Research Quality Audit
Audits research repositories for content depth, missing source files, and incomplete analysis documents to ensure high-quality documentation.
This skill provides a comprehensive auditing framework for research corpora within the AIWG ecosystem. It identifies 'stubs' or 'skeletons'—documents often written from abstracts rather than full papers—and assesses the depth of analysis based on line counts, section headers, and key quotes. Crucially, it can automate the expansion of shallow documents by dispatching specialized agents to read full source PDFs and rewrite the analysis with substantive detail, ensuring the research repository remains a high-value asset for AI-augmented software development.
Key Features
01Customizable quality thresholds and specific reference range auditing
02Verification of source availability for PDFs and extracted text files
03Automated classification into quality tiers from 'Full' to 'Skeleton'
04Deep corpus scanning for line counts, section headers, and quote frequency
05Auto-fix mode that dispatches agents to expand shallow documentation
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Use Cases
01Validating the quality of a research library after a large batch paper induction
02Generating health reports for large research corpora to prioritize expansion tasks
03Identifying and fixing 'abstract-only' documentation that lacks methodological depth