Agent skill · Content & Marketing

tech-stack-teardown

Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.

gooseworks-aigithub.com/gooseworks-aiGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add gooseworks-ai/goose-skills --skill tech-stack-teardown --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 12 KB
Bundled scripts: yes
Path: skills/competitive-intel/capabilities/tech-stack-teardown/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,091
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Tech Stack Recon Reverse-engineer a company's sales, marketing, and outbound infrastructure from public signals. No login, no API access to their tools needed — everything is derived from DNS records, website source code, technology profiling, blacklist databases, and public complaints. ## What It Detects | Category | Tools Detected | |----------|---------------| | **CRM** | HubSpot, Salesforce (via SPF, website pixels, DNS) | | **Cold Email Tools** | Smartlead, Instantly, Outreach, Salesloft, Lemlist (via SPF, DKIM, TXT records, website source) | | **People Databases** | Apollo, ZoomInfo, Clearbit, 6sense (via website tracker scripts) | | **Email Delivery** | SendGrid, Amazon SES, Postmark, Mailgun, Mandrill (via SPF includes, DKIM selectors) | | **Email Marketing** | Mailchimp, Brevo, ActiveCampaign, Klaviyo (via DKIM selectors) | | **Ad Retargeting** | LinkedIn Insight Tag, Facebook Pixel, AdRoll, Reddit Ads, Twitter Ads (via Apify profiler + source) | | **Website Builder** | Webflow, Framer, Next.js, WordPress (via Apify profiler + source) | | **Chat / Support** | Intercom, Drift, Crisp, Zendesk (via website source) | | **Analytics** | Google Analytics, Segment, Mixpanel, Amp

What's inside
Steps it walks through
  1. What It Detects
  2. How It Works
  3. Layer 1: DNS Records (Free, instant)
  4. Layer 2: Website Source Inspection (Free, instant)
  5. Layer 3: Apify Technology Profiler (Pay-per-use, ~$0.005/domain)
  6. Layer 4: Blacklist Checks (Free, instant)
  7. Layer 5: Public Complaint Search (Free)
  8. Cost
  9. Setup
  10. 1. Required
  11. 2. Optional (for Apify Technology Profiler)
  12. Usage
  13. Single Company
  14. Batch of Companies
Ships with 2 files
  • scripts/recon.py
  • skill.meta.json
Commands it runs
dig (DNS lookups) — included on macOS/Linux
which dig
curl (website source fetch) — included on macOS/Linux
which curl
Python 3 with requests + dotenv
pip3 install requests python-dotenv
Get your token at https://console.apify.com/account/integrations
Add to .env:
python3 scripts/recon.py --domains pump.co
python3 scripts/recon.py --domains "dili.ai,pump.co,runautomat.com"
More from goose-skills
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About this skill
What does the tech-stack-teardown skill do?

Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.

How do I install it?

Run `npx skills add gooseworks-ai/goose-skills --skill tech-stack-teardown --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going