Product Analytics

What it does

When your agent uses it

• Analyzing experiments
• Measuring feature adoption
• Diagnosing conversion drop-offs
• Evaluating statistical significance of product changes

Example prompts

• “/product-analytics”

Requirements

• Compatibility (from SKILL.md): Claude Code 2.1.277+.
• Pre-approved tools (allowed-tools): Read, Glob, Grep, WebFetch, WebSearch

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Peeking — stopping an experiment early because results look good inflates false-positive rate. Commit to a runtime before launch.
2. 2Multiple comparisons — testing 10 metrics at p < 0.05 means ~1 false positive by chance. Apply Bonferroni correction or pre-register your…
3. 3Sample Ratio Mismatch (SRM) — if variant group sizes differ from expected split by > 1%, your experiment is broken. Fix before analyzing…
4. 4Novelty effect — new features get inflated engagement in week 1. Run experiments long enough to see settled behavior (minimum 2 full…
5. 5Simpson's paradox — aggregate results can reverse when segmented. Always check results by key segments (device, plan tier, geography).

What it can do on your machine

Read from SKILL.md and the folder at commit 02bbf9a. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Glob
  • Grep
  • WebFetch
  • WebSearch

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and sql).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Claude Code 2.1.277+.

  From compatibility in the SKILL.md frontmatter.

Context cost

Product Analytics loads about 1.8k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 626 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~69

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from yonatangross/orchestkit at commit 02bbf9a, republished under its MIT licence (© yonatangross). 626 words, ~1,772 tokens.

name

product-analytics

description

A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.

allowed-tools

Read, Glob, Grep, WebFetch, WebSearch

compatibility

Claude Code 2.1.277+.

license

MIT

user-invocable

false

disable-model-invocation

false

metadata.owner-agent

product-strategist

metadata.category

document-asset-creation

metadata.version

1.0.0

metadata.author

OrchestKit

metadata.complexity

medium

metadata.tags

ab-test, cohort, retention, funnel, conversion, analytics, experiment, statistical-significance

## 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
Duration: [Minimum weeks] — never stop early (peeking bias)

Results:
  Control:   [metric value]  n=[count]
  Treatment: [metric value]  n=[count]
  Lift:      [+/- X%]        p=[value]  95% CI: [lower, upper]

Decision: SHIP / EXTEND / KILL
  Rationale: [One sentence grounded in numbers, not gut feel]

-- Day-N retention cohort query
SELECT
  DATE_TRUNC('week', first_seen)  AS cohort_week,
  COUNT(DISTINCT user_id)         AS cohort_size,
  COUNT(DISTINCT CASE
    WHEN activity_date = first_seen + INTERVAL '7 days'
    THEN user_id END) * 100.0
    / COUNT(DISTINCT user_id)     AS day_7_retention
FROM user_activity
GROUP BY 1
ORDER BY 1;

## Funnel: [Name] — [Date Range]

Stage 1: [Aware / Land]     → [N] users    (entry)
Stage 2: [Activate / Sign]  → [N] users    ([X]% from stage 1)
Stage 3: [Engage / Use]     → [N] users    ([X]% from stage 2)  ← biggest drop
Stage 4: [Convert / Pay]    → [N] users    ([X]% from stage 3)

Overall conversion: [X]%
Biggest drop-off:  Stage 2→3 ([X]% loss) — investigate first

© yonatangross, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in src/skills/product-analytics of yonatangross/orchestkit.

• SKILL.md
• references/stats-cheat-sheet.md
• rules/_sections.md
• rules/_template.md
• rules/ab-test-evaluation.md
• rules/cohort-retention.md
• rules/funnel-analysis.md
• test-cases.json

Open the folder on GitHubat commit 02bbf9a