Skip to content
Agentmart
Submit a tool

competitor-profiling

When the user wants to research, profile, or analyze competitors from their URLs.

Skill by mkurman

IndexedMIT★ 325
Write a review

Chalk Labs

Need help deploying competitor-profiling?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/mkurman-competitor-profiling

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitor-alternatives. For sales-specific battle cards, see sales-enablement.

Use cases: documents, security, sales, research

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/competitor-profiling

What's inside

Skill name
competitor-profiling
Description
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitor-alternatives. For sales-specific battle cards, see sales-enablement.
License
MIT
Metadata
version: 1.0.0
Instructions
370 lines in SKILL.md · view on GitHub

Files (3)

  • SKILL.md
  • templates.md · references/
  • tool-reference.md · references/

SKILL.md (first lines)

## Saving Raw Data

Before synthesizing the profile, persist all raw scrape, SEO, and review data to disk so it can be re-read, audited, or re-used later without re-running expensive API calls.

**Directory layout** (relative to project root):

```
competitor-profiles/
├── raw/
│   └── <competitor-slug>/
│       └── <YYYY-MM-DD>/
│           ├── scrapes/    # one .md file per scraped page (homepage.md, pricing.md, ...)
│           ├── seo/        # one .json file per DataForSEO call (backlinks-summary.json, ranked-keywords.json, ...)
│           └── reviews/    # one .md or .json file per review source (g2.md, capterra.md, ...)
├── <competitor-slug>.md    # final synthesized profile
└── _summary.md             # cross-competitor summary
```

Rules:

- `<competitor-slug>` is lowercase, hyphenated (e.g. `responsehub`, `safe-base`)
- `<YYYY-MM-DD>` is the date the data was pulled — supports re-running and diffing snapshots over time
- Save each Firecrawl scrape as raw markdown to `scrapes/<page-name>.md`
- Save each DataForSEO response as raw JSON to `seo/<endpoint-name>.json`
- Save each review source to `reviews/<source>.md` (cleaned text) or `.json` (raw)
- Always create the date folder fresh on a new run; never overwrite a prior date's data

The synthesized profile (`<competitor-slug>.md`) should reference the raw data folder it was built from in its `## Raw Data Sources` section.

---

## Research Process

### Phase 1: Site Scraping (Firecrawl)

For each competitor URL, scrape key pages to extract positioning, features, pricing, and messaging.

#### Step 1: Map the site

Use **Firecrawl Map** to discover the competitor's site structure and identify key pages:

```
firecrawl_map → competitor URL
```

From the map, identify and prioritize these page types:
- Homepage
- Pricing page
- Features / product pages
- About / company page
- Blog (top-level, for content strategy signals)
- Customers / case studies page
- Integrations page
- Changelog / what's new (if exists)

#### Step 2: Scrape key pages

Use **Firecrawl Scrape** on each identified page:

```
firecrawl_scrape → each key page URL
```

Save each result to `competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/scrapes/<page-name>.md` before extracting fields.

Extract from each page:

| Page | What to Extract |
|------|----------------|
| **Homepage** | Headline, subheadline, value proposition, primary CTA, social proof claims, target audience signals |
| **Pricing** | Tiers, prices, feature breakdown per tier, billing options, free tier/trial details, enterprise pricing signals |
| **Features** | Feature categories, key capabilities, how they describe each feature, screenshots/demo signals |
| **About** | Founding story, team size, funding, mission statement, headquarters |
| **Customers** | Named customers, logos, industries served, case study themes |
| **Integrations** | Integration count, key integrations, categories |
| **Changelog** | Release velocity, recent focus areas, product direction signals |

#### Step 3: Scrape competitor reviews (optional but high-value)

Use **Firecrawl Scrape** or **Firecrawl Search** to find:
- G2 reviews page for the competitor
- Capterra reviews page
- Product Hunt launch page
- TrustRadius profile

Save each scraped review page to `competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/reviews/<source>.md`. Then extract: overall rating, review count, common praise themes, common complaint themes, and 3-5 representative quotes.

---

### Phase 2: SEO & Market Data (DataForSEO)

Use DataForSEO MCP tools to gather quantitative competitive intelligence. Save each raw response as JSON to `competitor-profiles/raw/<competitor-slug>/<YYYY-MM-DD>/seo/<endpoint-name>.json` before parsing it into the profile. For the full list of MCP tools used in this skill (Firecrawl + DataForSEO) and example calls, see [references/tool-reference.md](references/tool-reference.md).

#### Domain Authority & Backlinks

Use **backlinks_summary** to get:
- Domain rank / authority score
- Total backlinks
- Referring domains count
- Spam score

Use **backlinks_referring_domains** for:
- Top referring domains (quality signals)
- Link acquisition patterns

#### Keyword & Traffic Intelligence

Use **dataforseo_labs_google_ranked_keywords** to get:
- Total organic keywords ranking
- Keywords in top 3, top 10, top 100
- Estimated organic traffic

Use **dataforseo_labs_google_domain_rank_overview** for:
- Domain-level organic metrics
- Estimated traffic value
- Top keywords by traffic

Use **dataforseo_labs_google_keywords_for_site** to discover:
- What keywords they target

From mkurman/zorai, MIT.

More from mkurman/zorai

Signals

Stars
325
Forks
24
Open issues
0
Last commit (this folder)
May 5, 2026 (6 months ago)
Last release
Oct 23, 2026 (13 days ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0.0
License
MIT
Website
zorai.app/
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from mkurman

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get competitor-profiling running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying competitor-profiling?

CallGet help
Cite this page

competitor-profiling skill. Agent Mart. https://agentmart.dev/skills/mkurman-competitor-profiling (last updated 2026-10-24)