| name | hackathon-finder |
| description | Find hackathons tailored to your tech profile and location by searching Devpost, Luma, Partiful, MLH, and Eventbrite. Use this skill when a user asks "find me a hackathon", "are there any hackathons near me", "what hackathons are coming up", "I want to compete in a hackathon", "find hackathons I could win", "upcoming hackathons in [city]", "online hackathons for [skill/tech]", or any request to discover hackathon opportunities. |
Hackathon Finder
Build a tech profile from everything known about you, then search Devpost, Luma, Partiful, MLH, and Eventbrite for upcoming hackathons — local or online — that match your stack and give you the best shot at winning.
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 — Build the user's tech profile
Before searching for anything, build a picture of who you're helping. Draw from everything available in the current conversation and session context:
- Languages and frameworks mentioned or used (e.g. React, Python, Rust)
- Projects described or worked on
- Areas of interest expressed (e.g. AI/ML, Web3, hardware, design)
- Skill level signals (beginner, experienced, specialist)
- Any past hackathon experience mentioned
Summarize this as a brief internal profile:
Tech profile:
- Primary languages: {e.g. Python, TypeScript}
- Frameworks / tools: {e.g. React, FastAPI, LangChain}
- Interest areas: {e.g. AI, developer tools, consumer apps}
- Skill level: {beginner / intermediate / experienced}
- Past hackathons: {yes/no, any wins?}
If very little is known, ask one quick question:
"What do you usually build with? (e.g. languages, frameworks, or areas like AI, web, mobile)"
Step 2 — Ask for location and format preference
Ask the user two things before searching:
Two quick questions:
1. **Where are you based?** (city and country — e.g. "Singapore", "London, UK", "Austin, TX")
Or if you prefer online-only hackathons, just say "online".
2. **What are you optimizing for?**
- 🏆 Best chance of winning (match your stack to the theme)
- 🌍 Biggest / most prestigious (large prize pools, well-known sponsors)
- 🤝 Best for networking / meeting people
- 🧪 Most interesting theme (something you'd genuinely enjoy building)
Wait for the user's response before proceeding.
Step 3 — Parallel hackathon search
Fire all agents simultaneously based on the user's location and tech profile.
tinyfish agent run \
--url "https://devpost.com/hackathons?challenge_type=online&status=upcoming" \
"You are on Devpost's upcoming hackathons page.
Find hackathons relevant to these technologies: {TECH_KEYWORDS}.
For each relevant hackathon extract:
- name
- theme or focus area
- prize pool (total or top prize)
- registration deadline
- hackathon dates
- online or in-person (and location if in-person)
- direct URL
- sponsor companies (if shown)
STRICT RULES:
- Do NOT click into any hackathon page
- Read only what is visible in the listing cards
- Only include hackathons with future dates
- Maximum 10 listings then stop
Return JSON array: [{name, theme, prize_pool, deadline, dates, format, location, url, sponsors: []}]" \
--sync > /tmp/hf_devpost.json &
tinyfish agent run \
--url "https://devpost.com/hackathons?challenge_type=in-person&status=upcoming&search={LOCATION_ENCODED}" \
"You are on Devpost's upcoming in-person hackathons page filtered to {LOCATION}.
Find hackathons in or near {LOCATION}.
For each extract:
- name
- theme or focus area
- prize pool
- registration deadline
- hackathon dates
- exact location (city, venue if shown)
- direct URL
- sponsor companies
STRICT RULES:
- Do NOT click into any hackathon page
- Maximum 8 listings then stop
- Only include hackathons with future dates
Return JSON array: [{name, theme, prize_pool, deadline, dates, location, url, sponsors: []}]" \
--sync > /tmp/hf_devpost_local.json &
tinyfish agent run \
--url "https://mlh.io/seasons/2026/events" \
"You are on the MLH (Major League Hacking) events page for the current season.
Find upcoming hackathons, especially any in or near {LOCATION} or online.
For each event extract:
- name
- date
- location (city or online)
- direct URL
- any theme or focus if shown
STRICT RULES:
- Do NOT click any event
- Read only the visible event cards
- Maximum 10 events then stop
- Only future events
Return JSON array: [{name, date, location, url, theme}]" \
--sync > /tmp/hf_mlh.json &
tinyfish agent run \
--url "https://lu.ma/discover?q=hackathon+{LOCATION_ENCODED}" \
"You are on Luma event discovery searching for hackathons near {LOCATION}.
Find upcoming hackathon events.
For each event extract:
- name
- date and time
- location (in-person address or online)
- host / organizer
- short description or theme
- direct URL
STRICT RULES:
- Do NOT click any event
- Read only what is visible in the event cards
- Maximum 8 events then stop
- Only future events
- If no hackathons found, return {found: false}
Return JSON array: [{name, date, location, host, description, url}]" \
-- > /tmp/hf_luma.json &
tinyfish agent run \
--url \
\
-- > /tmp/hf_devpost_tech.json &
tinyfish agent run \
--url \
\
-- > /tmp/hf_eventbrite.json &
&& /tmp/hf_devpost.json
&& /tmp/hf_devpost_local.json
&& /tmp/hf_mlh.json
&& /tmp/hf_luma.json
&& /tmp/hf_devpost_tech.json
&& /tmp/hf_eventbrite.json
Before running, replace:
{LOCATION} — e.g. Singapore, London{LOCATION_ENCODED} — URL-encoded e.g. Singapore{LOCATION_SLUG} — Eventbrite format e.g. singapore--sg{TECH_KEYWORDS} — comma-separated e.g. AI, React, Python{TECH_KEYWORD_ENCODED} — primary tech URL-encoded e.g. artificial-intelligence{PRIMARY_TECH} — single best-match technology e.g. AI/ML
Step 4 — Score and rank by fit
For each hackathon found, score it against the user's tech profile and stated goal:
Fit score (0-10):
- Theme matches user's primary tech area: +4
- Sponsor uses tech the user knows: +2
- Prize pool / prestige matches stated goal: +2
- Location matches preference: +2
Present hackathons ranked by fit score, with the win-probability assessment front and center.
Output format
## Hackathons for You — {LOCATION} · {date}
*Based on your profile: {PRIMARY_LANGUAGES} · {INTEREST_AREAS}*
*Searched: Devpost · MLH · Luma · Eventbrite*
---
### 🏆 Best Matches (Highest Win Potential)
#### {Hackathon Name}
**📅** {dates} · **📍** {location or online}
**💰** Prize: {prize_pool} · **⏰** Register by: {deadline}
**🎯 Why you'd win:** {1-2 sentences on why this fits the user's stack and the theme}
**Sponsors:** {sponsors}
🔗 {url}
---
#### {Hackathon Name}
[same structure]
---
### 🌍 Also Worth Considering
[remaining hackathons, shorter format]
- **{name}** — {date} · {location} · {prize} · {url}
---
### 📊 Summary
| Hackathon | Format | Date | Prize | Fit |
|---|---|---|---|---|
| {name} | Online/Local | {date} | {prize} | ⭐⭐⭐⭐⭐ |
---
### 💡 Strategy Tips
{1-3 targeted tips based on the user's profile and the hackathons found}
- e.g. "The {hackathon} has an AI track with no strong competition in the LLM tooling space — right in your wheelhouse"
- e.g. "For MLH events, solo Python projects with clean demos tend to score well with judges"
Edge Cases
- User is online-only — skip Devpost local, Luma local, and Eventbrite local agents; focus on Devpost online and MLH
- No hackathons found in the city — broaden to country, then to online, and note the broadening
- User has no clear tech profile — skip the fit scoring, present all hackathons by date and let the user choose
- User has won hackathons before — note this and suggest larger or more competitive events; skip beginner-friendly ones
- User is a beginner — prioritize beginner-friendly or first-timer hackathons; flag events that explicitly welcome new participants
- No upcoming hackathons found — suggest checking back closer to major seasons (September-November and January-March tend to be peak hackathon periods)
