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Final review fixes: Quick Path to top, decision tree, citations
- Moved Quick Path above Objectives (most actionable element first)
- Replaced generic Objectives with persona-specific value propositions
- Replaced "Not sure which track?" with 6-line decision tree
- Added citation to 26% reproducibility stat (Garijo et al. 2013)
- Reframed Databricks slot as "Scaling genomics" (neutral)
- Consolidated comms to Discord as primary channel
- Removed duplicate Quick Path section
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Copy file name to clipboardExpand all lines: docs/hackathon/index.md
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@@ -41,16 +41,28 @@ A skill is not a wrapper around someone else's code. It encodes domain judgement
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---
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## Objectives
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## Quick Path
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By the end of this hackathon, you will have:
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| Step | Time | What |
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|------|------|------|
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| 1. [Setup](setup.md)| 10 min | Clone repo, install dependencies, run a demo |
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| 2. [Your First Skill](first-skill.md)| 20 min | Scaffold a skill, write SKILL.md, add demo data |
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| 3. [Add Python](add-python.md)| 20 min | Implement the skill logic with a CLI endpoint |
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| 4. [Test and Submit](submit.md)| 10 min | Validate, test, open a PR |
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1.**Built a working bioinformatics skill** with a SKILL.md contract, demo data, and a runnable endpoint
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2.**Experienced agentic development first-hand** using coding assistants and AI agents to accelerate your work
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3.**Contributed to an open-source project** with a pull request to the ClawBio repository
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4.**Connected with a cross-disciplinary community** spanning AI engineering, genomics, proteomics, clinical diagnostics, and epidemiology across Imperial, KCL, the Crick, UCL, and industry
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These timings assume your environment is already set up. First-time Git or GitHub users should allow extra time; helpers will be available throughout.
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---
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Whether you are an AI engineer new to genomics or a genomics researcher new to agentic tools, you will leave with a concrete open-source artefact: a working skill, a documented scientific contract, and a pull request.
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## What You'll Walk Away With
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-**If you are a genomics researcher**: your existing pipeline wrapped as a one-command, reproducible tool that any AI agent or colleague can run without calling you.
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-**If you are an AI engineer**: a working bioinformatics skill built on real public APIs, with domain decisions you can point to as evidence you understand the biology.
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-**If you work in proteomics**: the first proteomics skill in ClawBio. Pioneer status.
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-**If you work in clinical diagnostics**: a prototype skill with explicit safety rules and CPIC evidence levels, demonstrating how agentic tools can handle sensitive domain logic.
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-**If you work in epidemiology or public health**: a population-level analysis tool with visual output that non-specialists can act on.
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Everyone leaves with a concrete open-source artefact: a working skill, a documented scientific contract, and a pull request.
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|**12:00**|**Doors open**| Arrive, get set up, connect to WiFi |
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|**12:30**|**Welcome**| Nathan Skene (Imperial) and Manuel Corpas (Westminster) |
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|**12:45**|**[Overview of the day](presentation/)**| Themes, tracks, how submissions work |
|**13:30**|**Pizza**| Lunch break, keep hacking if you want |
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|**14:00**|**Guided Tutorial (Jay Moore)**| Hands-on with helpers. Follow the [Agentic Tools tutorial](agentic-tools.md) and the [ClawBio Setup](setup.md)|
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|**14:30**|**Build**| Work on your skill (solo or in teams). Helpers circulate. |
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## Quick Path
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| Step | Time | What |
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|------|------|------|
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| 1. [Setup](setup.md)| 10 min | Clone repo, install dependencies, run a demo |
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| 2. [Your First Skill](first-skill.md)| 20 min | Scaffold a skill, write SKILL.md, add demo data |
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| 3. [Add Python](add-python.md)| 20 min | Implement the skill logic with a CLI endpoint |
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| 4. [Test and Submit](submit.md)| 10 min | Validate, test, open a PR |
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These timings assume your environment is already set up. First-time Git or GitHub users should allow extra time; helpers will be available throughout.
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Also see the [Presentation](presentation/) for the full-screen slide deck.
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---
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## Choose Your Track
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We have attendees ranging from AI agent engineers with no genomics background to researchers with 40+ years in computational biology. Pick the track that fits you. Each project is labelled as a **90-minute build** (achievable in the afternoon) or a **stretch build** (ambitious, likely a prototype).
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## Not Sure Which Track?
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Start with [Setup](setup.md) and run a few demos. See what clicks. Then pick a skill idea, or invent your own. The best skills come from scratching your own itch: what analysis do you do repeatedly that could be a one-command tool?
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- You have never written a bioinformatics pipeline: **Track A**
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- You work with VCF files or NGS data daily: **Track B**
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- Your primary data is protein or mass spec: **Track C**
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- You interpret genetic variants in a clinical context: **Track D**
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- Your work involves populations, surveillance, or disease burden: **Track E**
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- None of the above: **pick any 90-minute build** from any track, or invent your own
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If you want to team up, find someone from a different track. An AI engineer paired with a genomics researcher is a powerful combination.
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The best skills come from scratching your own itch: what analysis do you do repeatedly that could be a one-command tool? If you want to team up, find someone from a different track. An AI engineer paired with a genomics researcher is a powerful combination.
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## Communication
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-**On the day**: WhatsApp group (link shared at the venue) and Discord with RoboTerri
<divclass="card-body">Only <strongstyle="color:var(--text);">1 in 4</strong> computational biology papers can be reproduced without emailing the authors. The rest? Broken links, wrong Python versions, hardcoded paths.</div>
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<divclass="card-body">Only <strongstyle="color:var(--text);">1 in 4</strong> computational biology papers can be reproduced without emailing the authors. The rest? Broken links, wrong Python versions, hardcoded paths.<br><spanstyle="font-size:0.7rem;color:var(--muted);margin-top:0.3rem;display:block;">Garijo et al., PLOS ONE 2013; Collberg & Proebsting 2016</span></div>
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