jxtse

jxtse / scientific-research-skills

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Skills and tools for scientific research with LLM agents

15
1
100% credibility
Found Apr 23, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A set of instructional guides for AI assistants to follow research workflows, including literature searching, paper analysis, library management, and figure generation.

How It Works

1
🔍 Discover Research Skills

You find a collection of smart guides that teach your AI helper how to assist with scientific research, like finding papers or creating diagrams.

2
📋 Explore the Guides

You browse the list of helpful skills, such as searching for studies, reading papers deeply, or organizing your library.

3
Pick Your Skills
📖
Pure Guides

Select skills like paper reading that guide your thinking without extra setup.

🔗
Connected Skills

Choose skills like paper search or figure making that link to free online helpers.

4
📁 Add to Your AI Helper

You place your chosen skill guides into the spot where your AI assistant keeps its tools, making them ready to use.

5
🔑 Link Any Helpers

For powerful skills, you quickly sign up for free services like search tools or your note organizer and connect them with simple instructions.

6
✅ Test It Out

You ask your AI to try a skill, like finding related studies, and watch it work smoothly.

🚀 Research Supercharged

Your AI now handles literature reviews, paper summaries, and even diagram creation, speeding up your discoveries effortlessly.

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AI-Generated Review

What is scientific-research-skills?

This Python repo packs agent tools and skills for LLM agents tackling scientific research workflows, like multi-engine literature searches, structured paper reading, social media paper triage, related work surveys, Zotero management, and academic figure generation. It solves the gap where AI agents lack high-level scientific research skills—turning vague prompts into systematic processes such as skimming papers, building taxonomies, or generating publication-ready figures from method descriptions. Users drop these into platforms like OpenClaw, Claude Code, or Codex via simple folder copies, then configure API keys for search engines or Zotero.

Why is it gaining traction?

Unlike tool lists or low-level scripts, it delivers methodology-first scientific research skills examples—workflows that guide agents on *when* and *why* to act, not just *how*. Modular picks mean no bloat, with cross-platform support for skills github claude code, skills github openclaw, and similar setups, plus clear post-install config walks. Developers hook on the interactive agent onboarding that checks keys and verifies setups, making skills tools examples instantly usable.

Who should use this?

PhD students or postdocs using Claude or Copilot variants for lit reviews and paper synthesis, needing skills in scientific research and writing without manual drudgery. Research engineers prototyping LLM agents for academic tasks, like surveying gaps or generating figures. Faculty teaching scientific research skills for students, who can feed skills github repo markdowns as prompts.

Verdict

Early alpha with 15 stars and 1.0% credibility score—docs are solid and installation straightforward, but expect tweaks as skills mature. Grab it if you're deep in agent-driven research; skip for production until more adoption.

(198 words)

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