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 over-represented in LLM output vs human baseline. Apply as a final pass, not a rewrite-from-scratch: preserve meaning, facts, 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, no editing.
• No argument: humanize the draft already composed earlier this turn (self-review pass) — only reachable when the model chooses to invoke this skill mid-task; no platform hook guarantees a pre-send interception, so treat this path as best-effort, not a hard gate.

</inputs> <workflow>

1. Load the target text

• Inline text: work on it directly, no file I/O.
• File path: Read the file.
• Destination guard (before any edit): target must be human-facing prose. JSON/YAML/config, code, a machine-parsed agent envelope (Return ONLY:), or an ultra-caveman handover file under .temp/ → emit **! BLOCKED — humanizer does not apply to <destination type>** and make no edit. check mode reports without editing, so it is unaffected.
• No argument: treat the draft already composed earlier this turn as the target.

2. Scan against the checklist

Walk the text once per category below; enumerate every hit internally before editing anything (report-first, matches check mode output). The isolated-hit threshold in <notes> is the authority on which of those hits reach the report or an edit — an incidental lone match in an otherwise clean passage is scanned but not reported.

Vocabulary — cut or replace with plain equivalent:

Banned	Plain replacement
delve, boasts, testament, underscore(s), showcase, tapestry, intricate/intricacies, meticulous, robust, vibrant, pivotal, crucial, garner, foster(ing), align with, landscape, interplay, enduring, enhance	say the specific thing instead — drop the word, don't swap in another vague one
"stands as", "serves as", "marks a", "represents" (as copula dodge)	"is" / "was"
"Additionally,", "Moreover,", "It is important to note that"	delete, or state the fact directly

Syntax — flag and restructure:

• Negative parallelism: "not just X, but Y" / "not X, but Y" / "not only X but also Y" / "X rather than Y" used as a crutch
• Rule-of-three triads used for false comprehensiveness ("fast, reliable, and scalable")
• "-ing" superficial-analysis tails: "highlighting...", "underscoring...", "contributing to..." tacked onto a claim with no source
• Vague attribution / weasel words: "industry reports", "observers", "experts argue", "some critics" with no named source
• Formulaic "Despite its [positives], X faces challenges..." conclusion pattern

Formatting — flag and fix:

• Title Case In Headings → sentence case
• Mechanical bolding of every instance of a repeated term
• Markdown overuse — bold/bullets/headers where a plain sentence reads fine; the single most common tell in PR bodies and reports
• Bare-bullet inline-header lists (• **Header:** text) where prose or a real table reads better
• Em dash overuse — chain of — clauses instead of periods/commas
• Curly ("smart") quotes/apostrophes mixed inconsistently with straight ones
• ---/*** thematic breaks before headings (Markdown artifact bleeding into prose)

3. Apply fixes

• check mode: stop here — report findings (category, location, quote, suggested fix), no edit.
• Edit mode: apply minimal edit per flagged instance using Edit. Preserve every fact, number, citation — only phrasing/formatting changes. Re-read result once to confirm no fact dropped in rewrite.

4. Report

One line per category with hit count and net edits made (e.g. "vocabulary: 4 removed, syntax: 2 restructured, formatting: 1 fixed"). Zero hits: say so plainly, don't pad the report.

</workflow> <notes>

• Checklist source: Wikipedia's Wikipedia:Signs of AI writing essay — living document; vocabulary list drifts as models change ("delve" was the 2023-24 tell, largely purged by 2025). Treat the table above as a snapshot, not gospel — a word natural and specific in context isn't a forced cut just because it once trended in AI output.
• This skill governs artifacts headed for human eyes, not conversational chat turns or ultra-caveman-tier handover files — see SKIP list in description: for exact destination-based cutoff.
• Never invent facts while trimming a vague-attribution sentence — name the real source (if known from context) or cut the claim entirely. Don't launder a weasel-worded claim into a confident unsourced one.
• Dense co-occurrence (5+ flagged patterns in one passage) is the real signal — a single "robust" or one bolded term isn't worth flagging in isolation; don't over-trigger on incidental matches.
• Commit messages: rules/git-commit.md structural rules are inviolable (subject ≤50 chars, type(scope): detail, no line-wrap, mandatory co-author trailers, self-contained no internal labels) — on a commit message, humanizer touches only word choice inside those constraints, never subject length, wrapping, or trailer lines.
• Checklist deliberately excludes Wikipedia-only categories (broken wikitext, DOI/ISBN citation format, AfC submission-statement framing, non-existent Wikipedia templates) — those don't apply outside Wikipedia; don't re-add them.

</notes>