ab-test-analysis 100/100

Is ab-test-analysis safe to install?

SaferSkills independently audited ab-test-analysis (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

templates/skills/imported/pm-skills/pm-data-analytics/skills/ab-test-analysis/SKILL.md · 1 file

templates/skills/imported/pm-skills/pm-data-analytics/skills/ab-test-analysis/SKILL.md3.5 KB · Markdown

name: ab-test-analysis

description: "Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant."

A/B Test Analysis

Evaluate A/B test results with statistical rigor and translate findings into clear product decisions.

Context

You are analyzing A/B test results for $ARGUMENTS.

If the user provides data files (CSV, Excel, or analytics exports), read and analyze them directly. Generate Python scripts for statistical calculations when needed.

Instructions

Understand the experiment:

What was the hypothesis?
What was changed (the variant)?
What is the primary metric? Any guardrail metrics?
How long did the test run?
What is the traffic split?

Validate the test setup:

Sample size: Is the sample large enough for the expected effect size?
Use the formula: n = (Z²α/2 × 2 × p × (1-p)) / MDE²
Flag if the test is underpowered (<80% power)
Duration: Did the test run for at least 1-2 full business cycles?
Randomization: Any evidence of sample ratio mismatch (SRM)?
Novelty/primacy effects: Was there enough time to wash out initial behavior changes?

Calculate statistical significance:

Conversion rate for control and variant
Relative lift: (variant - control) / control × 100
p-value: Using a two-tailed z-test or chi-squared test
Confidence interval: 95% CI for the difference
Statistical significance: Is p < 0.05?
Practical significance: Is the lift meaningful for the business?

If the user provides raw data, generate and run a Python script to calculate these.

Check guardrail metrics:

Did any guardrail metrics (revenue, engagement, page load time) degrade?
A winning primary metric with degraded guardrails may not be a true win

Interpret results:

Outcome	Recommendation
Significant positive lift, no guardrail issues	Ship it — roll out to 100%
Significant positive lift, guardrail concerns	Investigate — understand trade-offs before shipping
Not significant, positive trend	Extend the test — need more data or larger effect
Not significant, flat	Stop the test — no meaningful difference detected
Significant negative lift	Don't ship — revert to control, analyze why

Provide the analysis summary:

   ## A/B Test Results: [Test Name]

   **Hypothesis**: [What we expected]
   **Duration**: [X days] | **Sample**: [N control / M variant]

   | Metric | Control | Variant | Lift | p-value | Significant? |
   |---|---|---|---|---|---|
   | [Primary] | X% | Y% | +Z% | 0.0X | Yes/No |
   | [Guardrail] | ... | ... | ... | ... | ... |

   **Recommendation**: [Ship / Extend / Stop / Investigate]
   **Reasoning**: [Why]
   **Next steps**: [What to do]

Think step by step. Save as markdown. Generate Python scripts for calculations if raw data is provided.

ab-test-analysis— agent skill

Is ab-test-analysis safe to install?

A/B Test Analysis

Context

Instructions

Further Reading

Audit the pieces. Scan the whole. Decide.