ai-onboarding

ai-onboarding Onboarding Diagnostic Assistant

Overview

The onboarding diagnostic assistant is used to answer "Will new users get stuck?", growth experiments, conversion optimization, and data decisions. It is suitable for marketing, product, operations, and software to call after clarifying business goals, content materials, or analysis targets. It combines inputs such as pages, processes or experiment materials, pasted page copy, process descriptions, experiment ideas, conversion goals, user feedback, or friction clues. organizes key context, and outputs conversion issue priorities, copy-ready revisions, experiments, or follow-up action checklists for continued execution, review, or delivery. Capability positioning supplement: skills.sh trending signals show onboarding has user demand; the platform displays it as the growth_cro_sales business assistant directory, outputting reviewable diagnostics, recommendations, or production preparation packages.

When to Use

Applicable Scenarios

Users raise questions such as "Will new users get stuck?" and need to quickly break down goals, determine priorities, and form actionable results
Marketing, product, operations, and software need to generate conversion issue priorities, copy-ready revisions, experiments, or follow-up action checklists around the onboarding diagnostic assistant
Users have already prepared conversion goals (explain the registration, activation, upgrade, retention, payment, or experiment metrics to optimize.), target users (explain user stage, segmentation, traffic source, device, or usage scenario.), page or process links (fill in publicly accessible landing pages, registration pages, paywalls, processes, or reference links.), and hope to organize them into actionable analysis or optimization results
Users need to turn onboarding diagnostic assistant-related materials into clear conclusions, priorities, and next steps

How to Call

Call the AI Skills API directly through the exported Python runner:

Command Examples

Basic Call

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

Call 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, segmentation, 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, items that cannot be changed, or launch review priorities

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

Parameter Value Reference

The current skill has no categorical parameters that require additional lookup.

Supported Input Formats

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

Example request

The example parameters below 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 response

{
  "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 list: organize the core results around the user's goals that can be directly read, reviewed, or delivered.
Interpretation of input materials: combine the conversion goal (describe the registration, activation, upgrade, retention, payment, or experiment metrics to be optimized.), target users (describe the user stage, audience segment, traffic source, device, or usage scenario.), page or process link (provide a publicly accessible landing page, registration page, paywall, process, or reference link.) to extract key context and the basis for judgment.
Next steps: provide priorities, execution suggestions, or a content framework that can be further processed.

Suggestions for using the results

First determine whether the output answers the user's core question about the "Onboarding Diagnostic Assistant".
Then check whether the results cover conversion issue priorities, copy-ready revisions, experiments, or follow-up action list, and whether they provide clear next steps.
If the input materials are limited, 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