Stalk My Interviewer
Deploy parallel TinyFish agents to research an interviewer across LinkedIn, GitHub, Twitter/X, news, and conference platforms — then synthesize a structured prep report so you walk in knowing exactly who you're talking to.
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:
- Interviewer's full name — e.g. "Sarah Chen"
- Company — e.g. "Stripe", "Anthropic", "Linear"
- Role you're interviewing for (optional but improves output) — e.g. "Senior Software Engineer"
If any are missing, ask before proceeding. If the name is very common (e.g. "John Smith"), ask for company and role to disambiguate before searching.
Step 2 — Parallel research
Fire all agents simultaneously. Every agent searches a different surface — run them all at once using & + wait.
bash# Agent 1 — LinkedIn tinyfish agent run \ --url "https://www.linkedin.com/search/results/people/?keywords={FULL_NAME_ENCODED}+{COMPANY_ENCODED}" \ "You are on a LinkedIn people search results page. Find the profile for {FULL_NAME} who works or worked at {COMPANY}. Click the most relevant result. On their profile extract: - Current job title and company - Previous roles (last 3 positions: title, company, duration) - Education (degrees, institutions) - Skills listed (top 10) - Summary / About section (if visible) - How long they have been at {COMPANY} STRICT RULES: - Click only the most relevant profile result — do not browse multiple profiles - Do NOT scroll more than twice on the profile page - If the page asks you to log in, extract whatever is visible before the gate and return it - Do NOT click any other links Return JSON: {name, current_title, current_company, tenure_at_company, previous_roles: [{title, company, duration}], education: [{degree, institution}], skills: [], summary}" \ --sync > /tmp/smi_linkedin.json & # Agent 2 — GitHub (relevant if role is technical) tinyfish agent run \ --url "https://github.com/search?q={FULL_NAME_ENCODED}+{COMPANY_ENCODED}&type=users" \ "You are on GitHub user search results for {FULL_NAME} at {COMPANY}. Find the most likely profile match. Click it. On their GitHub profile extract: - Username - Bio - Location - Company listed on profile - Pinned repositories (name, description, language, stars) - Most used programming languages (visible in stats or repos) - Any notable open source contributions or projects STRICT RULES: - Click only the single most relevant result - Do NOT navigate to individual repos - Read only what is visible on their profile page - If no clear match found, return {found: false} Return JSON: {found: bool, username, bio, location, pinned_repos: [{name, description, language, stars}], languages: [], notable_work}" \ --sync > /tmp/smi_github.json & # Agent 3 — Twitter/X tinyfish agent run \ --url "https://x.com/search?q={FULL_NAME_ENCODED}+{COMPANY_ENCODED}&src=typed_query&f=user" \ "You are on Twitter/X user search results for {FULL_NAME} at {COMPANY}. Find the most likely profile match. Click it. On their Twitter profile extract: - Display name and handle - Bio - Pinned tweet (if any) - Topics they tweet about most (infer from visible tweets — read up to 10) - Any strong opinions or recurring themes - Approximate tweet frequency / activity level STRICT RULES: - Click only the most relevant profile - Read only the first 10 visible tweets — do NOT scroll further - Do NOT click any tweet links or replies - If no match found, return {found: false} Return JSON: {found: bool, handle, bio, pinned_tweet, topics: [], opinions: [], activity_level}" \ --sync > /tmp/smi_twitter.json & # Agent 4 — Google News & web mentions tinyfish agent run \ --url "https://www.google.com/search?q=\"{FULL_NAME_ENCODED}\"+\"{COMPANY_ENCODED}\"&tbm=nws" \ "You are on Google News search results for {FULL_NAME} at {COMPANY}. Read the titles and snippets of the first 10 visible news results. Extract: - Any articles authored by or quoting {FULL_NAME} - Key topics they are associated with in the news - Any notable achievements, announcements, or controversies mentioned STRICT RULES: - Do NOT click any article links - Read only titles and snippets visible in the search listing - Maximum 10 results then stop Return JSON: {mentions: [{title, snippet, source, date}], topics: [], authored_articles: []}" \ --sync > /tmp/smi_news.json & # Agent 5 — Company engineering blog tinyfish agent run \ --url "https://www.google.com/search?q=site:{COMPANY_DOMAIN}+\"{FULL_NAME_ENCODED}\"" \ "You are on Google search results filtered to {COMPANY}'s website for content authored by or mentioning {FULL_NAME}. Read the visible results. Extract: - Any blog posts, articles, or pages authored by {FULL_NAME} - Topics they write about at the company - Any technical decisions or opinions expressed STRICT RULES: - Do NOT click any result links - Read only titles and snippets from the search listing - Maximum 8 results then stop - If no results, return {found: false} Return JSON: {found: bool, articles: [{title, snippet, url, topic}]}" \ --sync > /tmp/smi_blog.json & # Agent 6 — Conference talks tinyfish agent run \ --url "https://www.google.com/search?q=\"{FULL_NAME_ENCODED}\"+\"{COMPANY_ENCODED}\"+(talk+OR+keynote+OR+conference+OR+speaker+OR+presentation)" \ "You are on Google search results for conference talks and presentations by {FULL_NAME} at {COMPANY}. Read the visible results. Extract any conference talks, keynotes, podcast appearances, or panel discussions they have participated in: - Talk title - Event name - Year - Topic / summary from the snippet STRICT RULES: - Do NOT click any links - Read only titles and snippets - Maximum 8 results then stop - If no results, return {found: false} Return JSON: {found: bool, talks: [{title, event, year, topic}]}" \ --sync > /tmp/smi_talks.json & # Wait for all agents to complete wait echo "=== LINKEDIN ===" && cat /tmp/smi_linkedin.json echo "=== GITHUB ===" && cat /tmp/smi_github.json echo "=== TWITTER ===" && cat /tmp/smi_twitter.json echo "=== NEWS ===" && cat /tmp/smi_news.json echo "=== BLOG ===" && cat /tmp/smi_blog.json echo "=== TALKS ===" && cat /tmp/smi_talks.json
Before running, replace:
{FULL_NAME}— e.g.Sarah Chen{FULL_NAME_ENCODED}— URL-encoded e.g.Sarah%20Chen{COMPANY}— e.g.Stripe{COMPANY_ENCODED}— URL-encoded e.g.Stripe{COMPANY_DOMAIN}— e.g.stripe.com(infer from company name for well-known companies; ask the user if unsure)
Step 3 — Synthesize the prep report
Combine all results into a structured report. Only include sections where real data was found — do not pad with guesses.
## Interviewer Research Report — {FULL_NAME}, {COMPANY} *Researched: {date}* --- ### 👤 Background **Current role:** {title} at {company} ({tenure}) **Career path:** {brief summary of career trajectory — 2-3 sentences} **Education:** {degrees and institutions} --- ### 💻 Technical Profile **Languages / Stack:** {programming languages and technologies found} **Open source:** {notable repos or contributions, if any} **What they build / have built:** {summary from GitHub and blog posts} *(Skip this section if no technical data found)* --- ### 🧠 What They Care About **Recurring themes:** {topics that appear across Twitter, blog posts, talks} **Strong opinions:** {any publicly stated views on tech, engineering culture, product, etc.} **Published work:** {blog posts, articles, talks — with topics} --- ### 🎤 Conference & Public Presence {List of talks or appearances found, with event and year} *(Skip if none found)* --- ### 💬 Suggested Conversation Starters Based on what you found, specific things you can bring up naturally: - {specific thing they worked on or wrote about} - {specific opinion or project they're known for} - {something from a talk or article that genuinely interests you} --- ### 🎯 How to Tailor Your Interview Given their background, here's what to emphasize: - {specific advice based on their career path, seniority, or technical focus} - {what signals they likely care about based on their public work} - {anything to be aware of — e.g. if they wrote critically about X, show you've thought about it} --- ### ⚠️ Gaps in Research {List any sources that returned no data or were blocked, so the user knows what's missing}
Edge Cases
- LinkedIn blocked or requires login — extract whatever is visible before the gate, note the limitation, rely more heavily on other sources
- Very common name — if search results are ambiguous, stop and ask the user for more context (company URL, LinkedIn profile link, Twitter handle) before proceeding
- No public presence found — be honest: "Limited public information found for {name} at {company}. Here's what was found: [minimal data]. You may want to ask your recruiter for more context or search on LinkedIn directly."
- Person is very senior (VP, C-suite) — news and company blog will be richest; GitHub and Twitter may be sparse
- Person is very junior — GitHub may be the richest source; news and talks likely empty
- Role is non-technical — skip the GitHub agent entirely, weight LinkedIn and blog/news more heavily
