tinyfish-io/tinyfish-cookbook

stalk-my-interviewer

Research an interviewer online before a meeting using parallel TinyFish agents and return a structured prep report.

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How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/tinyfish-io/tinyfish-cookbook --skill stalk-my-interviewer

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

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?

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

2. Authenticated?

bash
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:

  • 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

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