Jupytermind：AI Scientist Skill Suite — Jupyter MCP Copilot Agent Skills

日本語 README

A collection of GitHub Copilot Agent Skills for natural-language (Japanese/ English) scientific computing over Jupyter via the Jupyter MCP (Datalayer jupyter-mcp-server). Every reasoning-based insight is recorded in the project notebook alongside the executed cell that provides its evidence. Built with SDD (musubix3) and TDD (pytest).

Skills included

| Skill | Scope | | --- | --- | | ai-data-scientist | Load, clean, explore, analyze, visualize, and draw insights from datasets via Jupyter (MVP + ML extension). | | ai-chemistry-scientist | Cheminformatics: molecular descriptors, ADMET heuristic screening, QSAR modeling, similarity search, docking-score heuristics, drug-likeness/structural-alert screening, formula/mass calculation, SMILES standardization and format conversion. | | ai-genomics-scientist | Computational genomics: sequence feature analysis, variant-effect heuristic annotation, splice-site strength scoring, gene-set enrichment analysis, pairwise sequence alignment. | | ai-materials-scientist | Materials-science simulation: phase-field microstructure evolution, molecular dynamics, classical/kinetic Monte Carlo, crystal plasticity, a simplified FEM solver, and a simplified binary CALPHAD phase diagram. | | ai-structural-biology-scientist | Structural biology: secondary-structure and hydrophobicity/burial heuristics, protein-protein docking-score heuristics, RMSD-based structural similarity, residue contact-map heuristics. | | ai-scientist | End-to-end single-project research guidance: planning, literature review, experimental design, data analysis, manuscript writing, peer review, reproducibility checks, and presentation. | | tech-writer | Structures and polishes technical documents: READMEs, design docs/ADRs, API references, PR descriptions, release notes, user manuals, code comments. | | japanese-prose | Improves Japanese prose quality using GiNZA-based diagnostics (kotonoha). | | presentation-planner | Plans PPTX content structure and design handoff (does not generate the file itself). | | sdd-* (sdd-change, sdd-requirements, sdd-design, sdd-implementation, sdd-quality, sdd-traceability, sdd-knowledge, sdd-formal-codegraph, sdd-issue-report) | The Specification-Driven-Development workflow (musubix3) used to build and evolve every skill above. |

See each skill's .github/skills/<name>/SKILL.md for its full invocation instructions and trigger phrases.

Setup

Install the skill from the npm registry (no need to clone this repo):

npm install ai-data-scientist-skill
npx ai-data-scientist doctor

Or, for a local checkout of this repo:

npm install
npx ai-data-scientist doctor

The Python environment (virtualenv + dependencies) is set up automatically on first CLI invocation in both cases.

npm install itself has no dependencies and does not use postinstall (since npm v12, install scripts of dependencies are disabled by default and require npm approve-scripts). Instead, bin/ai-data-scientist.js creates a .venv and installs the dependencies from pyproject.toml on first CLI invocation. Subsequent invocations are cached and start fast.

To set up the Python environment manually:

python3 -m venv .venv
.venv/bin/pip install -e .
.venv/bin/pytest

PDF export prerequisites

report_export.export_report(..., report_format="pdf") uses nbconvert's PDFExporter, which shells out to a system xelatex binary. This is not installed by pip/npm and must be provided separately, e.g.:

# Debian/Ubuntu
sudo apt-get install texlive-xetex texlive-fonts-recommended

# macOS
brew install --cask mactex-no-gui

If xelatex is not on PATH, PDF export raises a RuntimeError pointing back to this section; use report_format="html" if you do not need PDF output.

Skills

See .github/skills/<skill-name>/SKILL.md for each skill's invocation instructions and .musubix/features/<feature-slug>/requirements.md for its authoritative requirement set.

License

MIT License — see LICENSE.

Changelog

See CHANGELOG.md for release notes.