Can it tell me the winner from a few conversions?

It calculates supplied metrics and checks evidence limits. It does not declare significance or a causal winner when sample and design evidence are inadequate.

Should each test change only one thing?

One primary variable is preferred when feasible. Exploratory portfolios and multivariate designs need clear naming, appropriate design and sufficient evidence rather than being mislabeled A/B tests.

Does it run tests automatically?

It prepares specifications and learning records. Actual campaign actions require compatible authorized tools and confirmed execution.

Creative Testing Lab — Test Ideas Systematically

Example session with this skill installed

Illustrative scenario: We tested two ad opening lines, keeping audience, offer, landing page and format constant according to our supplied change log. A had 1,000 impressions, 50 clicks and five purchases. B had 800 impressions, 48 clicks and four purchases. Randomization and exposure overlap are unknown; refunds and revenue are unavailable. Summarize rates without declaring a winner, identify confounders and propose the next test and learning-ledger entry. Do not browse or change campaigns.

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Compiled the creative testing lab

Observed comparison

Variant	Impressions	Clicks	Purchases	Clicks / impressions	Purchases / clicks	Purchases / impressions
A	1,000	50	5	5%	10%	0.50%
B	800	48	4	6%	8.33%	0.50%

B’s observed click rate is one percentage point higher, or 20% relatively higher. Both variants have the same observed purchases-per-impression rate. These are descriptive calculations from supplied counts, not a statistically established lift or causal finding. Revenue, margin, refunds and attribution quality are unknown.

Interpretation boundaries

Only nine purchases occurred. Randomization, delivery timing, audience overlap, repeat exposures and comparable placement are unverified. The supplied change log says the opening line changed and other listed elements stayed constant, but it does not establish equivalent delivery or unique-user denominators. Do not promote B as a winner from click rate alone.

Next test specification

Question: does opening line B improve qualified conversion relative to A? Hypothesis: B may earn more initial attention, but the mechanism needs validation. Primary variable: opening line. Keep the offer, CTA, format, landing experience and approved delivery context constant where feasible. Use a verified randomized split if the platform supports it; otherwise name the comparison method and its limits.

Primary metric: attributed purchases per eligible assigned user or a clearly agreed exposure unit. Supporting metric: click rate. Guardrails: refunds, acquisition economics, customer fit and complaints. Obtain baseline, acceptable effect size and design assumptions before calculating evidence requirements; no sample threshold is invented. Predefine observation and decision rules to avoid stopping opportunistically.

Learning-ledger entry

Test: opening-line comparison. Evidence: supplied aggregate counts and change log. Observation: higher B click rate; equal purchases/impression. Status: inconclusive. Confounders: assignment, overlap and delivery unknown. Next action: validate instrumentation and run an approved controlled design.

No test was launched or stopped, budget changed, asset uploaded or winner promoted.

Connects securely to your tools. The creator never sees your data.

About this skill

Creative Testing Lab helps marketing and creative teams turn ideas into disciplined experiments. It defines the creative question, hypothesis, control, treatment, constants, delivery context, metrics and guardrails, then records results and decision-relevant learning.

It supports angles, hooks, first frames, messages, proof, formats, CTAs, visual systems and landing-message match. Supplied creative libraries, test plans, analytics, change logs and prior learning can support taxonomy, prioritization, confound checks and iterative test portfolios. Optional image and calculation tools support variant comparison and evidence diagnostics.

Without authorized live tools it provides test specifications and manual measurement procedures. It does not invent baselines, sample sizes, campaign settings, attribution or causal results; every comparison is not automatically an A/B test. Launches, stops, budget changes and winner promotion require authorization. No winner, lift, significance or revenue outcome is guaranteed.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Creative Testing Lab — Test Ideas Systematically