tech-stack-teardown

tech-stack-teardown is a skill for Claude Code, Codex from gooseworks-ai/goose-skills. It costs 79 tokens per session (3,258 once invoked), scanned A, original, MIT.

A public-signal investigator for mapping a company's sales and marketing tools. It checks DNS records, website code, technology profiles, blacklist databases, and public spam complaints without logging into the company's systems.

In plain words
What is it for?
Use it to identify CRMs, cold-email platforms, people databases, email delivery services, marketing tools, ad-tracking pixels, website builders, chat tools, and analytics systems.
Why use it?
It helps reveal how a company runs outreach, email, advertising, analytics, and support when you cannot access its internal tools. This gives you a view of the infrastructure behind its public-facing operations.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to identify CRMs, cold-email platforms, people databases, email delivery services, marketing tools, ad-tracking pixels, website builders, chat tools, and analytics systems.

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Install with agentmods
npx agentmods add skills/gooseworks-ai/goose-skills/tech-stack-teardown
About the project

Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.

gooseworks-ai/goose-skills · 1,202 stars · on GitHub · gooseworks.ai

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gooseworks-ai/goose-skills --skill tech-stack-teardown
Clone the repo
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for tech-stack-teardown

README.md
[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/tech-stack-teardown/github.svg)](https://agentmods.dev/skills/gooseworks-ai/goose-skills/tech-stack-teardown)
Your own site
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/tech-stack-teardown"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/tech-stack-teardown/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for tech-stack-teardown

Your own site · 80×15
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/tech-stack-teardown"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/tech-stack-teardown.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.03258
Opus 5 $0.00039 $0.01629
Sonnet 5 $0.00016 $0.00652
Haiku 4.5 $0.00008 $0.00326

Measured 9d ago against content hash 40b4b384b148, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

tech-stack-teardown scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/recon.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

# curl (website source fetch) — included on macOS/Linux
skills/competitive-intel/capabilities/tech-stack-teardown/SKILL.md · 326 lines

How it starts

The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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, Amplitude, PostHog, Heap (via website source)
Outbound Domains Separate cold sending domains (via SPF-only Google Workspace + redirect to primary)

How It Works

The skill runs 5 layers of detection, each revealing different signals:

Layer 1: DNS Records (Free, instant)

MX     → Primary email provider (Google Workspace, Microsoft 365, etc.)
SPF    → Every service authorized to send email on their behalf
DKIM   → Cryptographic proof of which tools actually send email
DMARC  → Email authentication policy (how strict they are)
TXT    → Misc verifications (Smartlead tracking domains, tool verifications)
CNAME  → Subdomains pointing to third-party services

This is the highest-signal layer. SPF and DKIM don't lie — if SendGrid is in their SPF, they use SendGrid.

Layer 2: Website Source Inspection (Free, instant)

Read the full file on GitHub · 326 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 326 lines · 79 tokens per session scan A 40b4b384b148

Subscribe to this mod's changes

tech-stack-teardown is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 79 tokens to every session and 3,258 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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