# 10 Tips for Becoming a
Top 1% AI User

Practical AI fluency for researchers who want to ship faster
Manuel Corpas
Senior Lecturer, University of Westminster · Turing Fellow
London Bioinformatics Meetup · 26 February 2026
Welcome everyone. I'm Manuel Corpas. Tonight I want to share 10 practical techniques that separate casual AI users from the top 1% who are shipping research faster than ever. This is about AI fluency: the habits, tools, and workflows that compound your productivity. We'll start with quick wins you can adopt tomorrow morning, and end with a live demo of an open-source bioinformatics skill I built this week. TIMING: 2 min

## The 99% vs the 1%

### Most researchers

- Use ChatGPT for one-off questions
- Copy-paste into web UIs
- Treat AI as a search engine
- No memory across sessions

### Top 1%

- AI agents run overnight
- Persistent memory + RAG pipelines
- Automated research workflows
- Build tools, not just use them

Here's the gap. Most researchers interact with AI the same way they'd use Google: ask a question, get an answer, move on. The top 1% have moved past that. They have systems that remember, agents that work while they sleep, and infrastructure that compounds. TIMING: 3 min
Tip 1

## Use AI Inside Your IDE, Not a Browser

- Claude Code in your terminal: reads your codebase, edits files, runs tests
- Cursor / Windsurf: AI-native editors with inline code generation
- GitHub Copilot: autocomplete on steroids

The browser is for chatting. The IDE is for building.

Tip 1 is the lowest-hanging fruit. Stop copy-pasting code between ChatGPT and your editor. Use Claude Code in your terminal — it reads your entire codebase, understands context, edits files directly, and runs your tests. If you do one thing after tonight, install Claude Code. TIMING: 2 min

## What Is Claude Code?

An agentic coding tool that lives in your terminal — not a chat window.

- Reads your entire codebase — understands project structure, conventions, dependencies
- Edits files directly — no copy-paste; it writes, refactors, and commits for you
- Runs commands — executes tests, installs packages, runs scripts, checks output
- Multi-step reasoning — plans before coding, iterates until tests pass
- CLAUDE.md — a project file it reads every session so it already knows your rules
- Custom skills — slash commands you define (`/handoff`, `/inbox-triage`, `/meeting-prep`)

Think of it as a junior developer who knows your project, follows your standards, and never forgets context.

So what actually is Claude Code? It's Anthropic's agentic coding tool. You install it in your terminal with npm, and it becomes an AI pair-programmer that can see your entire project. It doesn't just suggest code — it reads files, edits them, runs your tests, and iterates until things work. You give it a CLAUDE.md file with your project rules, and every session it starts already understanding your architecture. You can also define custom skills — slash commands like /handoff or /inbox-triage — that trigger complex multi-step workflows. It's the difference between asking an AI a question and having an AI that actually works alongside you. TIMING: 2 min
Tip 2

## Build Persistent Memory

    # MEMORY.md — the AI accumulates this across sessions

    ## Lecture Efficiency Rules (learned from strategy v7.2)
    - New lecture: 2 hours max. Repeat lecture: 30 min update only
    - Reuse first — always check slide bank before creating new
    - 60% of slides should be reusable across contexts

    ## Slide Style Preferences (stable pattern)
    - Concept slides: max 30 words. Method slides: max 40 words
    - Structure: title → outcomes → concepts → case study → summary
    - Tone: scientific and rigorous — equity as quality, not just ethics

    ## HEIM Integration Rules (auto-include when relevant)
    - Include in any lecture on GWAS, biobanks, AI in health
    - Placement: early (after intro) or as implications section
    - Depth: 2-3 slides for awareness; more for equity-focused lectures

    ## Reusable Slide Modules (built over time)
    - Genomic data formats: 5 slides (FASTA, VCF, BAM...)
    - GWAS fundamentals: 8 slides (design, stats, Manhattan plots)
    - ML in genomics: 6 slides (algorithms, applications, bias)

These are real entries. Lecture time budgets, slide style rules, content modules — all accumulated automatically across sessions.

Every session ends with `/handoff`. Decisions, preferences, and lessons feed back in. The next session already knows how I teach. The system gets sharper every day.

Tip 2: what you're looking at is my actual memory file for lecture creation. The AI knows my time budget — 2 hours max for a new lecture, 30 minutes for a repeat. It knows my slide style — max 30 words per concept slide, scientific tone. It knows when to include equity content automatically. It has a bank of reusable slide modules it built up over time — GWAS fundamentals, genomic formats, ML intro. None of this was a one-off prompt. It accumulated session by session. Every time I prepare a lecture, the system learns what worked and feeds it back. After a semester, it knows how I teach — my style, my rules, my content library — without me having to repeat anything. That's persistent memory. TIMING: 2.5 min
Tip 3

## Feed It Your Context

- A generic AI gives generic answers. Give it your context and it becomes yours.
- Upload your notes, papers, meeting transcripts, protocols — anything relevant
- The more specific the context, the more useful the output

Notes
Emails
Papers
Transcripts
→ embed
Vector Database
semantic search
across everything
→ retrieve
LLM
answers grounded
in your data
→ output
Answer
specific to you,
with sources

RAG = Retrieval-Augmented Generation — the AI searches your own documents before answering

Tip 3: the single biggest upgrade you can make. A generic AI gives you generic answers. Feed it YOUR context — your papers, your notes, your protocols — and it becomes dramatically more useful. Without context, asking for a lecture on genomic equity gives you a Wikipedia summary. With context — your own HEIM paper, your previous slides — it gives you something that sounds like you and builds on your actual work. You can do this by simply pasting text into the chat, attaching files, or for larger collections, building a RAG pipeline. The key insight: AI is only as good as the context you give it. TIMING: 2 min
Tip 4

## Use Voice, Not Just Text

- Whisper (local, Apple Silicon): transcribe meetings, lectures, ideas
- Voice memos → structured notes: talk for 5 min, get formatted output
- Dictation for prompts: faster than typing, more natural phrasing

Your brain outputs ideas faster as speech. Let AI handle the formatting.

Tip 4: voice. I run Whisper locally on Apple Silicon. I record a 5-minute voice memo with my thoughts, transcribe it, and feed it to Claude for structuring. This is how I draft papers, plan projects, and capture ideas on walks. Your brain is faster at speaking than typing. Use that. TIMING: 1.5 min
Tip 5

## Automate Your Daily Intelligence

- arXiv radar: daily paper ranking by relevance to your research
- Podcast extraction: transcribe + summarise AI/science podcasts automatically
- X/Twitter digest: summarise what key AI voices are saying today

    # Every morning at 06:30, my bot sends me this:
    📰 Top 3 Papers Today:
    1. "Ancestry-aware PRS improves prediction in..." (Score: 9.2)
    2. "Foundation models for single-cell..." (Score: 8.7)
    3. "Equitable genomic data sharing..." (Score: 8.4)

Tip 5: automate your information diet. I have scheduled jobs that run at 6:30am: arXiv ranking, podcast extraction, Twitter digest. By the time I open my laptop, my AI bot has already summarised the top papers, podcasts, and what key AI influencers are talking about. This saves me an hour every day. TIMING: 2 min
Tip 6

## Deploy Persistent AI Agents

- RoboTerri (Telegram): 15 commands, 13 tools, 8 scheduled job groups
- RoboIsaac (WhatsApp): analytical critique partner (Newton persona)
- Both share a memory bridge: same ChromaDB, same knowledge

These aren't chatbots. They're research assistants that run 24/7.

Tip 6: build persistent agents. I have two: RoboTerri on Telegram handles daily operations — paper summaries, podcast publishing, email drafts, strategy reviews. RoboIsaac on WhatsApp plays devil's advocate on my research plans. They share the same memory bridge. They never forget a conversation. TIMING: 2 min

## Meet RoboTerri

Prof. Teresa K. Attwood

Pioneer in bioinformatics
University of Manchester
Founding Chair of GOBLET
Manuel Corpas' PhD supervisor

From RoboTerri's `SOUL.md`:

    ## Identity
    RoboTerri is an AI agent inspired by Professor
    Teresa K. Attwood — her communication style,
    expertise, and collaborative approach.

    ## Voice Rules
    Keep it short and direct. British spellings.
    Sign-offs: "T." (default), "Tx" (casual).
    Characteristic phrases: "Indeed", "Great stuff!"
    Emoticons: ;-) (humour), ;-))) (strong amusement)

    ## Operational Lane
    Primary job: gather evidence, validate claims,
    surface contradictions, identify information gaps.
    Default to evidence retrieval, not coaching.

This is the real person behind RoboTerri. Professor Teresa Attwood — Terri — is a pioneer of bioinformatics at Manchester, founding Chair of GOBLET, and my mentor and collaborator for over 20 years. RoboTerri is an AI agent inspired by her communication style and collaborative approach. The SOUL.md file you see here is the identity prompt that shapes every response. The agent knows to gather evidence and validate claims, not to give motivational coaching. That's Terri's style: rigorous, supportive, direct. TIMING: 1.5 min
Tip 7

## Let AI Compound Your Outputs

One research insight → 5 outputs. You think once. AI reformats everywhere.

Research
Insight
"Your 23andMe says
you're a CYP2D6
poor metaboliser"
→
Drug report
10 drugs to avoid
`ClawBio`
Blog post
"What I learned from
my DNA about drugs"
`/substack`
Tweet thread
"Did you know your
genes affect codeine?"
`/tweets`
Podcast episode
Pharmacogenomics
explained in 10 min
`/playbook`
Lecture slides
CYP2D6 case study
for students
`/lecture`

All via RoboTerri on Telegram. One insight in, five formats out.

Tip 7: compound your outputs. Say you run your 23andMe data through ClawBio and discover you're a CYP2D6 poor metaboliser — codeine won't work for you, 10 drugs flagged. That one insight becomes a drug report, a blog post explaining what you learned, a tweet thread, a podcast episode on pharmacogenomics, and lecture slides with a CYP2D6 case study. One insight, five formats. The AI handles the reformatting. You handle the thinking once. TIMING: 1.5 min. "Now we get to the real stuff."

## Live Demo: Compound Output

In Telegram, I send RoboTerri one message:

    Write me a tweet thread explaining why everyone
    should check if they're a CYP2D6 poor metaboliser
    before taking codeine

RoboTerri replies in Terri's voice — with a ready-to-post thread.
Same insight → `/substack` for a blog post → `/playbook` for a podcast.
One question. Multiple formats. Zero reformatting.

This is the live demo for Tip 7. Open Telegram, type this exact message to RoboTerri. It will respond in Terri's style with a tweet thread about CYP2D6 and codeine. Point out: same pharmacogenomics insight from the ClawBio demo, now repurposed as social media content. Then mention: /substack turns it into a blog post, /playbook turns it into a podcast episode. One insight, many formats, all from Telegram. TIMING: 1 min (live demo)

## Tips 1-7 are about using AI.

## Tips 8-10 are about building with it.

This is where it gets interesting for bioinformaticians.

Transition slide. Pause here. "Everything so far is about using AI tools that already exist. Now I want to talk about building. Because the bioinformatics community has specific needs that general AI tools don't address." TIMING: 30 sec
Tip 8

## Why Not Just Use ChatGPT?

Ask Claude to "profile my pharmacogenes from this 23andMe file."

It will try. It'll write plausible-looking Python. But:

- It hallucinates star allele calls and uses outdated CPIC guidelines
- It forgets CYP2D6 *4 is no-function (not reduced)
- You spend 45 minutes debugging its output
- No reproducibility bundle. No audit log. No checksums.

Announcing: 🦖 ClawBio

A domain expert's judgement, frozen into code that an AI agent executes correctly every time.

"Now, some of you are thinking: can't I just ask ChatGPT to do this? You can try. Ask it to profile pharmacogenes from a 23andMe file. It'll write plausible Python. But it'll hallucinate star allele calls. It'll use outdated CPIC guidelines. It won't know that CYP2D6 *4 is no-function, not just reduced. You'll spend 45 minutes debugging output that looks right but isn't. ClawBio encodes the correct bioinformatics decisions so the agent gets it right first time, every time. The skill is a domain expert's judgement, frozen into code. Let me show you what that looks like." TIMING: 3 min

## 🦖 What Is ClawBio?

- The first bioinformatics-native skill library for AI coding agents
- Built on OpenClaw — 180,000+ GitHub stars, the fastest-growing open-source AI project
- A skill = a domain expert's knowledge frozen into code that an agent executes correctly every time
- Local-first: your genomic data never leaves your laptop
- MIT licensed: open-source, free, community-driven

Think of it this way:

    ChatGPT/Claude = a smart generalist who guesses at bioinformatics
    🦖 ClawBio skill = a domain expert's proven pipeline that the AI executes

Not a web app. Not a SaaS. Expert skills that run on your machine.

"So what exactly is ClawBio? It's the first bioinformatics-native skill library for AI coding agents. It's built on OpenClaw — which has over 180,000 stars on GitHub and is the fastest-growing open-source AI project right now. Here's the key concept: a skill is a domain expert's judgement, frozen into code. When an AI agent needs to do pharmacogenomics or ancestry analysis, instead of hallucinating an answer, it loads the ClawBio skill and executes it correctly every time. The difference: ChatGPT guesses at bioinformatics. A ClawBio skill is a proven pipeline that the AI executes. Everything runs locally — your genomic data never leaves your laptop. MIT licensed, fully open-source." TIMING: 2 min

## 🦖 ClawBio: How It Works

You ask: "What drugs should I worry about from my 23andMe?"
↓
CLAUDE.md reads the routing table → matches intent
↓
PharmGx
12 genes
51 drugs
Equity
Scorer
NutriGx
Advisor
Ancestry
PCA
Lit
Synth
+6
more
↓
Report + figures + checksums + reproducibility bundle

From ClawBio's `CLAUDE.md`:

    ## Skill Routing Table

    | User Intent         | Skill              |
    |---------------------|--------------------|
    | Drug interactions,  | pharmgx-reporter/  |
    | 23andMe, CYP2D6     |                    |
    | Genomic diversity,  | equity-scorer/     |
    | HEIM, FST            |                    |
    | Nutrition, MTHFR,   | nutrigx_advisor/   |
    | diet genetics        |                    |
    | Ancestry, PCA       | claw-ancestry-pca/ |

    ## Safety Rules
    - Genetic data never leaves this machine
    - Always include disclaimer
    - Use SKILL.md methodology only —
      never hallucinate bioinformatics

Here's how ClawBio actually works. On the left: you ask a question in plain English. CLAUDE.md has a routing table that matches your intent to the right skill. The highlighted skill runs — in this case PharmGx Reporter — and produces a report with figures, checksums, and a reproducibility bundle. On the right: that's the actual CLAUDE.md file. It maps natural language intents to skill directories. It also has safety rules — genetic data never leaves your machine, and the agent is forbidden from hallucinating bioinformatics decisions. It can only use what's in the SKILL.md. TIMING: 2 min

## 🦖 Skills — 5 MVP, 7 Planned

- claw-pharmgx — 12 genes, 51 drugs, CPIC [MVP]
- claw-ancestry-pca — PCA vs SGDP (164 pops) [MVP]
- claw-semantic-sim — PubMedBERT disease isolation [MVP]
- claw-orchestrator — routing + reporting [MVP]
- claw-nutrigx — 40 SNPs, 16 genes, nutrition [MVP]
- claw-equity-scorer — HEIM diversity index

- claw-vcf-annotator — VEP + ClinVar + gnomAD
- claw-lit-synth — PubMed + LLM summaries
- claw-scrna — Scanpy automation
- claw-struct-predict — AlphaFold/Boltz local
- claw-repro — Conda/Nextflow export

Each skill = a `SKILL.md` + Python scripts. Composable. Local-first. Auditable.

Dr David de Lorenzo

First community contribution

NutriGx Advisor
40 SNPs, 16 genes
personalised nutrition

Contributed within 24 hours
of ClawBio going public

Here's the current roadmap. Five skills are at MVP — and that fifth one is special. David de Lorenzo submitted the NutriGx Advisor skill within 24 hours of ClawBio going public on GitHub. It covers 40 SNPs across 16 genes for personalised nutrition — folate metabolism, vitamin D, caffeine, lactose, omega-3. That's the power of open-source skills: a domain expert freezes their knowledge into code, and the whole community benefits. TIMING: 1.5 min

## NutriGx Advisor — Demo by David de Lorenzo

Your browser does not support the video tag.

First community skill — contributed within 24 hours of ClawBio going public. 40 SNPs across 16 genes for personalised nutrition.

Play the video. David recorded this demo of the NutriGx Advisor skill he contributed to ClawBio. It shows the skill running on synthetic patient data, generating a personalised nutrition report from 23andMe-style genetic data. Let the video speak for itself — pause or narrate over it as needed. TIMING: 1-2 min (video length)
Tip 9

## Build Modular Skills

### Live Demo: Creating a new ClawBio skill

In Claude Code, I type:

    I want to build a GWAS quality control skill for ClawBio.
    Walk me through how to create it.

Claude Code reads `CLAUDE.md` + `CONTRIBUTING.md` + `SKILL-TEMPLATE.md`

1. Copy
template
→
2. Write
SKILL.md
→
3. Add
Python
→
4. Test +
demo data
→
5. PR to
ClawBio

The AI already knows the conventions. It scaffolds the skill for you.

"Let me show you how easy it is to build a new skill. I open Claude Code in the ClawBio directory and ask: I want to build a GWAS QC skill. Claude reads the CLAUDE.md, the CONTRIBUTING guide, and the skill template. It walks me through five steps: copy the template, write the SKILL.md with your domain methodology, add the Python implementation, create test data, and submit a PR. David de Lorenzo did exactly this — he contributed the NutriGx Advisor in under 24 hours. The AI scaffolds everything; the domain expert provides the judgement." TIMING: 2 min. Can do live in terminal if time permits.

## The Reproducibility Crisis, In Practice

### Today: Checking a Paper's Code

- Read paper. Want to verify Figure 3.
- Go to GitHub. Clone the repo.
- Wrong Python version. Fix dependencies.
- Need the reference data. Where is it?
- Download 2GB from Zenodo. Link is dead.
- Email first author. Wait 3 weeks.
- Paths: `/home/jsmith/data/`
- 2 days later: still broken.

### With ClawBio: Same Paper

- Read paper. Want to verify the drug report.
- `git clone ClawBio && cd ClawBio`
- Ask: "Run the pharmgx demo"
- CLAUDE.md routes to the right skill.
- Report appears. Identical.
- SHA-256 checksum: verified.
- Demo data + dependencies: bundled.
- 30 seconds. Done.

Every figure in your paper should be one command away from reproduction.

"How many of you have tried to reproduce a figure from a paper using the authors' GitHub code? You clone the repo, wrong Python version, dependencies broken, Zenodo link dead, paths hardcoded. Two days later: still broken. Now imagine: install the skill, run one command, Figure 3 appears, checksums verified. 30 seconds. That's what reproducibility should look like." TIMING: 2 min
Tip 10

## Publish Skills, Not Just Papers

- claw-pharmgx took a few hours to build. 12 genes, 51 drugs, CPIC guidelines.
- claw-nutrigx was contributed by David de Lorenzo in under 24 hours. 40 SNPs, 16 genes.
- 5 MVP skills live. Every skill ships with checksums and a reproducibility bundle.
- CLAUDE.md routes users to the right skill — no documentation needed.

Skills we need from you:

- claw-gwas — PLINK/REGENIE automation
- claw-metagenomics — Kraken2/MetaPhlAn wrapper
- claw-acmg — Clinical variant classification
- claw-pathway — GO/KEGG enrichment

"Tip 10: publish skills, not just papers. claw-pharmgx took a few hours to build. David de Lorenzo contributed claw-nutrigx in under 24 hours after ClawBio went public. The template is there, Claude Code scaffolds the structure, and CLAUDE.md means users can find your skill just by asking a question in plain English. Think about your own work. That GWAS pipeline you run every time? Make it a skill. That metagenomics classifier? Wrap it. That clinical variant interpretation workflow? A few hours of work and the whole community benefits. Imagine every bioinformatics paper shipping with a ClawBio skill. You read the paper, ask a question, get the exact results. That's the world we're building." TIMING: 2.5 min

# Some Common Pitfalls

What to watch out for when AI writes your research
Transition slide. "So we've covered 10 tips for using AI effectively. But before we wrap up, let me flag three common pitfalls — things that go wrong when you trust AI output without verification. These are especially dangerous in bioinformatics." TIMING: 15 sec

## AI Pitfall: Numbers That Sound Right

The problem

LLMs generate numbers that feel appropriate.

They do NOT generate numbers that match your data.

What goes wrong:

"We analysed 847 samples" — plausible, but yours was 623

"p = 3.2 × 10⁻⁸" — looks right, entirely invented

"73% of variants were missense" — confident and wrong

🔢

Verify EVERY number

against your source files,
code output, or analysis logs

"Here's the first AI pitfall that matters for bioinformatics: numbers. LLMs are remarkably good at generating numbers that feel right. Sample sizes that sound reasonable, p-values in the right ballpark, percentages that pass the smell test. But they're invented. If you ask an LLM to write your results section, it will confidently give you numbers that look publishable but don't match your actual data. In bioinformatics, where every paper is full of statistics, this is dangerous. Rule: verify every single number against your source data." TIMING: 1.5 min

## AI Pitfall: Claims That Grow

The problem

LLMs write persuasively. They naturally reach for broader, more impressive claims.

What you found:

"CYP2D6 poor metabolisers showed altered codeine response in our cohort"

What the LLM writes:

"This proves that CYP2D6 testing should be mandatory before prescribing any opioid"

📈

For each conclusion ask:

"Did we actually
measure this?"

"Second pitfall: claim expansion. LLMs are trained to write persuasively. They naturally escalate findings. 'We observed X' becomes 'This proves Y'. 'Associated with' becomes 'causes'. In bioinformatics, the gap between correlation and causation is everything. Your reviewers will catch this and your credibility takes a hit. Simple test: for every conclusion, ask yourself — did we actually measure this? If you're hedging mentally, the text needs hedging too." TIMING: 1.5 min

## AI Pitfall: "We Used X" (You Didn't)

You ask AI to write your Methods section.
It describes what it thinks you did — not what you actually did.

AI wrote:

"We used Tool A with default settings"

You actually used:

Tool B with custom parameters

Nobody can reproduce your work if the
methods section describes a different workflow.

🔬

Check every step
against your code

ClawBio solves this:
skills embed the actual
methodology in SKILL.md

"Third pitfall: the AI writes a methods section that sounds great but describes a different workflow to the one you actually ran. It guesses which tools you used, assumes default parameters, invents steps. If your methods section describes the wrong procedure, nobody can reproduce your work. That's why in ClawBio, every skill has a SKILL.md that documents the actual methodology — the AI reads that instead of guessing." TIMING: 1.5 min

## The Golden Rule

AI drafts. You verify.

If you can't explain it, don't submit it.

Your name is on the work.

"This is the golden rule of AI-assisted research. The AI drafts, you verify. If you can't explain every number, every method, every conclusion in your paper — don't submit it. Your name is on the work. Not ChatGPT's. Not Claude's. Yours. Use AI to be more productive, but never outsource your scientific judgement." TIMING: 1 min

## 10 Tips Recap

- 1. Use AI inside your IDE, not a browser
- 2. Build persistent memory
- 3. Feed it your context
- 4. Use voice, not just text
- 5. Automate your daily intelligence

- 6. Deploy persistent AI agents
- 7. Let AI compound your outputs
- 8. Why not just use ChatGPT?
- 9. Build modular skills
- 10. Publish skills, not just papers

The top 1% build systems. Everyone else uses tools.

Quick recap. Tips 1-7: use AI effectively — IDE, prompts, memory, voice, automation, agents, compounding. Tips 8-10: build with AI — open-source, modular skills, shipping infrastructure. The throughline: the top 1% build systems that compound. Everyone else uses tools one at a time. TIMING: 1 min

# Thank You

Questions welcome — and at the pub after

GitHub: github.com/ClawBio/ClawBio

Tutorial: Install RoboTerri (Telegram agent)

LinkedIn: linkedin.com/in/manuelcorpas

X: @manuelcorpas

🦖 Slides: github.com/ClawBio/ClawBio/slides

Thank you. I'll be at the pub after — come talk to me if you want to build a skill, have ideas for the library, or just want to chat about AI agents in research. Questions? TIMING: remainder