Compute overlap chi-squared metrics for partial reflectivity data files.
npx skills add https://github.com/mdoucet/analyzer --skill partial-assessment
Please help me install this Agent Skill. Skill: partial-assessment Source: https://github.com/mdoucet/analyzer/tree/main/skills/partial-assessment Command: npx skills add https://github.com/mdoucet/analyzer --skill partial-assessment
This Skill helps researchers ensure that partial reflectometry data from different angular settings overlap consistently before combining, preventing biased results and misinterpretation.
Run assess-partial <SET_ID> to generate a quality report for the specified partial data set.
High-intent search queries and answers about installing and using this skill.
You can validate partial reflectometry overlap by computing an overlap chi-squared metric between adjacent angular parts. This Skill reads REFL_{SET_ID}_{PART_ID}_{RUN_ID}_partial.txt files and outputs a Markdown report with a diagnostic plot to check for normalization or alignment issues.
Partial reflectivity data files must follow the naming convention REFL_{SET_ID}_{PART_ID}_{RUN_ID}_partial.txt. Each file needs a one-line header and four numeric columns: Q, R, dR, and dQ.
Overlap regions between adjacent partial data parts diagnose normalization, alignment, or sample-change problems. A computed overlap chi-squared metric reveals inconsistencies, and a generated diagnostic plot visually confirms whether the partial curves align properly.
Run assess-partial with a specified SET_ID to generate a quality report for that partial data set. The Skill identifies overlap regions only for the matching set and outputs a focused Markdown report and plot.
The overlap chi-squared metric quantifies consistency between adjacent partial reflectivity curves in overlap regions. High chi-squared values indicate normalization, alignment, or sample-change issues that should be resolved before combining the partial data.
Do not combine partial data when the overlap chi-squared metric reveals significant inconsistency between adjacent parts. The generated Markdown quality report and diagnostic plot help identify whether normalization, alignment, or sample-change issues prevent safe combination.
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