competitor-profiling
When the user wants to research, profile, or analyze competitors from their URLs.
Skill by mkurman
Chalk Labs
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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.
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About
Use cases: documents, security, sales, research
Reviews
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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.
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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
- Repository
- github.com/mkurman/zorai
- Website
- zorai.app/
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
competitor-profiling skill. Agent Mart. https://agentmart.dev/skills/mkurman-competitor-profiling (last updated 2026-10-24)