/rea-growth

name: rea-growth
description: Use when the user wants to know how a company gets its users, reverse engineer a competitor's growth or distribution, copy what a winning company does on each channel, or study a brand's content strategy (titles, thumbnails, covers, captions, graphics) on YouTube, TikTok, Instagram, X, LinkedIn or Reddit. Also for "how did X grow", "teardown of X's marketing", "what content works for X".

REA growth

A company in, a playbook out: when it grew, which moves did it, how its content is made on each channel and what wins there, and ten plays the user can copy, each with a ready prompt.

Runs on treg. If it isn't set up (`treg --version` fails), read https://treg.to/llms.txt and follow it to install and sign in, then come back here.

Modes

| Mode | When | Steps | Delivers | |---|---|---|---| | **full** (default) | "how does TARGET grow", "teardown", "what to copy" | 0-12 | `playbook.md` + `playbook.html` + CSVs | | **content** | titles, thumbnails, covers, captions, graphics, "what content works", and nothing about growth | 0, 1 (handles only), 7, 13 | `content.md` + `content.html` + CSVs |

How the steps connect

• Step 1 gives the path families step 4 maps keywords to, the social handles step 7 reads, and

the non-user touchpoints step 11 traces.

• Step 2 gives the dates (launches, funding, pixels first seen) step 3 lines up with traffic.
• Step 3 marks the inflection. Steps 4-7 and 12 compare before vs after it.
• Steps 4-8 each cover one channel group: search and AI answers, ads, who talks about TARGET,

TARGET's own content, the product loop. Step 12 reads them all by channel.

• Step 9 says what TARGET scales next; step 10 lines every move up against how fast TARGET grew;

step 11 orders everything along the customer's path; step 12 turns it into plays. Step 13 is content mode's shorter ending.

Rules

• Every finding is **SEEN** (a call result or an image shows it; link it) or **INFERRED** (reasoned

from what was seen; say from what). Nothing seen → "not seen", never guessed.

• Public evidence only. Create no accounts, fill no forms, contact no one.
• Before an endpoint's first call: `catalog_get` it, quote its price, check it fits the cap. Every

DataForSEO endpoint takes a JSON array body: [{…}].

• Free first: `treg.web.search` and `treg.web.extract` with header `X-Treg-Route-Prefer: tinyfish`.

Blocked page → the route tries the next vendor; still blocked → list it as blocked, move on.

• An endpoint fails → `catalog_search` the same job, try one alternative → else "unavailable".
• Python does all math; every number shown comes from code output. Date every count.
• ◆ After each step: 3-line finding + what the next step checks + est. cost → continue | skip | stop.

0 · Setup

INPUT: mode | target domain | user's company + domain | user's stage (pre-launch / early revenue / scaling) | budget cap (default $3) | country (US → location_code 2840, language_code en). Content mode: mode | target | channels (default all six) | cap; the user's company is optional and only feeds the "for your company" part of step 13. Ask for anything missing before the first call. No target → `treg.web.search` {"q":"fastest growing <category> startups 2026"} → propose 3, let the user pick.

1 · The site — ~$0.001 (page reads free)

• `treg.web.map` {"url":"https://TARGET"} → every URL. Group by path family (use cases, templates,

integrations, partners, pricing, docs, blog, changelog, compare/vs).

• `treg.web.extract` {"url":…} on home, about, pricing, sign-up, onboarding or getting-started doc,

referral/affiliate/partner page, changelog. Record: sign-up method (SSO, email, card up front?), waitlist or invite codes, what is free, trial length and how it ends (days or credits), plans and prices, the first thing a new user is told to do, every place a non-user meets the product (shared outputs, public pages, embeds, badges, invites, emails sent to outsiders).

• Handles: every official social account linked from the site (footer, about, press page) and the

founders' X handles (about page, else `treg.web.search` {"q":"TARGET founder"}). A handle not linked from the site counts only if its bio links back to TARGET.

• Content mode: read home and about only, collect the handles, go to step 7.

2 · Stack, partners, news — ~$0.16

• `predictleads.companies.technology_detections` {company_id_or_domain} → analytics, ad pixels, A/B

testing, referral/affiliate, lifecycle email, chat. Keep score ≥ 0.9 and not behind_firewall (the rest includes sites their users build). A pixel = SEEN; what they run on it = INFERRED.

• `predictleads.companies.connections` → partners, integrations, investors.
• `predictleads.companies.news_events` + `predictleads.companies.financing_events` → launches,

press, funding, reported revenue (label it "reported").

3 · When it grew — ~$0.15

• `dataforseo.x.dataforseo-labs-google-historical-rank-overview-live`

[{"target":"TARGET","location_code":2840,"language_code":"en","date_from":<24 months ago>}] → monthly organic + paid traffic estimate.

• Traffic by channel: `akta.x.website-traffic` {"company":"TARGET"} needs the team's own Akta key;

without one, say "unavailable" and use the organic vs paid split above.

• Mark the inflection (largest sustained step up). Line up step 2's launches, funding and the

first-seen dates of ad pixels against it.

4 · Search and AI answers — ~$0.14

• `dataforseo.google.domain.ranked_keywords`

[{"target":"TARGET","location_code":2840,"language_code":"en","limit":1000,"order_by":["ranked_serp_element.serp_item.etv,desc"]}] → keyword, URL, position, volume. Map URLs to step 1's path families; brand vs non-brand share; families that ranked before the inflection.

• 3 buyer questions for the category → `cloro.ai-search.chatgpt.scrape` {"prompt":…,"country":"US"}:

order brands are named, which pages are cited.

5 · Ads — ~$0.03

• Meta: `scrapecreators.x.v1-facebook-