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Competitor Discovery

Community
superamped
competitor-discovery

Use this skill to find, verify, classify, and rank direct, adjacent, and tangential competitors from public web evidence. Trigger it when entering a market, building a competitor set, validating perceived alternatives, or refreshing research after a pivot.

Overview

Publishersuperamped
Repositoryai-marketing-skills
Skill namecompetitor-discovery
Stars
73
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • 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 superamped on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/superamped/ai-marketing-skills/tree/main/skills/research/competitor-discovery
  4. 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.
2

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/superamped/ai-marketing-skills.git /tmp/ai-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/ai-marketing-skills/skills/research/competitor-discovery .claude/skills/competitor-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Discovery 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 Discovery 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 Discovery 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 Discovery

Usage

Use when you don't know who your competitors are, when entering a new market, or when refreshing the competitor list after a pivot or market shift.

Process

Step 1: Gather Inputs

Ask the user for:

  1. Product description — what it does, what category it's in, key features
  2. Target audience — who buys or uses the product (role, industry, company size)
  3. Known competitors (optional) — names or URLs to include without searching
  4. Number to find (optional) — default: 5-7

Step 2: Build Search Queries

Construct 4-6 search queries mixing these angles:

  • Category: "[category] tools 2025 2026" or "[category] software"
  • Alternative: "[product name] alternatives" or "tools like [product name]"
  • Problem: "[primary use case] solution" or "[pain point] tool"
  • Audience: "best [category] for [target company type]" or "[category] for [target role]"
  • Comparison: "best [category] compared" or "top [category] platforms"
  • Review sites: "[category] G2" or "[category] Capterra" (to mine company names from lists)

Tailor queries to the product type. A B2B SaaS product needs different queries than a marketplace or agency service.

Step 3: Search and Collect

Run web searches for each query. For each result:

  • Extract company names and domains that appear across multiple results
  • Pull companies listed on review/comparison sites (G2, Capterra, etc.) — these are strong signals
  • Skip aggregator sites themselves, job boards, news articles about funding, and clearly unrelated results

Compile a candidate list of 8-12 companies. For each, note:

  • Name
  • URL (homepage)
  • How found — which search queries surfaced them

Rank candidates by frequency — companies appearing in 3+ different searches are almost certainly direct competitors.

Step 4: Quick Profile Each Candidate

For each candidate, fetch their homepage (and pricing page if easily accessible) and extract:

  • One-liner — what they say they do, in their own words
  • Target audience signals — who the site speaks to
  • Overlap — how directly they compete (direct / adjacent / tangential)

Classify each candidate:

  • Direct competitor — same problem, same audience, similar solution
  • Adjacent competitor — same audience but different approach, or same approach but different audience
  • Tangential — some overlap but fundamentally different product

Drop tangential candidates unless the list would be too short (fewer than 3 direct competitors).

Step 5: Present for Confirmation

Present the ranked list to the user:

I found these competitors for [product name]:

Direct competitors:
1. [Name] — [one-liner] — [url]
2. [Name] — [one-liner] — [url]
3. [Name] — [one-liner] — [url]

Adjacent competitors:
4. [Name] — [one-liner] — [url]
5. [Name] — [one-liner] — [url]

Should I save these? Any to add or remove?

Wait for user confirmation. They may know competitors that web search missed, or flag false positives.

Output Format

# Competitor Discovery: [Product Name]

**Date:** [current date]
**Competitors found:** [X]

## Competitors

| # | Name | URL | Type | One-Liner |
|---|------|-----|------|-----------|
| 1 | [name] | [url] | Direct | [what they do] |
| 2 | [name] | [url] | Direct | [what they do] |
| 3 | [name] | [url] | Adjacent | [what they do] |

## Search Queries Used
- [query 1] — [X results]
- [query 2] — [X results]

## Recommended Next Steps
- Run competitor-site-analysis on top competitors for full profiles (pricing, moats, GTM signals)
- Run competitor-landscape after 2+ analyses for cross-competitor comparison and positioning map

Rules

  • Never include a company without visiting their site — one-liners must come from their actual homepage.
  • Never guess at pricing — only note what's publicly visible on the homepage or pricing page.
  • Never include more than 7 competitors without user approval — keep the list focused.
  • If web search returns fewer than 3 relevant competitors, the category may be too niche — try broader search terms.
  • If the market appears very crowded (15+ candidates found), suggest focusing on direct competitors only.
  • Re-run every 6 months or after significant product/market changes.

Frequently asked questions

What does the Competitor Discovery AI skill do?

Use this skill to find, verify, classify, and rank direct, adjacent, and tangential competitors from public web evidence. Trigger it when entering a market, building a competitor set, validating perceived alternatives, or refreshing research after a pivot.

Why use Competitor Discovery on TypingMind?

Because you install it once and use it with any model. Competitor Discovery 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 Discovery in TypingMind?

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

Which AI models can use Competitor Discovery?

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 Discovery?

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

Is the Competitor Discovery AI skill free?

Yes. It is published on GitHub by superamped 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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