Audit data, model baseline and enhanced scenarios, and quantify uncertainty for COMAP MCM Problem C.
npx skills add https://github.com/twj0/2026mcm --skill mcm-c-modeler
Please help me install this Agent Skill. Skill: mcm-c-modeler Source: https://github.com/twj0/2026mcm/tree/main/.windsurf/skills/mcm-c-modeler Command: npx skills add https://github.com/twj0/2026mcm --skill mcm-c-modeler
This Skill provides a structured framework to interpret COMAP MCM/ICM Problem C by translating the prompt into a modular data-analytic workflow: problem decomposition, data auditing, and principled modeling with uncertainty and robustness assessment.
Run the full modeling workflow on the provided data to generate a baseline and an improved, explainable model with uncertainty analysis.
High-intent search queries and answers about installing and using this skill.
Structure MCM C statistical modeling by decomposing the problem, auditing data, and building modular pipelines that output baseline versus enhanced models with robustness checks and uncertainty intervals.
Quantify data uncertainty and robustness by applying principled modeling pipelines that generate uncertainty intervals and perform robustness evaluation for auditable, explainable decision support outputs.
Audit data inventory by identifying available datasets, extracting missingness patterns, listing time ranges, and understanding data dictionaries to confirm data sufficiency before modeling.
Yes, you can generate interpretable baseline and improved models by splitting the task into modular deliverables with clear inputs, outputs, and evaluation criteria for reproducible reporting.
MCM C modeling requires documentation-driven reproducibility to maintain traceable pipelines, validate risk results, and produce auditable outputs with explainability for robust decision support.
Selecting feasible external data is supported when confirming data sufficiency, allowing you to perform robustness evaluation and generate validated risk and uncertainty results for MCM C.
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ovachieverEstimate regression and time-series models for statistical inference.
the-thinker0Quantify uncertainty in data-driven decisions using probability theory.
