Competitor Analysis logo

Competitor Analysis

OrganizationPopular
every-app
competitor-analysis

Analyze one competitor's organic footprint, ranking keywords, content themes, backlinks, and gaps.

Overview

Publisherevery-app
Repositoryopen-seo
Skill namecompetitor-analysis
Stars
22K
Forks
2.8K
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 every-app on GitHub. Read the source before you install it.

Installation

Install the Competitor Analysis 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/every-app/open-seo/tree/main/.agents/skills/competitor-analysis
  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/every-app/open-seo.git /tmp/open-seo
mkdir -p .claude/skills
cp -r /tmp/open-seo/.agents/skills/competitor-analysis .claude/skills/competitor-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Analysis 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 Analysis 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 Analysis 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.

OpenSEO Competitor Analysis

Goal

Analyze one competitor deeply enough to decide what to learn from, avoid, counter-position against, or outrank.

Use this for a named competitor. For identifying the market leaders first, use competitive-landscape.

Required inputs

  • projectId
  • Competitor domain
  • User's domain when comparison is requested
  • Optional topic/category/location/language

Project context

The project-context tools are free and shared with the app and other agents.

  1. Call get_project_context first and ground the analysis in it — the saved competitors say whether this domain is already known and what was concluded about it before.
  2. This skill needs competitors. If none are saved, run a minimal inline setup: save the competitor being analyzed, and ask the user (or infer from find_serp_competitors and confirm) whether there are others, write them back with update_project_context (addCompetitors), then continue the analysis. Never front-load the full interview; suggest seo-project-setup at the end for the rest.
  3. Before spending credits, check the research log. If the same research ran within the last 30 days, reuse that result and say so instead of re-buying it.
  4. On finish, write back what is durable with update_project_context — an addCompetitors upsert for this domain with a short note on its strengths and where it is vulnerable — and append a research log entry: { appendResearchLog: { summary: "Competitor analysis: <domain>. Verdict: <conclusion>" } }.

Deliver as a report

Deliver through the seo-report skill, saving with skill: "competitor-analysis". If that skill is not available, say so and stop before writing HTML.

OpenSEO MCP tools

  • get_domain_overview: baseline organic traffic and keyword count.
  • get_search_console_performance: when comparing to the user's own domain and Search Console is connected, use it as the first-party baseline (real clicks/impressions/CTR/position) instead of estimating the user's own performance from third-party data.
  • get_ranked_keywords: exact keyword, URL, rank, intent, traffic, CPC, and SERP-type rows for the competitor domain or page.
  • get_backlinks_overview: backlink/referring-domain profile.
  • find_serp_competitors: validate whether the named competitor is a real search competitor across the target keyword set.
  • search_local_businesses, get_local_serp_results, and get_google_business_questions: use for local SEO competitors when Maps/local-pack visibility, nearby businesses, categories, or Google Q&A matter.
  • get_serp_results: validate direct head-to-head SERPs for important keywords.
  • research_keywords: expand gaps or category terms when needed.

Workflow

  1. Call get_domain_overview for the competitor, passing provided location/language when supported.
  2. If comparing to the user, call get_domain_overview for the user's domain too — and if Search Console is connected, get_search_console_performance for the user's real baseline.
  3. Call get_ranked_keywords for the competitor. Use filters like maxRank, minSearchVolume, excludeBrandTerms, and resultTypes to keep rows relevant.
  4. If comparing to the user, call get_ranked_keywords for the user's domain/page too, or use get_serp_results for the shared terms when a lighter check is enough.
  5. For local SEO, use search_local_businesses and get_local_serp_results around the relevant business location(s) before drawing local-pack conclusions. Add get_google_business_questions only when Q&A evidence matters.
  6. Use find_serp_competitors when the competitor was supplied by the user but its search overlap is unclear.
  7. Group competitor keywords into themes:
    • Product/category terms
    • Alternatives/comparisons
    • Templates/tools/calculators
    • Educational guides
    • Branded demand
    • Local/neighborhood terms when relevant
  8. Call get_backlinks_overview for the competitor, especially if authority appears to explain rankings. Continue without backlink evidence if it is unavailable.
  9. Use get_serp_results for important shared or target keywords to compare positioning, passing provided location/language when supported.
  10. Produce an actionable plan:
    • What they are doing well
    • Where they are vulnerable
    • Which pages/keywords to pursue
    • What to avoid copying

Output format

h1: the competitor domain.

If a report template applies (see seo-report), its sections and tone replace this list.

Sections in this order:

  1. Snapshot — one or two opening sentences, then a table of the competitor's organic footprint (traffic estimate, keyword count, referring domains). Add the user's domain as a second row when comparing.
  2. The biggest lesson — one finding.
  3. Where they are vulnerable — one finding per opening, ordered by how winnable it is.
  4. Keyword themes — a table of theme, example keywords, volume, and whether the user competes there. A bar chart when a few themes dominate the footprint.
  5. Content patterns and authority — prose, with a note for anything inferred from keyword rows rather than seen on a page.
  6. What to do next — an ordered list, shortest useful.
  7. How this report was made — opens with the skill link line from seo-report, pointing at https://openseo.so/docs/skills/competitor-analysis ("OpenSEO Competitor Analysis skill"), then which tools reported what, and what you checked yourself.

Guardrails

  • Do not treat all competitor keywords as desirable. Filter for business fit.
  • Separate evidence from inference.
  • Do not infer competitor page/content-type patterns from keyword rows alone; use SERP or web evidence for page-level claims.
  • For local SEO, do not infer Maps/local-pack strength from national organic domain metrics alone; use local business and local SERP tools when the location is known or reasonably discoverable.
  • Do not recommend copying content; recommend a stronger angle or better answer to the same intent.
  • If the user's domain is unavailable, frame the analysis as competitor-only.

Frequently asked questions

What does the Competitor Analysis AI skill do?

Analyze one competitor's organic footprint, ranking keywords, content themes, backlinks, and gaps.

Why use Competitor Analysis on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/every-app/open-seo/tree/main/.agents/skills/competitor-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Competitor Analysis?

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 Analysis?

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

Is the Competitor Analysis AI skill free?

Yes. It is published on GitHub by every-app 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇