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ab-test-analysis

Analyze supplied controlled-experiment observations with setup-integrity checks, effect estimates, uncertainty, guardrails, practical significance, and a…

Skill by ThorStarlord

IndexedMIT
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Analyze supplied controlled-experiment observations with setup-integrity checks, effect estimates, uncertainty, guardrails, practical significance, and a bounded recommendation. Use for experimentation responsibility and produce test_results. Requires real observation/data evidence for a measured conclusion; without it return insufficient_evidence. Do not invent sample sizes, conversions, p-values, confidence intervals, power, guardrail values, or declare a winner from a plan alone.

Use cases: testing, data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/ab-test-analysis

What's inside

Skill name
ab-test-analysis
Description
Analyze supplied controlled-experiment observations with setup-integrity checks, effect estimates, uncertainty, guardrails, practical significance, and a bounded recommendation. Use for experimentation responsibility and produce test_results. Requires real observation/data evidence for a measured conclusion; without it return insufficient_evidence. Do not invent sample sizes, conversions, p-values, confidence intervals, power, guardrail values, or declare a winner from a plan alone.
License
MIT
Instructions
33 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • contract.md · references/

SKILL.md (first lines)

# A/B test analysis

Satisfy an `experimentation` responsibility by producing `test_results`.

## Inputs

Require an experiment definition/hypothesis plus actual control/treatment observations or a durable statistical result source. Prefer the corresponding `experiment_plan` and raw/aggregated observation references.

## Procedure

1. Read [references/contract.md](references/contract.md).
2. Reconstruct the preregistered primary metric, guardrails, split, duration, MDE/sample plan, and decision criteria when available.
3. Check setup integrity: sample counts, exposure, SRM/randomization evidence, duration/cycle coverage, peeking/multiple-comparison concerns, and data completeness.
4. Analyze the supplied metric using an appropriate declared method. Preserve raw inputs and calculation provenance.
5. Separate statistical uncertainty from practical/product significance.
6. Evaluate guardrails and important segment heterogeneity without fishing for an unplanned winner.
7. Return `ship`, `investigate`, `extend`, `stop`, `do_not_ship`, or `insufficient_evidence` as an analysis recommendation only.
8. If the recommendation is `investigate` or `extend`, name the unresolved evidence need. Do not treat that recommendation as automatic warrant for another experiment; a continuation experiment must pass Experiment Economy again.
9. Return control; no rollout/external action is authorized.

## Boundary

```text
statistical significance != product value
recommendation != rollout authority
extend recommendation != experiment warrant
investigate recommendation != experiment warrant
experiment plan != observed result
```

Returned evidence may justify reassessment; it does not self-authorize experiment continuation.

From ThorStarlord/sensemaking-skills, MIT.

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Last commit (this folder)
Sep 21, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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ab-test-analysis skill. Agent Mart. https://agentmart.dev/skills/thorstarlord-ab-test-analysis (last updated 2026-10-24)