0 tokens per session scan A a68049f299e7
cohort-analysis is an agent published in the GitHub repository alexmmatos/arthur-mcp (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 905 tokens. 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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