Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .claude/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
skills CLI
$ npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a codex
Project install goes to .agents/skills/; add -g for ~/.codex/skills/.
Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .agents/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skills CLI
$ npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a cursor
Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.
Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .cursor/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
skills CLI
$ npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a gemini-cli
Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.
Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .gemini/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
skills CLI
$ npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a github-copilot
Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.
Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .github/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
skills CLI
$ npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a opencode
OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
Install the "tech-stack-detective" agent skill from https://github.com/tinyfish-io/tinyfish-cookbook/tree/main/skills/tech-stack-detective into .opencode/skills/tech-stack-detective/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-detective", then confirm the skill loads.
OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
Skill name
tech-stack-detective
GitHub stars
2.2k
Token cost
~2.2k tokens
SKILL.md length
344 words
Files
1
Skills in repo
28
Repo updated
First seen
Licence
MIT
At a glance
Reverse-engineer a company's tech stack from public signals — job listings, StackShare, GitHub, engineering blog, and their live website.
Works in 3 steps: Gather inputs → Parallel research → Synthesize the stack map
A user asks what tech does Stripe use
SKILL.md covers Pre-flight Check (REQUIRED), Step 1 — Gather inputs, Step 2 — Parallel research and Step 3 — Synthesize the stack…, plus 1 more section
Calls npm; reaches stackshare.io and linkedin.com
What it does
Tech Stack Detective is an agent skill from tinyfish-io/tinyfish-cookbook. 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.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Stripe, GitHub and Notion. The repository describes itself as: A collection of sample apps and recipes built with the TinyFish web agent. Open-source examples for you to learn & build! The licence is MIT.
When your agent uses it
A user asks what tech does Stripe use
Whats Linears stack
What does Notion run on
Reverse engineer [company]s tech stack
Example prompts
“what tech does Stripe use”
“s Linear”
“what does Notion run on”
“/tech-stack-detective”
Requirements
Node.js
Workflow steps
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 292ee62. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
npm
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
stackshare.io
linkedin.com
github.com
google.com
Also links to:
agent.tinyfish.ai
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tech Stack Detective loads about 2.2k tokens when it runs. Until then it costs about 124 tokens; SKILL.md has 344 words of instructions outside code blocks.
Always· name and description, kept in context so the agent knows when to use it
~124
When it runs· the whole SKILL.md, loaded when a task matches
~2.2k
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
Download SKILL.mdSave it as .claude/skills/tech-stack-detective/SKILL.md (or your agent's skills folder).
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?
bash
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
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
Tech Stack Detective next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
Tech Stack Detective compared with similar skills
Skill
Stars
Used in
Tokens
Auto-check
Licence
Repo updated
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Reverse-engineer a company's tech stack from public signals — job listings, StackShare, GitHub, engineering blog, and their live website. Tech Stack Detective is an agent skill from tinyfish-io/tinyfish-cookbook. Reverse-engineer a company's tech stack from public signals — job listings, StackShare, GitHub, engineering blog, and their live website.
When should I use Tech Stack Detective?
Tech Stack Detective fits situations like: A user asks what tech does Stripe use; whats Linears stack; what does Notion run on; reverse engineer [company]s tech stack.
How do I install Tech Stack Detective in Claude Code?
Run `npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a claude-code`. Or copy the skill folder (skills/tech-stack-detective in tinyfish-io/tinyfish-cookbook) into .claude/skills/tech-stack-detective in your project. Claude Code loads it when a task matches its description.
How do I install Tech Stack Detective in Codex?
Run `npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a codex`. Or copy the skill folder (skills/tech-stack-detective in tinyfish-io/tinyfish-cookbook) into .agents/skills/tech-stack-detective in your project. Codex loads it when a task matches its description.
Can I use Tech Stack Detective in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detective -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tech-stack-detective, .gemini/skills/tech-stack-detective, .github/skills/tech-stack-detective and .opencode/skills/tech-stack-detective in your project.
What does Tech Stack Detective need to run?
Going by SKILL.md and its folder, Tech Stack Detective needs the command-line tools its instructions call (npm). Our summary lists: Node.js.
Does Tech Stack Detective access the network?
SKILL.md names 5 domains. In commands or code: stackshare.io, linkedin.com, github.com and google.com; the agent is likely to contact these when it follows the instructions. As links in the text: agent.tinyfish.ai. This is read from the text; nothing was executed.
Is Tech Stack Detective safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Tech Stack Detective use?
Tech Stack Detective is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tech Stack Detective use?
About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Tech Stack Detective?
Skills that share tags, products or a category with Tech Stack Detective: Brand Logos (usenotra/notra, 255 stars), Dev MCP Setup (evolution-foundation/evo-nexus, 544 stars), Composio (ComposioHQ/composio, 30k stars) and Uncodixfy (pdsuwwz/chatgpt-vue3-light-mvp, 578 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tech Stack Detective?
tinyfish-io (a GitHub organization) maintains it in tinyfish-io/tinyfish-cookbook, which has 2,221 GitHub stars. The repository holds 28 skills in this directory. The repository was last updated on October 1, 2026.
Source: tinyfish-io/tinyfish-cookbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.