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Agent skill

Competitor Profiling

by unifapi-agent in unifapi-agent/agents

When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning.

MITAuto-check passedMarketing & SEO

Install Competitor Profiling

skills CLI
$ npx skills add unifapi-agent/agents --skill competitor-profiling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install unifapi-agent/agents competitor-profiling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/competitive-intelligence-agent/competitor-profiling .claude/skills/competitor-profiling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
competitor-profiling
GitHub stars
586
Token cost
~2.4k tokens
SKILL.md length
781 words
Files
2
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning.

  • Works in 6 steps: Set scope. Confirm the competitor (name… → Read the owned site (Phase 1). Pull… → Pull search & market data (Phase 2). Run… → …
  • Wants to research
  • SKILL.md covers Use UnifAPI for live evidence, Workflow, Output: dossier template and Depth modes, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Competitor Profiling is an agent skill from unifapi-agent/agents. 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…

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `README.md`).

It sits in Marketing & SEO, covering Competitor analysis and Link building. It works with LinkedIn. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.

When your agent uses it

  • Wants to research
  • Analyze a competitor from its public footprint — site

Example prompts

  • “competitor profile,”
  • “competitor research,”
  • “competitor analysis,”
  • “/competitor-profiling”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Set scope. Confirm the competitor (name + URL), your product, and depth: quick scan (positioning + pricing + headline search/social…
  2. Read the owned site (Phase 1). Pull positioning, target audience, value prop, pricing, features, and proof with browser/markdown on the…
  3. Pull search & market data (Phase 2). Run the seo/competitors/* ops for rank, traffic, ranked keywords, and top pages, and the…
  4. Pull the social footprint. x/*, linkedin/*, youtube/*, reddit/*, and news/* above; capture each as a source URL + verbatim quote/figure +…
  5. Cross-reference, don't retype (Phase 3). Where claims and public signals disagree (e.g. "10,000 customers" vs. thin…
  6. Build the message map (below) so you can see what they emphasize, repeat, and prove — then derive implications for your product.

What it can do on your machine

Read from SKILL.md and the folder at commit fb53247. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Competitor Profiling loads about 2.4k tokens when it runs. Until then it costs about 154 tokens; SKILL.md has 781 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~154
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 781 words, ~2,403 tokens.

Download SKILL.mdSave it as .claude/skills/competitor-profiling/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
competitor-profiling
description
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.
license
MIT
metadata.author
UnifAPI
metadata.version
1.0.0
metadata.adapted_from
https://github.com/coreyhaines31/marketingskills
metadata.adapted_author
Corey Haines

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.
Show full SKILL.md (298 more words)Show less

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.
  • 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.

© unifapi-agent, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/competitive-intelligence-agent/competitor-profiling of unifapi-agent/agents.

  • SKILL.md
  • README.md

Open the folder on GitHubat commit fb53247

Compare with similar skills

Competitor Profiling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Competitor Profiling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Competitor Profiling this skillunifapi-agent/agents586—~2.4kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7872 repos~4.6kAutomated safety check: PassMIT
DataForSEO SEO Toolkitzubair-trabzada/dataforseo-claude164—~3.2kAutomated safety check: NotesMIT
DataForSEO Live SEO DataAgriciDaniel/claude-seo18k—~4.4kAutomated safety check: PassMIT
Competitor Analysispetera2c/simple-table2293 repos~1.1kAutomated safety check: PassMIT
SEO APIseranking/seo-skills160—~4.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Competitor Profiling

What does Competitor Profiling do?

When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning. Competitor Profiling is an agent skill from unifapi-agent/agents. When the user wants to research, profile, or analyze a competitor from its public footprint — site, search/SEO, backlinks, social, and positioning.

When should I use Competitor Profiling?

Competitor Profiling fits situations like: wants to research; analyze a competitor from its public footprint — site.

How do I install Competitor Profiling in Claude Code?

Run `npx skills add unifapi-agent/agents --skill competitor-profiling -a claude-code`. Or copy the skill folder (skills/competitive-intelligence-agent/competitor-profiling in unifapi-agent/agents) into .claude/skills/competitor-profiling in your project. Claude Code loads it when a task matches its description.

How do I install Competitor Profiling in Codex?

Run `npx skills add unifapi-agent/agents --skill competitor-profiling -a codex`. Or copy the skill folder (skills/competitive-intelligence-agent/competitor-profiling in unifapi-agent/agents) into .agents/skills/competitor-profiling in your project. Codex loads it when a task matches its description.

Can I use Competitor Profiling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add unifapi-agent/agents --skill competitor-profiling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/competitor-profiling, .gemini/skills/competitor-profiling, .github/skills/competitor-profiling and .opencode/skills/competitor-profiling in your project.

What does Competitor Profiling need to run?

SKILL.md names no scripts, command-line tools or credentials: Competitor Profiling is instructions for the agent only.

Does Competitor Profiling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Competitor Profiling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Competitor Profiling use?

Competitor Profiling is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Competitor Profiling use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Competitor Profiling?

Skills that share tags, products or a category with Competitor Profiling: SEO Dataforseo (AgriciDaniel/codex-seo, 787 stars), DataForSEO SEO Toolkit (zubair-trabzada/dataforseo-claude, 164 stars), DataForSEO Live SEO Data (AgriciDaniel/claude-seo, 18k stars) and Competitor Analysis (petera2c/simple-table, 229 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Competitor Profiling?

unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 586 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.

Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.