• 🧩 The skills
• 🔗 How they connect
• ✅ Authenticity
• 🔬 Example
• 📦 Install
• 🚀 Usage
• 🗂️ Repository layout
• 🛠️ Included tools
• 🤝 Contributing
• ⚖️ License
• 🙏 Acknowledgments

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:

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).

_{The 8-page paper (first 6 pages) — click any page for the full PDF.}

• Real code, really run — model.py is a 1-D FNO; the full study runs in ~20 min on a laptop CPU.
• Measured results — FNO 6.67% rel-L2 vs MLP 22.47% and CNN 68.12% (3 seeds); zero-shot super-resolution holds 6.7–8.1% from grid 128 to 1024.
• Real citations — 22 references, each traceable to its source.

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:

1. skills/<name>/SKILL.md with name and description frontmatter (name = folder name).
2. Optional references/, templates/, and tools.
3. No import anthropic / import openai.
4. python tools/validate_skills.py passing (CI runs it on every PR).

See CONTRIBUTING.md and the Code of Conduct.

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.