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 charlieviettq/awesome-agent-skill --skill data-cohort-analysisgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/data-cohort-analysis)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-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/skills/charlieviettq/awesome-agent-skill/data-cohort-analysis"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/data-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 | $0.00092 | $0.01205 |
| Opus 5 | $0.00046 | $0.00602 |
| Sonnet 5 | $0.00018 | $0.00241 |
| Haiku 4.5 | $0.00009 | $0.00120 |
Grade A, and why
"data-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.
This is a copy
86% identical to data-cohort-analysis — 8 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cohort Analysis
Framework
IRON LAW: Aggregate Metrics Hide Cohort Differences
A 70% monthly retention rate OVERALL can mask that January cohort retains
at 85% while June cohort retains at 50%. Aggregate metrics blend improving
and deteriorating cohorts together, hiding both problems and progress.
ALWAYS analyze by cohort before drawing conclusions.
Core Concepts
Cohort: A group of users who share a common characteristic in a specific time period. Most common: acquisition cohort (grouped by signup month).
Retention Matrix: Rows = cohorts (by signup month), Columns = time periods after signup (Month 0, 1, 2...). Cells = % of cohort still active.
Month 0 Month 1 Month 2 Month 3
Jan cohort 100% 65% 48% 40%
Feb cohort 100% 60% 42% 35%
Mar cohort 100% 70% 55% 48% ← Improvement!
Retention Types
| Type | Definition | Use Case |
|---|---|---|
| N-day | % active on exactly day N | Games, daily-use apps |
| N-day bounded | % active within first N days | General product usage |
| Week/Month | % active in week/month N | SaaS, subscriptions |
| Unbounded | % who ever return after day N | Low-frequency products |
Analysis Steps
Phase 1: Define Cohort and Activity
- Cohort definition: signup date, first purchase date, or other milestone
- Activity definition: login, purchase, specific action — must match the product's core value
- Time granularity: daily (for daily-use products), weekly, or monthly
Phase 2: Build Retention Matrix
- Group users into cohorts
- For each cohort, calculate retention at each time period
- Visualize as a heatmap (darker = higher retention)
Phase 3: Identify Patterns
- Retention curve shape: Does it flatten (good — stable core users) or keep declining (bad — everyone eventually churns)?
- Cohort comparison: Are newer cohorts retaining better or worse than older ones?
- Drop-off cliff: Is there a specific period where retention drops sharply? (e.g., Day 1 → Day 7 drops 50%)
What ships with it
3 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.
- 9d ago First seen · 110 lines · 92 tokens per session scan A 69c2cfd6da9e
"data-cohort-analysis" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,205 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to data-cohort-analysis, differing in 8 lines, and is treated as a copy.
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