UserLayer

UserLayer turns App Store and Google Play reviews into structured research outputs:

• pain points
• user segments
• market opportunities
• follow-up answers grounded in cited reviews

Use the bundled wrappers in scripts/main.py. Do not hand-roll raw HTTP requests when this skill is available.

Use This Skill When

• The user gives you an App Store or Google Play URL and wants review-backed insights.
• The user wants to ask follow-up questions about a completed full analysis.

Prerequisites

• API_KEY must be set to a valid UserLayer API key.
• LAUNCHBASE_API_URL is optional. Default: https://lb-api.workflowhunt.com

Install and Prompt Your AI

Install the skill:

npx clawhub@latest install userlayer

Then send your AI a prompt like this:

Please learn this skill first: npx clawhub@latest install userlayer

If it is easier for you to inspect the repo first, use: https://github.com/houyongsheng/userlayer

After installing it, use the UserLayer skill to analyze this app and return pain points, user segments, and opportunities grounded in real reviews:
<replace this with an App Store or Google Play URL>

Credentials and Host

• Primary credential: API_KEY
• Default API host: https://lb-api.workflowhunt.com
• API_KEY should be scoped only for UserLayer API access and treated as a paid production credential.

Available Wrappers

• analyze(app_url: str, max_reviews: int | None = None)
• check_status(analysis_id: str)
• query(pain_point_id: str, question: str)

Typical Flow

1. Call analyze() to start a full async run.
2. Poll with check_status() until the report is complete.
3. Read the returned pain_points.
4. Use a pain_point.id with query() for targeted follow-up questions.

You do not need a separate preview step. analyze() is the main public entry point.

Pricing Baseline

• One analyze() run costs $2.99 and includes retrieval plus full analysis of the latest 100 reviews.
• If max_reviews is raised above 100, extra reviews are billed as add-ons at $0.01 per extra review.
• query() is billed at $0.01 / 1K tokens.

Key Response Shapes

• analyze() starts an async job and returns a lightweight payload with:
  • success
  • data.analysis_id
  • data.status
• check_status() returns the full completed report when ready, including:
  • data.pain_points
  • data.user_segments
  • data.opportunities
  • sources
  • usage
• query() returns a follow-up answer for a specific pain point, including:
  • data.answer
  • data.confidence
  • sources
  • usage

Treat pain_points[].id from a completed analysis as the required input for query().

Operating Rules

• Default review count is 100 latest reviews.
• analyze() includes retrieval and analysis of 100 latest reviews by default.
• If max_reviews is raised above 100, extra reviews are billed as add-ons.
• analyze() is asynchronous and returns an analysis_id; poll with check_status().
• Use query() only after a completed full analysis returns a valid pain_point_id.
• Treat sources and cited review evidence as the source of truth.
• If DATA_INDEX_NOT_FOUND is returned, rerun a full analysis before querying again.