stalk-my-interviewer

name

stalk-my-interviewer

description

Research an interviewer online before a meeting using parallel TinyFish agents and return a structured prep report. Use this skill when a user says "research my interviewer", "I have an interview with [name] at [company]", "stalk my interviewer", "find out about [person] before my interview", "who is my interviewer", "prepare for interview with [name]", "look up my interviewer", or any request to learn about a specific person before meeting them professionally.

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?

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:

• 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.

# 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}" \
  -- > /tmp/smi_twitter.json &


tinyfish agent run \
  --url  \
   \
  -- > /tmp/smi_news.json &


tinyfish agent run \
  --url  \
   \
  -- > /tmp/smi_blog.json &


tinyfish agent run \
  --url  \
   \
  -- > /tmp/smi_talks.json &




  &&  /tmp/smi_linkedin.json
  &&  /tmp/smi_github.json
  &&  /tmp/smi_twitter.json
  &&  /tmp/smi_news.json
  &&  /tmp/smi_blog.json
  &&  /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