Open-source agent skills for AI for Science: topic exploration, literature survey, experiments, paper writing, and integrity audit — driven by any coding agent.
# Add to your Claude Code skills
git clone https://github.com/ai4s-research/ai4s-skillsai4s-skills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ai4s-research. Open-source agent skills for AI for Science: topic exploration, literature survey, experiments, paper writing, and integrity audit — driven by any coding agent. It has 140 GitHub stars.
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Clone the repository with "git clone https://github.com/ai4s-research/ai4s-skills" and add it to your Claude Code skills directory (see the Installation section above).
ai4s-skills is primarily written in Python. It is open-source under ai4s-research on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh ai4s-skills against similar tools.
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Seven agent skills for AI-for-Science research — turn a research direction into literature surveys, runnable experiments, publication-grade papers, and integrity audits, with every citation, number, and figure traceable to its source.
| Skill | Role | Primary output |
|---|---|---|
| ai4s-agent | Runs the four skills below in order | the full package |
| research-explorer | Explore topics from a broad direction | research_exploration.md, topic_matrix.md, literature_pre_survey.md |
| literature-survey | Write a literature survey | 6–20 pp PDF, 60+ real citations, LaTeX source, taxonomy figures |
| experiment-suite | Build an experiment package | design doc, runnable code, results.json with provenance, figures, report |
| paper-writer | Write a research paper | 8–14 pp PDF, 200+ citations, 4–8 figures, tables |
| mindmap-render | Render a mindmap | image from a topic_matrix.md (Python script) |
| integrity-auditor | Audit a paper's integrity | image / numerical / logical findings, 4-level evidence grading, audit_report.md |
Each skill is a folder with a SKILL.md plus its own references, templates, and
tools. MIT-licensed; works with Claude Code, Cursor, Codex, and Aider.
direction
│
▼
[1] research-explorer ──▶ pick one $TOPIC
│
├──▶ [2] literature-survey → survey PDF + bibliography.bib
├──▶ [3] experiment-suite → results.json + figures/
└──▶ [4] paper-writer → paper PDF (reuses [2] and [3])
integrity-auditor ──▶ audits any paper: external PDF / DOI / arXiv, or [4]'s output
ai4s-agent runs steps 1–4 in order. Skills pass work to each other through a
shared slug and the path output/<skill>/<slug>/latest/.
The focus of the project. Every skill enforces:
| Principle | In practice |
|---|---|
| Real citations | Every BibTeX entry links to a URL the agent fetched in the same session; none from memory. |
| Labelled numbers | Every number is marked measured, simulated, or illustrative; simulated values are never reported as measured. |
| Runnable experiments | experiment-suite outputs runnable code and a results.json with provenance. Supply real results and they replace the simulated ones; the "simulated" disclosure is then removed. |
| Resumable runs | Long tasks save progress after each step and continue from the last checkpoint, so a reported "done" reflects completed work. |
| Publication layout | booktabs tables, [!t] floats, ~\cite{}; vector-PDF figures with embedded fonts and defined color palettes. |
| Review disclosure | Every generated document states that domain-expert review is recommended. |
| Integrity checks | integrity-auditor inspects a paper for image, numerical, and logical problems and grades the evidence. |
A complete run from experiment-suite + paper-writer: "Learning the Burgers
Solution Operator with a Fourier Neural Operator" — an 8-page paper backed by code
the agent wrote and ran. Full artifact in examples/fno-burgers/
(paper, code, results.json, report).
model.py is a 1-D FNO; the full study runs in ~35 min on a laptop CPU.Every number is measured (provenance in results.json); the paper states it was
AI-generated and recommends domain-expert review.
Run the installer from the project you want the skills in:
git clone https://github.com/ai4s-research/ai4s-skills
cd /path/to/your-project
/path/to/ai4s-skills/install.sh # all skills → ./.claude/skills
/path/to/ai4s-skills/install.sh paper-writer # or specific ones
SKILLS_DIR=~/.claude/skills /path/to/ai4s-skills/install.sh # global instead
To install by hand, copy any skills/<name>/ into ~/.claude/skills/ (global) or
<project>/.claude/skills/ (project).
In Claude Code:
Use the literature-survey skill to write a survey on <your topic>.
With Cursor, Codex, or Aider, point the agent at the skill file:
Read skills/literature-survey/SKILL.md and its references/, then produce the survey
for "<your topic>" as specified.
Each SKILL.md directs the agent to read its references/ first; those files hold
the procedures for bibliography expansion, figures, layout, and quality checks.
ai4s-skills/
├── skills/
│ ├── ai4s-agent/ SKILL.md + references/
│ ├── research-explorer/ SKILL.md
│ ├── literature-survey/ SKILL.md + references/ + templates/survey/
│ ├── experiment-suite/ SKILL.md + references/ + figure_examples/
│ ├── paper-writer/ SKILL.md + references/ + templates/paper/
│ ├── mindmap-render/ SKILL.md + scripts/ + tests/
│ └── integrity-auditor/ SKILL.md + references/ + forensics_tools/ + templates/ + tests/
├── tools/validate_skills.py structure / frontmatter validator (run in CI)
├── install.sh
└── .github/workflows/ci.yml
Each SKILL.md carries YAML frontmatter (name, description) so an agent can
find and route to it.
Small, single-purpose scripts the skills call. Each directory has its own
requirements.txt.
skills/integrity-auditor/forensics_tools/ — image duplication / ORB matching, panel splitting, channel checks, magnitude (Benford-style) consistency, decimal matching, spreadsheet aggregate consistency.skills/experiment-suite/figure_examples/ — a matplotlib style kit (style_kit.py) and worked figure examples.skills/mindmap-render/scripts/ — generate_mindmap.py.A new skill needs:
skills/<name>/SKILL.md with name and description frontmatter (name = folder name).references/, templates/, and tools.import anthropic / import openai.python tools/validate_skills.py passing (CI runs it on every PR).See CONTRIBUTING.md and the Code of Conduct.
If you use AI4S Skills in your research, please cite it:
@software{ai4s_skills,
author = {{The AI4S Skills Contributors}},
title = {AI4S Skills: open-source agent skills for AI for Science},
year = {2026},
version = {0.1.0},
doi = {10.5281/zenodo.21297455},
url = {https://github.com/ai4s-research/ai4s-skills},
license = {MIT}
}
The DOI above cites all versions; GitHub's "Cite this repository" button
(generated from CITATION.cff) provides the same reference
in APA and BibTeX.
MIT.
Outputs are drafts. Review by a domain expert is recommended before any citation, submission, or decision. Verify numbers, citations, and claims.
Thanks to linux.do — a vibrant tech community where this project is shared and discussed.