humanizer

<objective> Detect and remove statistical AI-writing fingerprints from human-facing prose before it ships. Grounded in Wikipedia's crowd-sourced AI-detection corpus (`Wikipedia:Signs of AI writing`) — a maintained list of vocabulary, syntax, and formatting patterns that over-represent in LLM output vs human baseline. Apply as a final pass, not a rewrite-from-scratch: preserve meaning, facts, and structure; only excise the tells. </objective> <inputs> - **text or file path to humanize**: optional. Inline text, or a file path (Markdown/plain text) to edit in place. - **check `<file>`**: read-only mode — report findings without editing. - No argument: humanize the draft already composed earlier in this turn (self-review pass) — only reachable when the model chooses to invoke this skill mid-task; there is no platform hook that guarantees a pre-send interception, so treat this path as best-effort, not a hard gate. </inputs> <workflow> **Task hygiene**: call `TaskList` first; triage orphaned tasks. **Task tracking**: skip for single-pass humanize calls under 3 steps; use for multi-file batch runs. ## 1. Load the target text - Inline text → work on it directly, no file I/O. - File path → `Read` the file. - No argument → treat the draft already composed earlier in this turn as the target. ## 2. Scan against the checklist Walk the text once per category below; flag every hit before editing anything (report-first, matches `check` mode output). **Vocabulary — cut or replace ...