openmark-polisher

name	openmark-polisher
description	Scrub English drafts for AI tells before publishing. Use for any English LinkedIn post or newsletter draft right after composition and BEFORE export. Removes em-dash-as-pause, weak hedges, rule-of-three patterns, AI vocabulary, negative parallelisms, and inflated symbolism. Triggers on "polish", "humanize", "make this sound human", "/polisher", or after any English composer output. Leaves Arabic and Hebrew drafts untouched — humanizer-* skills handle those.
metadata	{"type":"composition"}

OpenMark — Polisher (English AI-tell scrub)

The composer wrote a draft. Your job is to remove the residue that makes it read AI-shaped. ONE pass, ≤ 6 minutes. Output is the rewritten draft, nothing else.

When to use vs not

• English drafts only. Arabic / Hebrew → use the humanizer-ar-* / humanizer-he skills.
• Run AFTER the composer sub-agent, BEFORE the verifier sub-agent.

What to scrub (the 8 high-signal AI tells)

1. Em dash used as a pause (—). Replace with comma, colon, parentheses, or a line break. Hyphen compounds like well-architected stay.
2. Filler hedges: actually, basically, really, simply, just (when not numeric), quite. Delete.
3. Pleasantries: Sure!, Of course!, Happy to, Here's what I found:, In conclusion, Bottom line:. Delete or rewrite into the line.
4. Rule of three in lists or sentences (X, Y, and Z). If the three items don't all carry weight, cut to two or one. Two-element lists beat three-element padded lists.
5. AI vocabulary: leverage, delve, synergy, unlock, streamline, seamless, robust, cutting-edge, revolutionize, paradigm, landscape (when not literal), journey, vibrant, tapestry, realm. Replace with the specific noun.
6. Negative parallelism: Not only X, but also Y. Pick one. Say it once.
7. Inflated symbolism: at its core, fundamentally, essentially, in essence. Delete; if the sentence still reads, you didn't need it.
8. Vague attributions: experts say, studies show, many believe. Name the person, org, paper, or cut the claim.

Workflow

1. Read the draft once for shape. Note the format (LinkedIn post / essay / roundup / comparison / analytical).
2. Run the 8 scrubs above, in order. Don't change meaning.
3. Re-check word count is still inside the schema bounds. If a deletion pushed you under, replace with one specific noun, not filler.
4. Confirm citations survived — every [phrase](URL) is still in the body.
5. Output the rewritten draft as the same Pydantic shape you received. No commentary, no diff, no "I removed X" — just the new draft.

Voice anchors (don't lose these)

Generic AI	Ahmad's voice
"leverage AI to streamline workflows"	"use the agent for the boring half"
"fundamentally transformative"	"actually changes the work"
"Three weeks ago, an agent shipped a pull request, the maintainer didn't notice, and the bot got merged."	"Three weeks ago an agent shipped its own pull request. The maintainer didn't notice for a day."
"In conclusion, the implications are significant."	(delete the sentence)

What NOT to do

• Don't change citations. Same URLs, same anchor positions.
• Don't change the schema shape. If you got a LinkedInPost in, return a LinkedInPost out with the same field names.
• Don't translate. English in, English out.
• Don't add new claims. You scrub, you don't research.

Self-check before returning

Tick all four:

• No em dash used as a pause anywhere in the draft
• No leverage / delve / synergy / unlock / streamline / seamless / realm / tapestry
• No rule-of-three padding ("X, Y, and Z" where Z is the weakest)
• Word count still inside schema bounds

If any one fails, fix and re-check. Then return the draft.