This skill reviews only the lines that changed in a git-versioned document. It runs git diff <base> (default base: HEAD), restricts rule and trope checks to lines added or modified in .tex files (or .tex/.md/.txt for a non-LaTeX repo), and writes the same ai-slop-report.md schema as /ai-slop:review so /ai-slop:revise can apply the suggestions unchanged.
The skill auto-detects the paper and the diff in the current working directory.
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Verify git context. Run git rev-parse --is-inside-work-tree. If not in a git repo, stop with the message above. Otherwise capture the repo root for later path resolution.
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Resolve inputs. Auto-detect the LaTeX root; exit 1 means a general-scope repo, not a failure (see Paper detection). Parse the base ref (default HEAD) and any --tropes=<path> arguments (repeatable) from the user's message.
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Compute the diff. Run git diff --unified=0 <base> -- <pathspec> to list changed lines, where <pathspec> is '*.tex' when detection (step 6) returns latex and widens to '*.tex' '*.md' '*.txt' for general. Parse the unified-diff hunk headers (@@ -<old_start>,<old_count> +<new_start>,<new_count> @@) to extract per-file line ranges of added or modified lines in the new tree (the working-tree side). Track these as the changed-line set per file. If no matching files changed, write an empty report (Summary: "No changes since <base>."), print it, and stop.
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Expand to paragraph context. For each changed-line range, expand outward in the new file to the nearest preceding and following blank line so the changed prose is reviewed in its full paragraph. Record both:
- the changed lines (used to scope what counts as a finding), and
- the surrounding paragraph (used as context so multi-line tropes or sentence-length checks have enough text to operate on).
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Identify sections. For each changed paragraph, walk backward in the new file to the nearest preceding \section{} or \subsection{} (for Markdown, the nearest preceding # / ## heading) to map the paragraph to its section. This drives section-aware rules (e.g., verb tense, threats-to-validity specificity), which apply only when the scientific layer is in scope.
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Determine which rule layers to load. Same three layers as /ai-slop:review under ../../shared/. Run python3 ${CLAUDE_SKILL_DIR}/../../scripts/detect_scope.py <repo-root>: latex → load all three layers; general → load rules-general.md, plus rules-scientific.md when --scientific was passed (a non-LaTeX research manuscript). The diff pathspec in step 3 follows the LaTeX/general split. Read each selected layer file; each adds its own rules and self-check section, and a finding's Rule name comes from whichever layer defines it.
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Load the AI-trope catalog. If --tropes=<path> was passed (one or more times), read each named file and concatenate them in the order given. Otherwise run python3 ${CLAUDE_SKILL_DIR}/../../scripts/fetch_tropes.py ${CLAUDE_SKILL_DIR}/../../shared/tropes-snapshot.md and read its stdout. The script tries the upstream Gist, then the tropes.fyi viewer, then the bundled fallback, and always emits a non-empty body.
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Per-paragraph pass. For each changed paragraph, scan the prose against the rules and the trope catalog. A finding is in scope only if at least one line of the offending quote falls inside the changed-line set. A pre-existing violation on an unchanged line is out of scope, even when adjacent to a change. For each in-scope violation, record:
- The rule or trope name.
- The location (
file:line in the new file).
- A short verbatim quote of the offending text, with enough surrounding context to be unique within the paper.
- A concrete suggested replacement.
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Cross-cutting metrics, scoped to the diff. Compute on changed lines only:
- Em-dash count and locations in changed lines.
- Restricted-word occurrences in changed lines.
- Verb-tense compliance for changed paragraphs (against the section table in the scientific layer; only when that layer is in scope).
- American-vs-British spelling in changed lines.
- "Significant" audit on changed lines.
Skip metrics that need full-paper context (e.g., em-dash density per page, sentence-length variance over runs of three sentences spanning untouched prose, paragraph-restricted-word density when the diff touched only a fraction of the paragraph). Note the scoping in the report's Summary.
Density needs full-paper context, but a single mark that is the wrong choice does not. On a changed line, treat a semicolon joining two independent clauses a period would separate (especially when the second opens with we, it, this, they, or these), an em-dash standing in for a period, or a colon used as a generic mid-sentence pause as a per-section finding, with the corrected punctuation as the suggested revision.
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Citations and BibTeX, scoped to the diff. On changed lines, scan for newly added or modified \cite{} calls. Apply the same checks as /ai-slop:review step 6 (citation clusters with three or more entries lacking per-work explanation, missing grounding comments — % GROUNDING <key>: counts as grounded, same as % GROUNDING: — spelled-out author names that should use \citeauthor{}). For BibTeX field checks scoped to newly cited keys: identify the .bib files via \bibliography{...} / \addbibresource{...} directives, run python3 ${CLAUDE_SKILL_DIR}/../../scripts/check_bib_fields.py <bibfiles>, and report only entries whose keys appear in newly added \cite{} calls. Do not flag pre-existing citations the diff did not touch. Standard BibTeX semantics apply (no crossref inheritance) — sanity-check flagged entries. Always list changed \cite{} calls that lack a grounding comment under Grounding to-do (the same always-on list as /ai-slop:review, restricted to changed cites). For newly cited keys, also run python3 ${CLAUDE_SKILL_DIR}/../../scripts/verify_references.py <bibfiles> [--mailto you@example.org] and report flagged entries whose keys appear in newly added \cite{} calls under Reference verification (advisory and online-first, exactly as in /ai-slop:review step 6).
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Write the report. Save ai-slop-report.md in the working directory. As in /ai-slop:review, the report must never be committed: if the working directory is inside a git repository and its .gitignore does not already list ai-slop-report.md, append that line (creating .gitignore if absent).
Then run python3 ${CLAUDE_SKILL_DIR}/../../scripts/lint_markdown.py --fix ai-slop-report.md. If the linter exits non-zero, read its stdout findings (one per line, tab-separated <file>:<line>\t<rule>\t<message>), revise the report in place to address each, and re-run the linter. Repeat at most three iterations; after the third pass proceed regardless of the linter's state. The lint loop is internal quality control — do not mention lint output, rule names, exit codes, or iteration counts in the user-facing summary.
Then Read the file back and quote its contents verbatim in your reply — do not regenerate the report text from memory for the inline echo, which has triggered repetition glitches (duplicate disclaimer blockquotes and ## Summary headings). Echoing the Read result keeps the printed version identical to the file. Use the report template from ../review/SKILL.md "Report template" with one extra header line:
**Diff scope:** base=<base ref>, files=<list of changed files in the diff pathspec>
Place this line under **Reviewed:**. The Summary should explicitly note that the review only covered changed lines, so a reader of the report does not assume the rest of the paper was checked.
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Stop after the report. Do not modify the paper. If the user wants the findings applied, route them to /ai-slop:revise — the schema is identical, so revise mode works without changes.
