REM: Reverse Engineer Marketing

Paste a link. Get the whole marketing machine behind it.

Paid ads · organic social · SEO & AI search · founders · funnel · positioning history, with the evidence for every claim.

npx reverse-engineer-marketing notion.so

Quick start · Web app · What it finds · Use it from your agent (MCP) · Example report · How it works

Nobody shares their growth alpha anymore. But most of it is public if you know where to look: ad libraries show which ads have been running for months (the winners), pixels show which platforms they buy, sitemaps show programmatic SEO, Wayback shows every repositioning, and founders post their playbook in public.

REM collects all of it from one link and turns it into a report: what the company's growth engine is, how the channels connect, and a step-by-step playbook to copy it. Every fact cites the evidence it came from, and everything REM couldn't see is listed as unknown, with a link to check it yourself.

Inspired by REA (Reverse Engineer Anything), but for marketing instead of binaries.

Quick start

Try it with zero setup, no keys: a recorded Cal AI teardown opens in the web app.

npx reverse-engineer-marketing demo

Then run it on any company:

npx reverse-engineer-marketing linear.app

That works with no API keys: website, tracking pixels (including pixels hidden inside Google Tag Manager containers), SEO/sitemaps, AI-crawler stance, Wayback positioning history, App Store/Google Play and Hacker News.

Add keys to unlock the rest:

export SCRAPECREATORS_API_KEY=...   # ad libraries, social stats, founders, Reddit (~25–50 credits per run)
export ANTHROPIC_API_KEY=...        # the written strategy report (Claude)
npx reverse-engineer-marketing calai.app

Output goes to ./rem-reports/<domain>-<date>/: report.md plus evidence.json (the raw evidence bundle).

It also accepts App Store and Google Play links: rem https://apps.apple.com/us/app/…/id123.

rem <url> [--no-ai] [--json] [--only paid,social,seo] [--handle tiktok=name] [--out dir]
rem web              # local web app (Claude Code / Codex subscription)
rem demo             # recorded Cal AI teardown in the web app, no keys
rem diff <domain>    # what changed since the last run: new/killed ads, growth, repositioning
rem setup            # connect REM to Claude Code / Codex (shows the plan, asks before changing anything)
rem doctor           # which sources your keys unlock
rem mcp              # run the MCP server

Related MCP server: datavessel

Web app: use your Claude Code or Codex plan

npx reverse-engineer-marketing web

Opens a local web app at http://localhost:4321. Paste a link, pick who writes the report, and watch the evidence come in and the playbook stream out. Every E12 citation opens the raw evidence behind it.

The Posts & ads tab is a review library of everything collected: Meta ads (active and paused, with creatives, inline video, run length, variants, top-by-impressions), Google and LinkedIn ads, TikTok and Instagram videos, YouTube, X and LinkedIn posts (company and founders), and Reddit threads and brand/founder replies. Filter by platform, sort by top / newest / longest running, open any original. The rail shows the brand logo and the founders with photos, roles, LinkedIn/X audience and a confidence badge.

Subscription engines run the CLI you're already logged into. REM strips ANTHROPIC_API_KEY / OPENAI_API_KEY from their environment so your plan is used, not an API key, and runs them in a scratch folder with your MCP servers and hooks disabled. The server listens on 127.0.0.1 only and rejects other hosts and origins. Add your Scrape Creators key from the top-right pill; it's saved to ~/.config/reverse-engineer-marketing/config.json (mode 600). Past runs are listed on the home page.

What it finds

Watch competitors over time

Run REM on the same company again next week, then:

rem diff calai.app

You get the deltas: Meta ads in the library and live now, ads launched, Apple Search Ads volume, follower growth per platform, sitemap growth, channels that switched on or off, homepage repositioning, and which ads and posts are new or no longer running.

Use it from your agent (MCP)

REM is an MCP server, so Claude Code, Codex, Cursor, or any MCP client can run it. With MCP you don't need an Anthropic key: your agent writes the report itself.

One command, with a plan you approve first (it backs up anything it replaces):

npx reverse-engineer-marketing setup        # detects Claude Code / Codex, shows the plan, asks
npx reverse-engineer-marketing setup --dry-run

Or by hand:

# Claude Code
claude mcp add reverse-engineer-marketing -e SCRAPECREATORS_API_KEY=... -- npx -y reverse-engineer-marketing mcp

# Codex
codex mcp add reverse-engineer-marketing --env SCRAPECREATORS_API_KEY=... -- npx -y reverse-engineer-marketing mcp

Any other client:

{
  "mcpServers": {
    "reverse-engineer-marketing": {
      "command": "npx",
      "args": ["-y", "reverse-engineer-marketing", "mcp"],
      "env": { "SCRAPECREATORS_API_KEY": "..." }
    }
  }
}

Then ask:

Reverse engineer the marketing of linear.app and give me a playbook I can copy.

Guided workflows

Named MCP prompts your client can run in one click (the REA pattern: investigate → spec → build → verify):

Prompt pack

Paste into Claude Code / Codex once REM is connected:

Reverse engineer the marketing of calai.app. Lead with their longest-running ads and quote the hooks.

Compare notion.so, coda.io and clickup.com: which channel does each one win, and what's the one play I should copy from each?

What changed at linear.app since my last run? New ad angles, ads they killed, audience growth.

Steal the playbook: reverse engineer calai.app, then build a 30-day plan for my app (myapp.com) with 10 ad hooks adapted from their winners and 3 UGC video briefs.

Which competitors bid on App Store search in Europe for "calorie tracker"-type apps, and what message does each show?

Install the bundled skill to teach your agent the workflow: copy skills/reverse-engineer-marketing/ into ~/.claude/skills/ (or your agent's skills folder).

How it works

link ──► website scan ──► discovers socials, apps, people, brand
              │
              ├─► pixels + GTM containers      ├─► Meta / Google / LinkedIn ad libraries
              ├─► robots, sitemaps, llms.txt   ├─► TikTok / IG / YouTube / X / LinkedIn
              ├─► Wayback history              ├─► founders (LinkedIn + X)
              └─► App Store / Play / HN        └─► Reddit / Product Hunt
                                │
                    evidence bundle (E1…En) + unknowns
                                │
                  deterministic channel scorecard (no AI)
                                │
             Claude / your agent + the harness ──► report.md
                                │
                    validator (citations, grounding)

Evidence contract. Every item is an observation (seen at a URL or API) or a derivation (computed, e.g. posts/week). The report's interpretations must cite evidence ids and label inferences. Anything not checked is an unknown, never "they don't do it". A pixel means intent, not current spend, and dollar spend is never invented.

The harness is a versioned file bundle (harness/): analyst role, guardrails, report template, a cue → lesson library, and one playbook per channel. Every report records the harness version and SHA. Change a playbook, run npm run eval, and compare scores. Recorded evidence bundles in evals/fixtures/ let you iterate on the prompt without re-scraping.

Cost per full run: about 25–50 Scrape Creators credits plus one Claude call (default claude-opus-5, override with REM_MODEL). Scrape Creators responses are cached for 24h in ~/.cache/reverse-engineer-marketing so agents can re-run without paying twice.

Use it responsibly

REM reads public information only: public web pages, public ad transparency libraries (Meta, Google, LinkedIn, Apple), public social profiles. It never logs in as anyone, never touches private data, and never runs anything on the target.

Same rule as clean-room engineering: study the strategy, ship your own. Learn which angles, offers, channels and page templates work, then make your own creatives and copy. Don't republish competitors' ads, footage, copy or brand assets; that's their IP. Respect each site's terms and your local laws.

Limits (honest)

• Apple Search Ads are only visible for the EU (Apple's DSA repository); other regions are unknown.

• TikTok and X ads have no global public library; REM reports the pixel and gives you the manual library link. Reddit's ad library was shut down.

• Ad library counts come from samples (first pages); REM says "in the sample".

• Founder, TikTok-handle and Product Hunt matches come from search and can be wrong; weak matches are flagged.

• Server-side tracking and email tools are invisible from the browser, so they're reported as unknown.

• Respect each site's terms and your local laws. REM reads public pages and public ad transparency libraries only.

Develop

git clone https://github.com/dansugc/reverse-engineer-marketing && cd reverse-engineer-marketing
npm install
npm run dev -- notion.so --no-ai   # run from source
npm test                           # unit tests (parsers, scorecard, validator)
npm run eval                       # validate example reports against recorded fixtures
npm run eval -- --live             # regenerate with Claude and score (costs tokens)

Adding a source: write a collector in src/sources/ that adds evidence to the store (observations or derivations, never conclusions) and records unknowns with a manual-check link, then teach src/analyze/scorecard.ts what it means. See AGENTS.md.

Built by DansUGC

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License

MIT. Built by DansUGC.