Skill UI

Maintain a review-first engineering wiki with provenance, citation-aware queries, explicit capture and promotion, and deterministic checks. Helps teams capture durable lessons and verify knowledge before turning it into rules.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill addresses vague or ambiguous user requests by initiating a structured, multi-round questioning process. Instead of making assumptions, it systematically clarifies dimensions like audience, scope, tone, and format across several rounds (up to three). This structured approach ensures the resulting output is highly accurate and requires minimal revision cycles.

Turn a repository into a cited HTML slide deck and detect when it drifts from the code. Citations record file, lines, and hash; a stdlib check reports CURRENT, MOVED, CHANGED, or MISSING. Ideal for docs-as-code and maintaining accurate presentations.

Verify citations and references in documents against real sources using the Stipple API. Checks resolution, arithmetic, and unsupported claims. Use before publishing research reports, whitepapers, or academic writing.

Generates comprehensive technical documentation pages with evidence-based depth, including Mermaid diagrams, citations, and VitePress frontmatter. Ideal for documenting components, systems, or features with traceable code paths.

Answer repository questions grounded entirely in source code evidence. Searches and reads relevant files, then synthesizes answers with inline citations. Useful for understanding specific files, functions, or components, and for answering how or where questions about the codebase.