vera-tools.org — curated Claude Code skills, AI workflow tools, and human–AI collaboration frameworks for bio/pharma R&D: data (biostatistician, bioinformatician, machine learning engineer), clinical research, pre-clinical work, and medical writing.
For the PhD who entered industry at 30+
Late, not behind.
Human–AI collaboration frameworks for late-start scientists in biotech, pharma, and clinical research. The mentor–mentee R&D system is being replaced by a mentor–AI system. Decompose your work, architect the workflow your team runs inside, stay essential. No SaaS slop, no AI hype.
"Your domain expertise is the moat. The job is to architect the workflow that puts it in front of every decision."
Who is this for?
Bio/pharma R&D, all career stages. Pick the entry point that fits — the rest of the site stays the same; the framing changes.
Four tracks. One direction.
Every tool, guide, and collection maps to a track. Start where you are.
Step 1: Identify what AI takes off your plate.
Literature scans, first-draft writing, code review, regulatory checklists — install Skills, keep your hours for what only you can do.
Step 2: Protect what only you can.
Industry transitions, ROI to non-PhD managers, visa risk, the methodological judgment AI cannot replicate yet.
Step 3: Prototype the workflows you can sell.
Scientific consulting, indie SaaS for niche bio/pharma problems, frameworks you can later license back to your day job.
Step 4: When to architect, when to leave.
Stay academic, go industry, start something, recover — make the bet deliberately, including the one about your own pace.
Built here
All plugins →First-party plugins and skills published by Vera, demonstrating the architect thesis in working code. The plugins are multi-skill bundles; the skills below are individual installable workflows for clinical trial design and indication research.
VeraSuperHub/stat-research-pipeline
Statistical Research Pipeline
Multi-skill Claude plugin for classical statistical research workflows: outcome-type detection, assumption checking, primary tests, effect sizes, extended modeling, SEM, manuscript drafting, and LaTeX assembly. 30 sub-skills covering 14 outcome / model families.
VeraSuperHub/ai-research-pipeline
AI Research Pipeline
Multi-skill Claude plugin for AI/ML research workflows: data diagnostics, baseline modeling, full-battery analysis (ML + DL), interpretability, manuscript drafting, LaTeX assembly, and external review. Domain experts bring the question and judgment; the pipeline structures the execution layer.
Pharma R&D skills
Veronica0206/vera-clinical-indication-researching
/vera-clinical-indication-researching
Systematic clinical indication research producing a structured Word/PDF dossier: MoA landscape, compound landscape, endpoint framework, and study designs. Built by Vera.
Veronica0206/vera-clinical-trial-designing
/vera-clinical-trial-designing
Sample size calculation for clinical trials with binary, continuous, or time-to-event primary endpoints. Single-arm and 1:1 controlled designs. Built by Vera.
Veronica0206/vera-master-trial-designing
/vera-master-trial-designing
Designs and simulates basket, umbrella, and platform master protocols. Public-scope baselines: no-borrowing/complete-pooling basket, MAMS umbrella, concurrent-control platform. Built by Vera.
Recent guides
All guides →The H-1B PhD's risk arsenal: layoff-proofing inside your 60-day grace period
A layoff on H-1B status gives you 60 days. Here is a structured risk management framework for the first two weeks — and the open-source tools that reduce friction at every step.
Read →MS in bioinformatics / DS → industry: the AI-augmented job-search workflow
A 90-day plan for MS graduates pivoting from research-adjacent roles into industry data science, ML engineering, or applied bioinformatics. Concrete tools, concrete sequence, no motivational filler.
Read →Your dissertation is a moat. Your job is to stop hiding it.
Most PhDs entering industry bury their research experience in two lines at the bottom of a resume. This is the wrong approach. Here's how to package dissertation depth as visible, priceable expertise.
Read →You don't need a PhD to be a moat. Here's the MS playbook.
MS holders are routinely under-priced — by themselves and the market. The 18 months you spent specializing are not a watered-down PhD. They are a different instrument. Here's how to use it.
Read →Late-start technical professionals are not behind. Here's the math.
If you finished your PhD at 31, you are not five years behind your peers. You are five years deeper into a moat they will spend the next decade trying to dig. Here's why the conventional framing is wrong — and what to do with the correct one.
Read →Recently added tools
Full directory →actual
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analytics
Open source, privacy-first web analytics. Lightweight, cookie-free Google Analytics alternative. Self-hosted or cloud.
anki
Anki is a smart spaced repetition flashcard program
Continue.dev
Open-source AI coding assistant for VS Code and JetBrains. Brings model-agnostic chat, autocomplete, and edit-with-AI to your editor — connect Claude, GPT, local models via Ollama, or any OpenAI-compatible endpoint.
Logseq
A privacy-first, local-first, open-source platform for personal knowledge management. Outliner-based, with a graph view and Markdown/Org-mode files you fully own.
activitywatch
The best free and open-source automated time tracker. Cross-platform, extensible, privacy-focused.