TheDop

Analytical Figures Skill — Development skill for Claude Code

Development community

Claude Code skill: publication-grade figures + gated analysis for analytical chemistry (FTIR/ATR, PXRD, calibration, chemometrics, CIF/crystal, cocrystal ID).

How to install Analytical Figures Skill

This entry records only its repository, not the path inside it, so there is no exact command to give. Open TheDop/analytical-figures-skill and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Analytical Figures Skill does

Claude Code skill: publication-grade figures + gated analysis for analytical chemistry (FTIR/ATR, PXRD, calibration, chemometrics, CIF/crystal, cocrystal ID).

Alternatives in Development

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Kaizen — Applies continuous improvement methodology with multiple analytical approaches, based on Japanese Kaizen philo 663 ★

README

analytical-figures

Ask [Claude Code](https://claude.com/claude-code) for a calibration curve from your `.spc` files and get a journal-ready figure with LOD/LOQ, confidence and prediction bands and a residual panel, plus one self-contained Python script that reproduces it. Ask for a calculated PXRD pattern from a CIF, a cocrystal-vs-physical-mixture call or PLS diagnostics with leakage-safe cross-validation and get the same: a figure that passed its checks, and the code that made it.

`analytical-figures` is a Claude Code skill for analytical chemistry and spectroscopy: FTIR/ATR and PXRD spectra, calibration with figures of merit (LOD/LOQ, recovery, ICH Q2), multivariate calibration (PLS/PCR/PCA), crystal structures from CIFs (validation table, calculated PXRD, deterministic 3D views) and cocrystal identification.

It is not a plotting tutorial. It does two things a plotting library does not:

It gates the numbers before they reach a figure. Ingest checks, identical processing across a batch, named error statistics (SD vs SEM vs CI, with n), leakage-safe cross-validation, mandatory residual panels, no extrapolation past the calibrated range, and a static critic that flags a figure-of-merit hand-typed onto a figure instead of interpolated from the code.
It hands over a script, not a PNG. The deliverable is one self-contained Python file (bundle.py amalgamates the modules an analysis uses) with a provenance header, so every figure is reproducible, editable and attachable to a report.