| name | tech-stack-detective |
| description | Reverse-engineer a company's tech stack from public signals — job listings, StackShare, GitHub, engineering blog, and their live website. Use this skill when a user asks "what tech does Stripe use", "what's Linear's stack", "what does Notion run on", "reverse engineer [company]'s tech stack", "what framework does [company] use", "how is [company] built", "what languages does [company] hire for", or any request to figure out what technology a company uses under the hood. |
Tech Stack Detective
Reverse-engineer any company's tech stack from public signals — job listings, StackShare, GitHub, engineering blog, and their live website — then return a layered map of what they actually run.
Pre-flight Check (REQUIRED)
Before making any TinyFish call, always run BOTH checks:
1. CLI installed?
which tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_INSTALLED"
If not installed, stop and tell the user:
Install the TinyFish CLI: npm install -g @tiny-fish/cli
2. Authenticated?
tinyfish auth status
If not authenticated, stop and tell the user:
You need a TinyFish API key. Get one at: https://agent.tinyfish.ai/api-keys
Then authenticate:
tinyfish auth login
Do NOT proceed until both checks pass.
Step 1 — Gather inputs
You need:
- Company name — e.g.
Stripe, Linear, Vercel, Notion
- Company domain — e.g.
stripe.com (infer from company name for well-known companies, ask if unsure)
Optional:
- Area of interest — e.g. "just the frontend", "their data pipeline", "what they use for auth" (if specified, focus the output on that layer)
Step 2 — Parallel research
Fire all 5 agents simultaneously.
tinyfish agent run \
--url "https://stackshare.io/{COMPANY_SLUG}" \
"You are on the StackShare profile for {COMPANY}.
Extract their full tech stack as listed:
- All tools and services listed under each category
- Category names (e.g. Languages, Frameworks, Data Stores, DevOps, etc.)
- Any tools listed as 'used by' this company
STRICT RULES:
- Do NOT click any tool links
- Do NOT navigate away
- Read only what is visible on this page
- If the page returns 404 or no company found, return {found: false}
Return JSON: {found: bool, stack: [{category, tools: []}]}" \
--sync > /tmp/tsd_stackshare.json &
tinyfish agent run \
--url "https://www.linkedin.com/jobs/search/?keywords={COMPANY_ENCODED}+engineer" \
"You are on LinkedIn job listings for {COMPANY}.
Read the job titles and visible snippets for engineering roles.
Extract all technologies, languages, frameworks, and tools mentioned in:
- Job titles
- Visible job description snippets
Focus on: programming languages, frameworks, databases, cloud providers, tooling.
STRICT RULES:
- Do NOT click any job listing
- Read only titles and visible preview text
- Maximum 15 listings then stop
- Deduplicate — list each technology once
Return JSON: {technologies: [{name, category, mention_count}]}" \
--sync > /tmp/tsd_linkedin.json &
tinyfish agent run \
--url "https://github.com/{COMPANY_SLUG}" \
"You are on the GitHub organization page for {COMPANY}.
Extract signals about their tech stack from public repositories:
- Top 6 pinned or most-starred repositories
- Primary programming languages used across repos (visible in language bars)
- Any infrastructure or tooling repos (e.g. terraform, kubernetes configs, SDKs)
- Any open source projects that reveal their internal stack
STRICT RULES:
- Do NOT click into any repository
- Read only what is visible on the org page
- If no org found, return {found: false}
Return JSON: {found: bool, top_repos: [{name, description, language, stars}], languages: [], infra_signals: []}" \
--sync > /tmp/tsd_github.json &
tinyfish agent run \
--url "https://www.google.com/search?q=site:{COMPANY_DOMAIN}+engineering+OR+blog+OR+tech" \
"You are on Google search results for the engineering blog of {COMPANY} at {COMPANY_DOMAIN}.
Find the engineering or tech blog URL, then read the visible post titles and snippets.
Extract:
- Technologies, tools, or architectural decisions mentioned in post titles and snippets
- Any posts about infrastructure, scaling, or architecture decisions
- Any open source tools they built or adopted
STRICT RULES:
- Do NOT click any links
- Read only titles and snippets visible in search results
- Maximum 10 results then stop
Return JSON: {blog_url, tech_signals: [{technology, context}], architecture_posts: [{title, snippet}]}" \
--sync > /tmp/tsd_blog.json &
tinyfish agent run \
--url "https://{COMPANY_DOMAIN}" \
"You are on {COMPANY}'s homepage at {COMPANY_DOMAIN}.
Analyze the page for frontend technology signals:
- JavaScript framework clues (React, Vue, Angular, Svelte, etc.) — look for script tags, __NEXT_DATA__, __nuxt, ng-, data-reactroot, etc.
- CSS framework signals (Tailwind classes, Bootstrap, etc.)
- Analytics tools (Google Analytics, Segment, Mixpanel, etc.)
- CDN or hosting signals (Vercel, Cloudflare, Fastly, etc.)
- Any visible 'built with' or 'powered by' badges
- Meta tags that reveal framework or CMS
STRICT RULES:
- Do NOT navigate away from the homepage
- Read source signals from the visible page
- Return only what you can confidently infer — do not guess
Return JSON: {frontend_framework, css_framework, analytics: [], cdn_hosting, other_signals: []}" \
--sync > /tmp/tsd_website.json &
wait
echo "=== STACKSHARE ===" && cat /tmp/tsd_stackshare.json
echo "=== JOBS ===" && cat /tmp/tsd_linkedin.json
echo "=== GITHUB ===" && cat /tmp/tsd_github.json
echo "=== BLOG ===" && cat /tmp/tsd_blog.json
echo "=== WEBSITE ===" && cat /tmp/tsd_website.json
Before running, replace:
{COMPANY} — e.g. Stripe{COMPANY_SLUG} — lowercase, hyphenated e.g. stripe{COMPANY_ENCODED} — URL-encoded e.g. Stripe{COMPANY_DOMAIN} — e.g. stripe.com
Step 3 — Synthesize the stack map
Combine signals from all sources. Assign a confidence level to each technology based on how many sources confirmed it:
- High confidence — mentioned in 3+ sources or explicitly listed on StackShare
- Medium confidence — mentioned in 2 sources or inferred from job listings
- Low confidence — mentioned in only 1 source or inferred from indirect signals
## Tech Stack — {COMPANY}
*Reverse-engineered from StackShare, job listings, GitHub, engineering blog, and live site analysis*
*Data fetched: {date}*
---
### 🖥️ Frontend
| Technology | Confidence | Source |
|---|---|---|
| {tech} | 🟢 High / 🟡 Medium / 🔴 Low | {sources} |
### ⚙️ Backend
| Technology | Confidence | Source |
|---|---|---|
### 🗄️ Data & Storage
| Technology | Confidence | Source |
|---|---|---|
### ☁️ Infrastructure & DevOps
| Technology | Confidence | Source |
|---|---|---|
### 📊 Analytics & Monitoring
| Technology | Confidence | Source |
|---|---|---|
### 🔧 Developer Tooling
| Technology | Confidence | Source |
|---|---|---|
---
### 🧠 Key Architectural Signals
{2-4 bullet points on notable architectural choices inferred from the research}
- e.g. "Strong Go + Rust signals in job listings suggest performance-critical backend services"
- e.g. "Multiple Kubernetes and Terraform repos on GitHub indicate heavy infrastructure-as-code culture"
---
### 🔍 Sources
- StackShare: {found / not found}
- Job listings: {N} engineering roles analyzed
- GitHub: {N} public repos
- Engineering blog: {blog_url or not found}
- Live site analysis: {COMPANY_DOMAIN}
### ⚠️ Low Confidence Items
{List technologies with only 1 source signal and what that signal was}
Edge Cases
- StackShare profile doesn't exist — skip it, rely on other 4 sources, note the gap
- Company GitHub org not found — try common variations (
{company}hq, {company}-inc, {company}io) before giving up
- Private company with minimal public presence — job listings and website analysis will be the richest sources; be upfront about confidence levels
- Very large company (Google, Meta, Amazon) — stack is extremely diverse; ask if the user wants a specific team or product area, otherwise summarize known public stacks
- Startup with almost no public signals — be honest: "Limited public signals found. Based on job listings alone: [findings]"
- Company uses different names on GitHub vs LinkedIn — use the domain as the anchor and note the discrepancy
