awesome-research-skills

Awesome Research Skills

The research workflow every AI agent should have.

A growing open-source Skill stack for the full research lifecycle—from discovering and understanding papers to writing, reviewing, polishing, and presenting research.

Available today: research writing, faithful academic polishing, academic de-templating, and paper-to-slides. More stages are being built. Across the workflow, Agents should show their sources, checks, uncertainty, and changes instead of silently rewriting the research.

Built and maintained by Yila.ai, a research workspace for literature, evidence, data, writing, and presentation workflows.

Designed for Codex, Claude Code, WorkBuddy-style research agents, and other systems that support reusable Skill instructions.

Start from your task

Install in 30 seconds

Node.js 18 or later is required for the installer. Install the complete currently available stack:

npx skills add Yila-AI/awesome-research-skills --global --agent codex --skill '*' --yes --copy

Or install only the capability you need today:

# Plan, draft, revise, or audit a paper
npx skills add Yila-AI/awesome-research-skills --global --agent codex --skill science-research-writing --yes --copy

# Translate or polish an existing manuscript
npx skills add Yila-AI/awesome-research-skills --global --agent codex --skill sci-ssci-polishing --yes --copy

# Remove academic AI-like patterns while preserving the science
npx skills add Yila-AI/awesome-research-skills --global --agent codex --skill academic-humanizer --yes --copy

# Turn a paper or research results into an academic presentation
npx skills add Yila-AI/awesome-research-skills --global --agent codex --skill research-presentation --yes --copy

List all installable Skills with npx skills add Yila-AI/awesome-research-skills --list.

Use only one Skill?

The commands above copy only the selected runtime Skill into your Agent's Skills directory. You do not need to clone the complete repository or point an Agent at the repository root. Versioned, use-only ZIP archives for production-ready Skills are published on the Releases page.

The full research lifecycle

The project is growing toward one connected workflow rather than a folder of unrelated prompts:

QUESTION → DISCOVER → READ → SYNTHESIZE → DESIGN → ANALYZE → WRITE → REVIEW → POLISH → COMMUNICATE

The product direction is a single $research entry point that can understand the current stage and route work to the right specialist module. The current release exposes each available Skill directly while that unified workflow is being built.

The modules will share research context—questions, sources, evidence, author decisions, claims, data, and revision history—so that evidence found during search can survive all the way into a manuscript, review, or presentation.

Use the complete workflow on Yila.ai

These open-source Skills come from the research workflows being built at Yila.ai. While more stages are packaged as reusable Skills, Yila.ai already provides an integrated workspace for literature search, paper reading, evidence synthesis, research design, data analysis, writing, figures, posters, and slides.

Start a research task on Yila.ai →

Available today

The current release provides these connected capabilities:

The Academic Humanizer is an independent Yila.ai implementation informed in part by AIScientists-Dev/academic-humanizer (MIT). It is not affiliated with or endorsed by the upstream project. See the third-party notice.

In plain language: the writing Skills help build and refine the paper; research-presentation carries the evidence into a talk without flattening the science. Scientific judgment remains with the author.

Building a research Agent?

You can reuse the repository's Evidence-Preserving Draft Contract, Claim-Strength Contract, and Target-Journal Model Builder in your own project.

Reuse the mechanisms · Cite this repository · Add your project

Start with one sentence

Use $science-research-writing to help me write my paper.
Here are the materials I currently have: [attach files or paste text]

The Skill reads what you have, identifies whether the next useful result is a plan, draft, revision, or audit, and proceeds without requiring a long intake prompt or an internal mode selection.

Getting started · Use cases · Copyable inputs · Output guide

For paper-to-slides work, start with the Research Presentation quick start and use cases.

Copy-paste examples

From materials to a paper structure

Use $science-research-writing.
I have finished my study, but I do not know how to organize it into a paper.

Research question:
[paste your research question]

Methods:
[paste what you did]

Main results:
[paste key findings, tables, or notes]

Target journal or field:
[paste if available]

Polish without changing the science

Use $sci-ssci-polishing.
Please polish this Discussion paragraph for academic clarity.
Do not change numbers, citations, terminology, limitations, or claim strength.
If any sentence sounds unsupported or overclaimed, flag it instead of fixing it silently.

Text:
[paste paragraph]

Remove the AI template voice

Use $academic-humanizer.
Make this academic passage sound less templated and more natural.
Do not change any claim, number, citation, limitation, or uncertainty.
Do not optimize for an AI detector; report the patterns you changed.

Text:
[paste paragraph]

Before — fluent but generic:

In recent years, graph neural networks have attracted increasing attention. Importantly, our novel Model-X leverages a multi-scale encoder to address this crucial challenge. Extensive experiments demonstrate that Model-X improves macro-F1 by 4.7% over Baseline-B on three datasets (Smith et al., 2024), thereby proving its universal superiority.

After — specific and evidence-bounded:

Existing graph neural networks lose long-range dependencies. Model-X uses a multi-scale encoder to address this problem. Across three datasets, Model-X improves macro-F1 by 4.7% over Baseline-B (Smith et al., 2024). This result supports its advantage in the evaluated settings.

The result keeps the method, metric, effect, comparator, dataset scope, and citation while removing the template opening and universal overclaim.

See three complete input → optimization → output examples · 查看中文完整案例

Paper to Slides showcase