Competitor Scan

Organization

WellApp-ai

competitor-scan

Research best-in-class products using Browser MCP and WebSearch

Overview

Publisher	WellApp-ai
Repository	`Well`
Skill name	`competitor-scan`
Stars	342
Forks	48
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by WellApp-ai on GitHub. Read the source before you install it.

Installation

Install the Competitor Scan AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/WellApp-ai/Well.git /tmp/Well
mkdir -p .claude/skills
cp -r /tmp/Well/cursor-rules/skills/competitor-scan .claude/skills/competitor-scan

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Scan in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitor Scan on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitor Scan is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Competitor Scan Skill

Research how best-in-class products solve similar problems using Browser MCP for screenshots and WebSearch for teardowns.

When to Use

At the start of DIVERGE Loop (L1)
When exploring new UI patterns
When benchmarking against industry standards

Instructions

Phase 1: Identify Competitors

Use the domain competitor table:

Domain	Products to Study
Workspaces/Collaboration	Notion, Linear, Slack, Figma, Attio
Data Tables	Airtable, Retool, Rows, Grist
AI Chat	ChatGPT, Claude, Gemini, Perplexity
Onboarding/Flows	Stripe, Plaid, Mercury, Ramp
Settings/Admin	Vercel, Railway, PlanetScale
Invitations/Team	Slack, Notion, Linear, Figma
Billing/Subscriptions	Stripe, Paddle, Chargebee

Phase 2: Screenshot Key Flows (Browser MCP)

For each relevant competitor:


1. browser_navigate to the product URL or relevant page
2. browser_snapshot to understand the page structure
3. browser_take_screenshot to capture the UI
4. browser_click / browser_type to navigate through flows

Capture:

Entry points (how users start the flow)
Key screens (main interactions)
Edge cases (empty states, errors)
Micro-interactions (hover states, transitions)

Phase 3: Research Teardowns (WebSearch)

Search for existing analysis:


WebSearch "[Product] UI teardown [feature]"
WebSearch "[Product] UX case study [feature]"
WebSearch "[Feature] best practices design patterns"

Phase 4: Extract Patterns

For each competitor, note:

Aspect	Pattern
Layout	How is content organized?
Navigation	How do users move between states?
Actions	How are primary/secondary actions presented?
Feedback	How is success/error communicated?
Copy	What language/tone is used?

Output Format

After running this skill, output:

markdown
## Competitor Scan

### Products Analyzed
1. [Product A] - [URL or feature]
2. [Product B] - [URL or feature]
3. [Product C] - [URL or feature]

### Key Patterns Observed

| Pattern | Product | Description |
|---------|---------|-------------|
| [Pattern] | [Product] | [How they do it] |

### Insights for Our Design
- [Insight 1]: [How to apply]
- [Insight 2]: [How to apply]

### Screenshots Captured
- [Description of screenshot 1]
- [Description of screenshot 2]

Invocation

Invoke manually with "use competitor-scan skill" or follow Ask mode DIVERGE loop which references this skill's phases.

Related Skills

problem-framing - Define what problem to research
design-context - Compare external patterns with internal

Frequently asked questions

What does the Competitor Scan AI skill do?

Research best-in-class products using Browser MCP and WebSearch

Why use Competitor Scan on TypingMind?

Because you install it once and use it with any model. Competitor Scan is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitor Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WellApp-ai/Well/tree/main/cursor-rules/skills/competitor-scan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Competitor Scan?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitor Scan?

As many as you like. As long as a model supports skills, you can use Competitor Scan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitor Scan AI skill free?

Yes. It is published on GitHub by WellApp-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Competitor Scan Skill

When to Use

Instructions

Phase 1: Identify Competitors

Phase 2: Screenshot Key Flows (Browser MCP)

Phase 3: Research Teardowns (WebSearch)

Phase 4: Extract Patterns

Output Format

Invocation

Related Skills

Frequently asked questions

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