TinyFish Cookbook is a collection of example applications, recipes, and automations built with TinyFish, a web service that lets AI agents search the live web, read pages, and perform browser-based tasks. Developers use it to learn how to build web agents and connect them with workflows such as research, monitoring, and data collection. The catalogue entries provide agent skills and integrations for working with these examples and TinyFish endpoints.
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 skills add tinyfish-io/tinyfish-cookbook --skill stalk-my-interviewergit clone --depth 1 https://github.com/tinyfish-io/tinyfish-cookbookWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tinyfish-io/tinyfish-cookbook/stalk-my-interviewer)<a href="https://agentmods.dev/skills/tinyfish-io/tinyfish-cookbook/stalk-my-interviewer"><img src="https://agentmods.dev/badge/skills/tinyfish-io/tinyfish-cookbook/stalk-my-interviewer.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00101 | $0.02627 |
| Opus 5 | $0.00051 | $0.01314 |
| Sonnet 5 | $0.00020 | $0.00525 |
| Haiku 4.5 | $0.00010 | $0.00263 |
Grade A, and why
stalk-my-interviewer 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 8d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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}" \
--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
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.
- 8d ago First seen · 256 lines · 101 tokens per session scan A 3449650ec580
stalk-my-interviewer is a skill published in the GitHub repository tinyfish-io/tinyfish-cookbook (2,153 stars, last pushed 7d ago), licensed MIT. It adds 101 tokens to every session and 2,627 once invoked, about $0.0005 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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