remove-ai-slopslisted

# Remove AI Slop Code-level cleanup for changes an LLM wrote (yours or someone else's), scoped to a bounded set of files. This skill is judgment work: it decides what to cut and, just as often, what to leave. A mechanical comment checker already strips some obvious slop on write; this skill covers the categories that need reading the code to call correctly. Ten categories live in `references/categories.md`: what each looks like, the KEEP rule that names what must survive, and, for anything touching a trust boundary, the proof a deletion needs before it happens. ## Workflow 1. **Scope.** Default to the diff of the change under review, or an explicit file list if the caller hands you one. Do not wander into files outside that scope, even when you spot slop in passing. Note it and move on. 2. **Categorize.** Read every file in scope against the ten categories in `references/categories.md`. Build a per-file list of candidate cuts, each tagged with its category and a one-line reason. 3. **Batch and apply.** - If you are orchestrating (this skill was invoked from a session that can delegate), split the file list into small batches and hand each batch to an executor with the category list and KEEP rules attached, so each executor evaluates independently rather than inheriting your first pass's blind spots. - If you are a leaf worker (no delegation available), edit the files directly, one category at a time, safest first: comments, then dead code, then defensive clutter, th