product-analytics SKILL.md skillA

product-analytics from yonatangross/orchestkit is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add yonatangross/orchestkit/product-analytics@v1.0.0

Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
yonatangross/orchestkit
Path
plugins/ork/skills/product-analytics/SKILL.md
Versions recorded
10
Latest version
v1.0.0, committed Nov 4, 2026
First version recorded
Jun 20, 2026
Size
7,029 bytes
Repository stars
299
License
MIT

A/B test evaluation, cohort retention, funnels and significance. Use when analyzing experiments, feature adoption or conversion drop-offs.

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Product Analytics

Frameworks for turning raw product data into ship/extend/kill decisions. Covers A/B testing, cohort retention, funnel analysis, and the statistical foundations needed to make those decisions with confidence.

## Quick Reference

| Category | Rules | Impact | When to Use |
|----------|-------|--------|-------------|
| [A/B Test Evaluation](#ab-test-evaluation) | 1 | HIGH | Comparing variants, measuring significance, shipping decisions |
| [Cohort Retention](#cohort-retention) | 1 | HIGH | Feature adoption curves, day-N retention, engagement scoring |
| [Funnel Analysis](#funnel-analysis) | 1 | HIGH | Drop-off diagnosis, conversion optimization, stage mapping |
| [Statistical Foundations](#statistical-foundations) | 1 | HIGH | p-value interpretation, sample sizing, confidence intervals |

**Total: 4 rules across 4 categories**

## A/B Test Evaluation

Load `rules/ab-test-evaluation.md` for the full framework. Quick pattern:

```markdown
## Experiment: [Name]

Hypothesis: If we [change], then [primary metric] will [direction] by [amount]
  because [evidence or reasoning].

Sample size: [N per variant] — calculated for MDE=[X%], power=80%, alpha=0.05
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add yonatangross/orchestkit/product-analytics@sha256:894e6216a08f5bfb

The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_o7fap6tzsod4bgzv.svg)](https://markdownregistry.com/a/art_o7fap6tzsod4bgzv)

5 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 10 versions of this file, between Jun 20, 2026 and Nov 4, 2026, about one every 15 days.

versioncommittedcommitsizeaudit
v1.0.0 latest2026-11-04 3036b27 7,029 BA view · diff
v1.0.02026-10-07 480e358 7,147 BA view · diff
v1.0.02026-09-29 105a980 7,151 BA view · diff
v1.0.02026-09-28 220adb4 7,169 BA view · diff
v1.0.02026-09-20 02cb935 7,181 BA view · diff
v1.0.02026-08-29 2aed0b0 7,181 BA view · diff
v1.0.02026-08-01 0ed071d 7,181 BA view · diff
v1.0.02026-07-25 47b78fb 7,178 BA view · diff
v1.0.02026-07-10 10a2d30 7,178 BA view · diff
v1.0.02026-06-20 5700399 7,178 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (7029 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

yonatangross/orchestkit · 299 stars · license MIT · pushed 2026-11-07 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_o7fap6tzsod4bgzv
GET https://markdownregistry.com/api/v1/resolve?ref=yonatangross/orchestkit/product-analytics
GET https://markdownregistry.com/api/v1/blob/894e6216a08f5bfba42f24a012de22bedf2364f1b2a1dac4326ad6db94e3117c

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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Every file in yonatangross/orchestkit

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