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.
npx agentmods add skills/tinyfish-io/tinyfish-cookbook/tech-stack-detectivenpx skills add tinyfish-io/tinyfish-cookbook --skill tech-stack-detectivegit clone --depth 1 https://github.com/tinyfish-io/tinyfish-cookbookWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00115 | $0.02222 |
| Opus 5 | $0.00057 | $0.01111 |
| Sonnet 5 | $0.00023 | $0.00444 |
| Haiku 4.5 | $0.00012 | $0.00222 |
Grade A, and why
tech-stack-detective scanned grade A with 0 findings 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 3d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
# 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
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.
- 3d ago First seen · 226 lines · 115 tokens per session scan A 31da3882fae9
tech-stack-detective is a skill published in the GitHub repository tinyfish-io/tinyfish-cookbook (2,135 stars, last pushed 6d ago), licensed MIT. It adds 115 tokens to every session and 2,222 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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