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 san-npm/skills-ws --skill retention-analyticsgit clone --depth 1 https://github.com/san-npm/skills-wsWrote 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/san-npm/skills-ws/retention-analytics)<a href="https://agentmods.dev/skills/san-npm/skills-ws/retention-analytics"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/retention-analytics/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/san-npm/skills-ws/retention-analytics"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/retention-analytics.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 | $0.00073 | $0.04628 |
| Opus 5 | $0.00036 | $0.02314 |
| Sonnet 5 | $0.00015 | $0.00926 |
| Haiku 4.5 | $0.00007 | $0.00463 |
Grade A, and why
retention-analytics 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 5d 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention Analytics
Workflow
1. Cohort Retention Analysis
Pick a retention definition first — they answer different questions and are NOT comparable:
| Definition | Counts a user retained in period N if they… | Use for |
|---|---|---|
| Classic / Nth-day (return) | were active in exactly that period | Apps with an expected cadence (daily/weekly); strict, drops fast |
| Rolling / unbounded | were active in period N or any later period | Reduces noise; "still alive by now" — best for irregular usage |
| Bracket / range | were active anytime within a window (e.g. days 7–13) | Smooths out daily volatility; standard for weekly/monthly views |
| Revenue retention (NRR/GRR) | $ from the cohort, not user count | Subscription/account health, board reporting (see §6) |
The query below uses classic (exact-period) retention. To convert it to rolling, change a.active_week = c.cohort + INTERVAL 'N weeks' to a.active_week >= c.cohort + INTERVAL 'N weeks'. For bracket weekly retention the per-week match is already a 1-week bracket; widen it (e.g. BETWEEN) for monthly brackets.
SQL — classic weekly retention cohorts:
WITH cohorts AS (
SELECT user_id, DATE_TRUNC('week', created_at) AS cohort
FROM users WHERE created_at >= CURRENT_DATE - INTERVAL '90 days'
),
activity AS (
SELECT DISTINCT user_id, DATE_TRUNC('week', event_time) AS active_week
FROM events WHERE event = 'session_start'
)
SELECT
c.cohort,
COUNT(DISTINCT c.user_id) AS cohort_size,
ROUND(100.0 * COUNT(DISTINCT CASE WHEN a.active_week = c.cohort + INTERVAL '1 week' THEN c.user_id END) / COUNT(DISTINCT c.user_id), 1) AS w1_pct,
ROUND(100.0 * COUNT(DISTINCT CASE WHEN a.active_week = c.cohort + INTERVAL '2 weeks' THEN c.user_id END) / COUNT(DISTINCT c.user_id), 1) AS w2_pct,
ROUND(100.0 * COUNT(DISTINCT CASE WHEN a.active_week = c.cohort + INTERVAL '4 weeks' THEN c.user_id END) / COUNT(DISTINCT c.user_id), 1) AS w4_pct,
ROUND(100.0 * COUNT(DISTINCT CASE WHEN a.active_week = c.cohort + INTERVAL '8 weeks' THEN c.user_id END) / COUNT(DISTINCT c.user_id), 1) AS w8_pct
FROM cohorts c
LEFT JOIN activity a ON c.user_id = a.user_id
GROUP BY c.cohort ORDER BY c.cohort;
What ships with it
1 file 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.
- 5d ago First seen · 277 lines · 73 tokens per session scan A bd0d9997b7a3
retention-analytics is a skill published in the GitHub repository san-npm/skills-ws (2 stars, last pushed 5d ago), licensed MIT. It adds 73 tokens to every session and 4,628 once invoked, about $0.0004 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 2026-09-07.
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