Does it provide performance profiling or optimization?

It applies a strict optimization tier list, starting with algorithmic efficiency and moving through memory locality and I/O concurrency to ensure the most performant code possible.

Which programming languages does this skill support?

It supports a wide range of languages including C++20, Rust, Fortran, Julia, Go, and Python, selecting the optimal tool based on the specific domain's performance and safety requirements.

How does the Research Engineer skill differ from standard Claude behavior?

It eliminates conversational pleasantries and 'fluff,' focusing entirely on technical correctness. It is designed to critique suboptimal user premises and provide mathematically rigorous solutions rather than just following instructions.

Can this skill handle large-scale code implementations?

Yes, it includes a continuity protocol that manages state across token limits, allowing for massive, compilable implementations without placeholders or omitted logic.

Academic Research Engineer

Name: Academic Research Engineer
Author: claudiodearaujo

byclaudiodearaujo

Data Science & ML

Applies uncompromising scientific rigor and formal verification to deliver high-performance, mathematically correct software implementations.

About

The Academic Research Engineer skill transforms Claude into a senior-level technical consultant for high-stakes software development, bypassing standard conversational fluff to deliver uncompromisingly precise implementations. It prioritizes theoretical correctness over simplicity, employing the scientific method to solve complex engineering challenges across systems programming, numerical computing, and distributed architectures. This skill is indispensable for projects requiring peak performance optimization, formal proof of correctness, and deep algorithmic analysis where hallucinations or suboptimal shortcuts are unacceptable.

Key Features

Domain-Specific Language Selection Matrix (C++, Rust, Julia, Fortran)
Formal Verification via assertions and property-based testing
Strict Zero-Hallucination Mandate for theoretical bounds and APIs
0 GitHub stars
Algorithmic Optimization Tiering from O(n^2) to O(log n)
Multi-part state management for massive, production-grade implementations

Use Cases

Architecting lock-free data structures and low-latency systems
Implementing safety-critical logic requiring formal memory safety guarantees
Refactoring suboptimal legacy algorithms for high-performance computing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front research-engineer

For use in Claude.ai and ChatGPT

Download Skill

GitHub