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 skills/brainbytes-dev/everything-claude-marketing/cohort-analysisnpx skills add brainbytes-dev/everything-claude-marketing --skill cohort-analysisgit clone --depth 1 https://github.com/brainbytes-dev/everything-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/skills/brainbytes-dev/everything-claude-marketing/cohort-analysis)<a href="https://agentmods.dev/skills/brainbytes-dev/everything-claude-marketing/cohort-analysis"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/cohort-analysis.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 | $0.00033 | $0.03009 |
| Opus 5 | $0.00016 | $0.01504 |
| Sonnet 5 | $0.00007 | $0.00602 |
| Haiku 4.5 | $0.00003 | $0.00301 |
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 4d 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cohort Analysis for Marketing
When to Activate
- Analyzing customer retention rates and identifying drop-off points
- Estimating customer lifetime value (LTV) for budget planning
- Comparing the quality of customers acquired from different channels or campaigns
- Understanding how product changes affect user behavior over time
- Evaluating the impact of onboarding improvements
- Segmenting users by behavior to personalize marketing
- Presenting retention or LTV data to executives or investors
First Questions
- What type of cohort analysis do you need? (Acquisition, behavioral, segment-based)
- What is the key event you're tracking? (Purchase, login, feature usage, subscription renewal)
- What time granularity makes sense? (Daily, weekly, monthly cohorts)
- How far back does your data go? (Need at least 3-6 months for meaningful patterns)
- What tool or data source will you use? (SQL database, analytics platform, spreadsheet)
- What action do you want to take based on the results? (Improve retention, estimate LTV, evaluate channels)
Cohort Types
Acquisition Cohorts
Group users by when they signed up or made their first purchase.
- Use case: "How do January sign-ups retain compared to February sign-ups?"
- Best for: Tracking retention over time, measuring impact of product/onboarding changes, LTV estimation.
- Time basis: Sign-up date, first purchase date, install date.
Behavioral Cohorts
Group users by actions they took (regardless of when they signed up).
- Use case: "Do users who complete onboarding retain better than those who don't?"
- Best for: Identifying activation milestones, proving the value of specific features, informing product development.
- Action basis: Completed onboarding, used feature X, invited a teammate, made a second purchase.
Segment Cohorts
Group users by shared characteristics.
- Use case: "Do enterprise customers retain differently from SMB customers?"
- Best for: Channel comparison, persona validation, pricing tier analysis.
- Segment basis: Acquisition channel, plan type, company size, geography, persona.
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.
- 4d ago First seen · 273 lines · 33 tokens per session scan A 3e1c27d913c7
cohort-analysis is a skill published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 3,009 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 2026-08-31.
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