se-uhd

Fill the grounding comments that review only flags as missing. For each `\cite{}` in a LaTeX paper without a quote-backed grounding comment — no comment at all, or a `TODO verify` stub left by revise mode or an earlier run — fetch the cited source, extract a verbatim quote that supports the claim, and write a `% GROUNDING` comment carrying that quote into the source — or a `TODO verify -- <reason>` stub when the source cannot be retrieved. Use when the user asks to ground citations, fill grounding comments, or close the review's grounding to-do. LaTeX source only.

Generate a project-local WRITING.md file from the layered writing rules and add a reference to it in the repository's CLAUDE.md (creating CLAUDE.md if it does not exist). Use when the user wants the writing conventions to live in their repo as an editable, project-local file. Triggers on prompts such as "set up writing rules in this repo", "generate WRITING.md", "init the writing conventions", or `/ai-slop:init`. Writes WRITING.md and creates or amends CLAUDE.md; does not modify your content.

Review only the modified parts of a git-versioned document for AI slop and rule violations. Use when the user has uncommitted edits or a feature branch and wants to audit only what they changed, not the whole draft. Triggers on prompts such as "check my edits", "review what I just changed", "audit this branch's prose", or `/ai-slop:review-diff`. Uses the same layered rules as `/ai-slop:review` (general by default; `--scientific` and LaTeX layers as detected), scoped to the diff. Writes a structured Markdown report that revise mode can apply.

Review a whole code repository's natural-language text for AI slop and rule violations — every Markdown and plain-text file plus the comments and doc-comments of its source and config files, not just one document or a diff. Use when the user wants to audit the prose spread across a codebase (READMEs, changelogs, design docs, and the comments in code and config). Triggers on prompts such as "scan this repo for slop", "check the prose across the codebase", "audit the comments and docs", or `/ai-slop:review-repo`. Loads the general rules by default; `--scientific` adds the research-article layer. Writes a structured Markdown report grouped by file.

Review a document (LaTeX, PDF, or plain prose) for AI slop and rule violations. Use when the user names a draft, hands you a path to a `.tex`, `.pdf`, or text file, or asks to check, audit, or review prose for AI tropes — and, for research papers, statistical reporting, citations, BibTeX correctness, and hallucinated references. The general rules apply by default; `--scientific` adds the scientific layer and LaTeX source loads all three. Writes a structured Markdown report with concrete suggested revisions that revise mode can apply.

Apply the findings of an `/ai-slop:review` report to the source — replace each flagged quote with its suggested revision, and insert `% GROUNDING` TODO stubs for ungrounded citations. Use when the user has a generated `ai-slop-report.md` (or equivalent) and wants the suggestions applied to the paper.

Detect hallucinated (fabricated) references in academic paper PDF files. Use when the user asks to check, audit, or verify the references/bibliography of one or more papers for hallucinated or fabricated citations, or names a paper PDF file (or directory of PDF files) to check. Extracts each reference, verifies it against academic databases (offline DBLP plus CrossRef, arXiv, Semantic Scholar, and other open databases) without using an LLM, then triages only the database-unverified residue via web search and writes a report of likely-hallucinated references plus per-paper manual-verification sheets.

Apply the community LLM reporting guidelines for empirical SE studies (see https://llm-guidelines.org). Use when the user plans, discusses, or audits an empirical study whose method involves an LLM, walks through the scope statement, eight reporting guidelines, or reporting checklist, picks a study type from the taxonomy (LLMs as annotators, judges, synthesis, or subjects; studying LLM usage in software engineering; LLMs for new SE tools; benchmarking LLMs for SE tasks), or asks for a structured pass over a draft (`.tex` or `.pdf`, plus any supplementary material) producing an `llm-guidelines-report.md`.

Scan a git repository with PMD plus an LLM-based antipattern scan driven by the bundled catalog, attribute each finding to its git-blame author, ask the user to select one developer, and synthesize a tailored LEARNING_PLAN.md with concept explanations, refactor sketches of the author's actual flagged code, and pointers to canonical references. Use when the user asks to scan a codebase for code-quality issues, build a personalized learning plan from static analysis findings, or coach a specific developer based on their characteristic warnings. Writes intermediate JSON reports under .code-quality-mentor/ and a final LEARNING_PLAN.md at the repo root.