Validate research methods against data fields for required auxiliary fields.
npx skills add https://github.com/Jiale-Zou/Thesis-Agent --skill plan-thesis-check
Please help me install this Agent Skill. Skill: plan-thesis-check Source: https://github.com/Jiale-Zou/Thesis-Agent/tree/main/skills/plan-thesis-check Command: npx skills add https://github.com/Jiale-Zou/Thesis-Agent --skill plan-thesis-check
This skill helps researchers verify that a given data plan — specifically the research method and the associated data fields — satisfies data requirements for modeling, analysis, and reproducibility. It ensures that the required fields exist or can be derived and that data usage aligns with methodological constraints, preventing missing data and incompatible transformations.
Provide the research method and data field definitions to receive a feasibility assessment and a recommended data checklist.
High-intent search queries and answers about installing and using this skill.
Checking data-method alignment involves analyzing your research method against associated data fields to verify compliance with data requirements. It evaluates whether required auxiliary fields for modeling exist or can be derived, identifying gaps for data preparation.
A data feasibility check verifies that your data plan satisfies data requirements for modeling, analysis, and reproducibility. You need it to ensure required fields exist or can be computed, preventing missing data and incompatible transformations during your research.
To identify missing auxiliary fields for Python-based data modeling, evaluate your data plan to detect needed time, identifier, or grouping fields. The validation process flags any missing auxiliary fields, outlining a minimal data checklist for execution.
Yes, this data validation method supports Python-based tooling to implement the validation of research methods against data fields. It evaluates whether a Python-based implementation is practical and outputs clear feasibility assessments and required data checklists.
A limitation of validating thesis data plans before data collection is that it relies entirely on the defined data fields and research methods provided. It can only identify whether fields can be derived from existing inputs, not guarantee actual data availability during collection.
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