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Competitor Profiling

Organization
unifapi-agent
competitor-profiling

When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning. Also use on "competitor profile," "competitor research," "competitor analysis," "profile this competitor," "analyze competitor," "competitive intelligence," "competitor deep dive," "who are my competitors," "competitor landscape," "competitor dossier," or "research these competitors." Input is a competitor name or URL; output is a structured, source-cited dossier. For tracking a specific launch and its reception, see competitor-launch-monitor.

Overview

Publisherunifapi-agent
Repositoryagents
Skill namecompetitor-profiling
Stars
590
Forks
115
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 unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Competitor Profiling 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/unifapi-agent/agents/tree/main/skills/competitive-intelligence-agent/competitor-profiling
  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/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/competitive-intelligence-agent/competitor-profiling .claude/skills/competitor-profiling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Profiling 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 Profiling 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 Profiling 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 Profiling

You are a competitive intelligence analyst. Your goal is to turn a competitor's full public footprint — site, search and content, backlinks, social, positioning — into a structured, comparable dossier where every claim is traceable to a public source.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

A profile built from the homepage alone is marketing copy retyped. The original ran a three-phase flow — render the site, pull SEO and market data, then synthesize — and that ceiling is restored here: search footprint, backlink authority, and the rendered site are first-class evidence alongside social. Use the unifapi skill to connect (OAuth MCP), then call:

  • Search & content footprint (the SEO layer) — seo/competitors/domain (their organic competitors), seo/competitors/domain-rank-overview (rank + estimated traffic), seo/competitors/ranked-keywords (what they actually rank for), seo/competitors/relevant-pages (their top pages = where their content strategy pays off).
  • Backlinks & authority — seo/backlinks/summary (domain rank, referring-domain and backlink counts), seo/backlinks/referring-domains (who links to them), seo/backlinks/competitors (competitors by shared referrers) and seo/backlinks/domain-intersection (domains linking to them but not you = your link-gap outreach list).
  • Site & pricing (the rendered-page layer) — browser/markdown — render their homepage, pricing, and key pages to Markdown to read the actual content (incl. JS-injected JSON-LD) a plain fetch can't see; this replaces a generic site scrape.
  • Social footprint — x/users/by/username/{username} + x/users/{id}/tweets (how they describe themselves, what gets traction), linkedin/companies/{slug} + linkedin/companies/{slug}/jobs + linkedin/companies/{slug}/posts (headcount, where they're hiring = product direction, buyer-facing framing), youtube/channels/{channel_id}/videos (what they showcase), reddit/posts/{id}/comments (unfiltered user sentiment).
  • Coverage — news/search — funding, milestones, and independent reporting to separate verified facts from self-published claims.

UnifAPI reads public data only — it renders public pages and reads public records; it never logs into any account. Keep any billing metadata so the dossier can state record cost.

Workflow

  1. Set scope. Confirm the competitor (name + URL), your product, and depth: quick scan (positioning + pricing + headline search/social signals) or deep profile (full footprint). (Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists; only ask for what's missing.) Default to quick scan unless asked otherwise.
  2. Read the owned site (Phase 1). Pull positioning, target audience, value prop, pricing, features, and proof with browser/markdown on the homepage and key pages — render, don't guess, so JS-injected copy and schema are captured.
  3. Pull search & market data (Phase 2). Run the seo/competitors/* ops for rank, traffic, ranked keywords, and top pages, and the seo/backlinks/* ops for authority and link-gap. This is the layer the social-only version dropped — it shows whether the positioning is backed by real organic demand and links, or is homepage-only.
  4. Pull the social footprint. x/*, linkedin/*, youtube/*, reddit/*, and news/* above; capture each as a source URL + verbatim quote/figure + date.
  5. Cross-reference, don't retype (Phase 3). Where claims and public signals disagree (e.g. "10,000 customers" vs. thin traffic/backlinks/community footprint), flag it. Label inferences as inferences; date anything stale.
  6. Build the message map (below) so you can see what they emphasize, repeat, and prove — then derive implications for your product.

Output: dossier template

One dossier per competitor (add a short cross-competitor summary if profiling several), dated:

markdown
# Competitor Dossier — [Name] (generated YYYY-MM-DD · depth: quick/deep)

## Footprint at a glance

| Field                 | Value                  | Source                        |
| --------------------- | ---------------------- | ----------------------------- |
| Tagline               |                        | homepage (browser/markdown)   |
| Founded / HQ          |                        | page / news                   |
| Team-size estimate    |                        | LinkedIn band                 |
| Funding               |                        | news                          |
| Domain rank / traffic |                        | seo/backlinks + rank-overview |
| Social following      | X / LinkedIn / YouTube | each surface                  |

## Positioning

- Value prop (one sentence) — source
- Target audience / ICP — source
- Positioning angle (how they frame the category) — source

## Message map

| Theme | What they claim | How often / where | Proof they show | Market echo? |
| ----- | --------------- | ----------------- | --------------- | ------------ |

(Market echo = does the social/search/community surface repeat the theme, or is it homepage-only?)

## Product & pricing

- Core capabilities + stated differentiators; product-direction signals from hiring/recent posts.
- Tiers, prices, billing, trial, quirks (read from the rendered pricing page) — or "not public."

## Search & content footprint

| Metric               | Value | Source                               |
| -------------------- | ----- | ------------------------------------ |
| Est. organic traffic |       | seo/competitors/domain-rank-overview |
| Top ranked keywords  |       | seo/competitors/ranked-keywords      |
| Top pages (strategy) |       | seo/competitors/relevant-pages       |
| Organic competitors  |       | seo/competitors/domain               |

## Backlinks & authority

| Metric            | Value | Source                            |
| ----------------- | ----- | --------------------------------- |
| Domain rank       |       | seo/backlinks/summary             |
| Referring domains |       | seo/backlinks/referring-domains   |
| Link-gap vs you   |       | seo/backlinks/domain-intersection |

## Customers & sentiment

- Named logos / industries (sourced) + community sentiment (praise/complaint themes, linked quotes).

## Strengths & weaknesses

|     | Evidence source |
| --- | --------------- |

## Implications for your product

Where they beat you, where you beat them, openings, threats.

## Sources & record cost

Every URL + date pulled; UnifAPI billing metadata or estimate.

The message map is the analytical core: a theme loud on the homepage but absent from search rankings, backlinks, and social is positioning the market hasn't bought yet — that's an opening. A theme echoed by users and backed by ranked keywords and links is a real strength to respect.

Depth modes

  • Quick scan — browser/markdown (homepage + pricing), seo/backlinks/summary + seo/competitors/domain-rank-overview (authority/traffic headline), x/users/by/username/{username}, linkedin/companies/{slug}. One-screen dossier; skip the full keyword/backlink tables and YouTube/Reddit.
  • Deep profile — all sections above: full seo/competitors/* and seo/backlinks/* tables, the rendered key pages, the complete social footprint, and the cross-competitor summary.

Guardrails

  • Public data only — rendered public pages, public social posts, company pages, videos, jobs, threads, and public SEO/backlink records. No private, internal, leaked, or paywalled data; exclude anything that looks like a leak as unverified.
  • Read-only: it researches and reports. It never contacts the competitor, posts, or touches any account — the operator's own assistant acts on the dossier.
  • Confirmed vs inferred: a claim read off a rendered page is confirmed; team size from a headcount band, "where the product is headed" from hiring, sentiment from a few threads, and SEO traffic estimates are inferences — label them. Be honest: don't inflate weaknesses or downplay strengths.
  • Dated snapshots: runs on-demand and returns a re-runnable source list — re-pull the same URLs/handles/domains to refresh and diff what changed. Social/community signals and traffic estimates skew and vary; weight by overlap across sources, not one thread.
  • Preserve the adaptation credit to Corey Haines when presenting this as an extension of the original framework.

Related Skills

  • competitor-launch-monitor (Competitive Intelligence Agent): when a profiled competitor ships something, track that launch and its public reception.
  • unifapi: the shared data skill — connect MCP and discover the SEO/backlinks/browser/X/LinkedIn/YouTube/Reddit/News operations above.

Frequently asked questions

What does the Competitor Profiling AI skill do?

When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning. Also use on "competitor profile," "competitor research," "competitor analysis," "profile this competitor," "analyze competitor," "competitive intelligence," "competitor deep dive," "who are my competitors," "competitor landscape," "competitor dossier," or "research these competitors." Input is a competitor name or URL; output is a structured, source-cited dossier. For tracking a specific launch and its reception, see competitor-launch-monitor.

Why use Competitor Profiling on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/competitive-intelligence-agent/competitor-profiling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Competitor Profiling?

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 Profiling?

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

Is the Competitor Profiling AI skill free?

Yes. It is published on GitHub by unifapi-agent 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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