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?
bashwhich 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?
bashtinyfish 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.
bash# Agent 1 — StackShare profile 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 & # Agent 2 — Job listings (tech signals from requirements) 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 & # Agent 3 — GitHub organization 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 & # Agent 4 — Engineering blog 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 & # Agent 5 — Live website analysis 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
