| 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.
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 &
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 &
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
