ai-ab-testing

ai-ab-testing A/B testing design assistant

Quick start

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Overview

The A/B testing design assistant is used to answer "Is this change worth testing?", growth experiments, conversion optimization, and data decision-making. It is suitable for marketing, product, operations, and software to invoke after clarifying business goals, content materials, or analysis targets. It will combine pages, processes, or experiment materials, paste page copy, process descriptions, experiment ideas, conversion goals, user feedback, or friction clues. and other inputs, organize key context, and output conversion issue priorities, copy-ready revisions, and experiment or follow-up action checklists, making it easy to continue execution, review, or delivery. Capability positioning supplement: the skills.sh trending signal shows that ab-testing has user demand; the platform presents it as the growth_cro_sales business assistant directory, outputting reviewable diagnostics, recommendations, or production-ready packages.

When to use

Applicable scenarios

• Users ask questions such as "Is this change worth testing?" and need to quickly break down goals, determine priorities, and form executable results
• Marketing, product, operations, and software need to generate conversion issue priorities, copy-ready revisions, and experiment or follow-up action checklists around the A/B testing design assistant
• Users have already prepared conversion goals (explain the registration, activation, upgrade, retention, payment, or experiment metrics to optimize.), target users (explain user stage, audience segment, traffic source, device, or usage scenario.), page or process links (fill in publicly accessible landing pages, registration pages, paywalls, processes, or reference links.), and want to organize them into executable analysis or optimization results
• Users need to turn A/B testing design assistant-related materials into clear conclusions, priorities, and next steps

How to invoke

Directly call the AI Skills API through the exported Python runner:

Command example

Basic invocation

python3 scripts/run.py --params '{}'

Invocation with common parameters

python3 scripts/run.py --params '{"goal":"转化目标"}'

Parameter description

Parameter	Type	Required	Default	Description
goal	string	No	-	Explain the registration, activation, upgrade, retention, payment, or experiment metrics to optimize
audience	string	No	-	Explain user stage, audience segment, traffic source, device, or usage scenario
materialUrl	string	No	-	Fill in publicly accessible landing pages, registration pages, paywalls, processes, or reference links; a complete accessible URL must be provided
materialFile	string	No	-	Upload page screenshots, conversion data, user feedback, experiment plans, or product materials
materialText	string	No	-	Paste page copy, process descriptions, experiment ideas, conversion goals, user feedback, or friction clues
brandRequirements	string	No	-	Supplement sample size, metric definitions, brand restrictions, technical constraints, unchangeable items, or launch review priorities

See the complete machine-readable parameter structure at references/form-schema.json。

Parameter value reference

This skill currently has no categorical parameters that require additional table lookup.

Supported input formats

The current skill directly accepts JSON parameters; if the parameters include a link field, pass a complete, accessible URL.

Example request

The following example parameters can be passed directly to scripts/run.py, and the runner will send them to the AI Skills API.

python3 scripts/run.py --params '{"goal":"转化目标"}'

Equivalent --params JSON：

{
  "goal": "转化目标"
}

Example return result

{
  "success": true,
  "data": {
    "message": "示例结果请以技能真实返回结构为准。"
  },
  "meta": {
    "executionTime": 842,
    "cached": false
  }
}

Deliverables

• Conversion issue priorities, copy-ready revisions, experiments, or follow-up action checklist: organize core results around the user's goals that can be directly read, reviewed, or delivered.
• Interpretation of input materials: combine the conversion goal (explain the registration, activation, upgrade, retention, payment, or experiment metrics to be optimized.), target users (explain the user stage, audience segment, traffic source, device, or usage scenario.), and page or flow links (provide publicly accessible landing pages, registration pages, paywalls, flows, or reference links.) to extract key context and the basis for judgment.
• Next steps: provide priorities, execution suggestions, or a content framework that can be further developed.

Suggestions for using the results

• First determine whether the output answers the user's core questions about the "A/B Testing Design Assistant".
• Then check whether the results cover conversion issue priorities, copy-ready revisions, experiments, or follow-up action checklist, and whether they provide clear next steps.
• If the input materials are insufficient, it is recommended to have the user supplement the goals, audience, constraints, or original materials before running.

Preparation before running

• AISKILLS_BASE_URL: default https://ai-skills.ai
• AISKILLS_API_KEY: required, used for authenticated calls
• AISKILLS_TENANT_ID: default default