How do I install the User Layer skill?

Run npx skillmds@latest add lord1egypt/user-layer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is User Layer safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with User Layer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is User Layer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published User Layer?

Lord1Egypt (@lord1egypt) published this skill. Their other Agent Skills are listed on their SkillMD profile.

User Layer by lord1egypt — Agent Skill

Name: User Layer
Rating: 4.5 (4 reviews)
Author: Lord1Egypt

User Layer

Use this skill when the user wants a UserLayer report for an App Store or Google Play app, including full analysis, polling, and follow-up questions grounded in real reviews. Trigger it for competitor review analysis, user pain point extraction, segment discovery, and opportunity validation tasks.

Lord1Egypt Updated Sep 8, 2026 2 repo stars

File contents

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

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

Call analyze() to start a full async run.
Poll with check_status() until the report is complete.
Read the returned pain_points.
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.

---name: user-layerdescription: Use this skill when the user wants a UserLayer report for an App Store or Google Play app, including full analysis, polling, and follow-up questions grounded in real reviews. Trigger it for competitor review analysis, user pain point extraction, segment discovery, and opportunity validation tasks.---# UserLayerUserLayer turns App Store and Google Play reviews into structured research outputs:- pain points- user segments- market opportunities- follow-up answers grounded in cited reviewsUse 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`## 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 Flow1. 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.

Frequently asked questions