Claude Code Skills

Claude Code Skills#

JAXSR includes a set of Claude Code skill files that enable an AI assistant to guide you through symbolic regression workflows interactively. Instead of reading through documentation to find the right API calls, you can describe your problem in natural language and get tailored recommendations for basis libraries, selection strategies, uncertainty quantification, and more.

What Are Claude Code Skills?#

Claude Code is Anthropic’s CLI tool for AI-assisted software development. Skills are structured knowledge files that give the assistant domain-specific expertise. When you use Claude Code in a project that contains skill files, the assistant automatically loads them and can provide context-aware guidance.

JAXSR’s skill files teach the assistant:

How to recommend basis libraries based on your problem domain
Which selection strategy and information criterion to choose
When and how to apply physical constraints
Which UQ method fits your needs
How to set up DOE studies and active learning loops
How to use the CLI for code-free workflows

File Layout#

The skill files live in two mirrored locations:

.claude/skills/jaxsr/          # Primary location (used by Claude Code)
├── SKILL.md                   # Main skill definition
├── guides/                    # Detailed topic guides
│   ├── active-learning.md
│   ├── basis-library.md
│   ├── cli.md
│   ├── constraints.md
│   ├── doe-workflow.md
│   ├── known-model-fitting.md
│   ├── model-fitting.md
│   ├── rsm.md
│   └── uncertainty.md
└── templates/                 # Ready-to-run starter scripts
    ├── active-learning-loop.py
    ├── basic-regression.py
    ├── constrained-model.py
    ├── doe-study.py
    ├── langmuir-isotherm.py
    ├── notebook-starter.py
    └── uncertainty-analysis.py

src/jaxsr/skill/               # Mirror (included in the package)
└── ...                        # Same contents as above

These two directories must stay in sync. After editing any file under .claude/skills/jaxsr/, run:

rm -rf src/jaxsr/skill && cp -r .claude/skills/jaxsr src/jaxsr/skill

Skill Components#

SKILL.md — Main Skill Definition#

The entry point for the assistant. It contains:

Activation triggers — when the skill should engage (e.g., “discover algebraic expressions”, “set up a DOE study”, “choose between UQ methods”)
Assistant mode — a step-by-step diagnostic flow that walks users through problem characterization, basis library selection, strategy choice, constraint setup, UQ method selection, and reporting format
Decision tables — quick-lookup tables mapping scenarios to recommended configurations
Quick-reference API and CLI — concise code snippets for common workflows
Common pitfalls — mistakes the assistant should warn users about

Guides#

Each guide covers one topic in depth with code examples, comparison tables, and decision flowcharts:

Guide	Topic
`basis-library.md`	Building and configuring basis function libraries. Covers all `add_*()` methods, domain-specific recipes (DOE, engineering, kinetics), and library sizing guidelines.
`model-fitting.md`	Selection strategies (`greedy_forward`, `greedy_backward`, `exhaustive`, `lasso_path`) and information criteria (`aic`, `aicc`, `bic`). Includes a strategy comparison table and criterion selection flowchart.
`uncertainty.md`	UQ methods: OLS prediction intervals, coefficient intervals, Bayesian Model Averaging, conformal prediction, bootstrap, ensemble predictions, and ANOVA.
`constraints.md`	Physical constraints: bounds, monotonicity, convexity/concavity, sign constraints, and known coefficients. Hard vs. soft enforcement.
`doe-workflow.md`	Full DOE lifecycle: study creation, experimental design (Latin hypercube, factorial, CCD, Box-Behnken), data collection, model fitting, adaptive sampling, and reporting.
`active-learning.md`	Acquisition functions (uncertainty, error, leverage, gradient) and the adaptive sampling loop.
`rsm.md`	Response Surface Methodology: CCD and Box-Behnken designs, canonical analysis, ridge analysis, and optimization.
`known-model-fitting.md`	Fitting known model forms (Langmuir, Arrhenius, Michaelis-Menten) using parametric basis functions, including ANOVA and uncertainty analysis.
`cli.md`	Complete CLI reference: `jaxsr init`, `design`, `add`, `fit`, `suggest`, `report`, `status`.

Templates#

Ready-to-run Python scripts the assistant can offer as starting points. Users can copy and customize them:

Template	Description
`basic-regression.py`	Minimal workflow: load data, build library, fit, inspect, export.
`constrained-model.py`	Adds monotonicity and bound constraints to a regression model.
`doe-study.py`	End-to-end DOE: create study, generate design, add data, fit, suggest next experiments, report.
`uncertainty-analysis.py`	Runs all UQ methods (OLS, BMA, conformal, bootstrap, ensemble) and compares results.
`active-learning-loop.py`	Iterative loop: fit model, pick acquisition function, suggest points, collect data, repeat.
`langmuir-isotherm.py`	Known-model fitting for the Langmuir adsorption isotherm with parametric basis functions.
`notebook-starter.py`	Jupyter notebook cell structure with markdown headers and code cells for a complete analysis.

Using the Skills#

With Claude Code CLI#

If you have Claude Code installed, simply open a terminal in the JAXSR project directory and start a session:

claude

The assistant will automatically detect the skill files. You can then ask questions like:

“I have temperature and pressure data for a catalyst. Help me find an equation.”
“Which UQ method should I use for 30 data points?”
“Set up a DOE study for three continuous factors.”
“I know the model is Langmuir — help me estimate the parameters.”

The assistant will walk you through the decision process step by step, recommend configurations, and generate ready-to-run code.

Without Claude Code#

The skill files are still useful as standalone documentation. You can read them directly:

Browse the guides in .claude/skills/jaxsr/guides/ for in-depth topic coverage
Copy templates from .claude/skills/jaxsr/templates/ as starting points for your scripts
Reference SKILL.md for quick-lookup decision tables and API cheat sheets

Contributing to the Skills#

When adding or modifying skill files:

Edit files in .claude/skills/jaxsr/ (the primary location)

Sync to the package mirror:

rm -rf src/jaxsr/skill && cp -r .claude/skills/jaxsr src/jaxsr/skill

Verify code examples against the actual JAXSR API (see the Red-Team Review checklist in CLAUDE.md)

Run linting:

black src/ tests/
ruff check --fix src/ tests/