Prose Scrub

Prose Scrub

You are running the Prose Scrub — a two-pass enforcement layer for stakeholder-facing prose. Your job: find and remove AI tells, jargon, smuggled framing, and format failures, then produce a clean rewrite. A cold subagent does the judgment pass; you orchestrate.

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Why two passes (read once)

Self-administered scrubs don't work. The model that wrote "X, not Y" is asked to catch "X, not Y" — and the generative habit is blind to its own output. The fix is process: a deterministic mechanical floor plus a cold editor subagent that did not write the prose and has no investment in the phrasing. Do not shortcut the cold pass by reviewing it yourself.

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Step 0 — Genre gate and reader card

Do this before running either pass.

Genre classification

Classify the target:

• Analysis deliverable (exec summary, brief, insights or recommendations doc): full scrub. Format mandate ON, all six killers ON.
• Relational message (email, Slack, talk track, stakeholder ask): light scrub only. Tell-scan for genuine AI tells; no format restructuring, no killer #2. Leave the shape alone.
• Essay / other prose: tells only, no format mandate.

When unsure whether a doc is relational, treat it as relational. A missed bullet costs nothing; a flattened relationship costs a lot.

If a target arrives labeled "analysis deliverable" but is actually a relational message (an email, a talk track), downgrade and say so.

Reader card

The cold editor is isolated from the drafting turns, not from knowing who the reader is. Assemble from the project — not from the drafting conversation:

• Reader(s) and relationship — who receives this, their role
• Shared history / state — what's already been agreed or parked
• Register and sensitivities — formality, what's politically live
• Protected framing — moves to preserve: collaborative deference, air-cover language, softened asks, shared-history callbacks. To a blind editor these read as "hedging"; they are the point. Do not flag or strip them.

For analysis deliverables the reader card is lighter (audience + register). For relational messages it is mandatory and load-bearing.

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Step 1 — Mechanical tell-scan (deterministic floor)

Run your tell-scan against the target file:

{tell_scan} ""

It should emit file:line: [HIGH|flag] category: "match" — hint for each hit and exit non-zero if any HIGH-severity tell is present. Capture the output — it seeds Step 2.

For a relational message, ignore format flags (fact-stack, paragraph-wall). Act only on tell flags (cadence, vague-observer, journalese). The HIGH-severity tell gate still applies.

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Step 2 — Fresh-eyes editor pass (judgment)

Spawn a cold editor subagent at Opus tier. Pass it:

• The draft text
• The reader card (from Step 0)
• The genre and which rules are ON
• The Step 1 scan output
• Not the drafting conversation

Pick the prompt template by genre.

Full scrub (analysis deliverable)

> You are a ruthless copy editor. You did not write the document below and have no attachment to its phrasing. Your only job is to find and fix the prose failures, then rewrite it clean. > > Reader card (who this is for — preserve what it protects): {paste reader card — readers, relationship, shared history, register, protected-framing list}. The protected-framing items are NOT tells. Do not flag or strip collaborative deference, air cover, ownership language, or shared-history callbacks. Fix the prose for this reader, not an imaginary generic one. > > Target format: {paste the format spec for the relevant document type — BLUF first, bold claim-leads, fact-stacking paragraphs become bullet lists, ≤2 non-lead sentences per insight paragraph, etc.}. > > The six recurring killers to hunt: {paste the six killers below}. > > Also apply these judgment checks: fix manufactured "we" in solo analysis (the analyst's own actions are "I", not "we"); fix unfaithful metric labels (but preserve your domain vocabulary canon — accepted terms stay exactly as written, never relabeled to "plainer" substitutes); flag undefined corpus or method terms for inline definition. > > PRESERVE domain vocabulary: the accepted terms in {domain_vocab} stay exactly as written. The faithful-label fixes apply only to genuine misuse, never to accepted funnel-stage or measurement labels. > > Structural mandate (enforce it): any paragraph carrying ≥2 discrete facts becomes a bulleted or numbered list with bold leads. No insight paragraph beyond the bold claim-lead may exceed 2 sentences (anticipated-question answers and context sections excepted). A clean sentence inside a fact-stacking paragraph is still a format failure — restructure it. "Don't over-rewrite" applies to wording, not format. > > A mechanical scan already flagged these lines (fix every one): {paste Step 1 output}. > > Return exactly three things: > 1. Structure map — per section: heading, paragraph count, max sentences in any non-lead paragraph, bullet count. Flag every section with a non-lead paragraph over 2 sentences or with fact-stacking prose and no bullets. > 2. Findings — a line-referenced list: line N — — . Include judgment tells the scan can't catch (smuggled framing, weak metaphor, buried lede, jargon the reader can't parse). > 3. Rewrite — the full document in the target format: scannable, plain English, every killer removed, every fact-stacking paragraph turned into bullets, every number preserved exactly. Do not invent numbers or claims. Do not soften an honest limit — state it straight in a "Yes, but." block. > > Plain readability is the bar, not merely tell-free prose. Read every sentence as if saying it to a colleague out loud; if it's awkward, rephrase it.

Light scrub (relational message)

> You are a careful copy editor. You did not write the message below. Hunt ONLY the genuine AI tells and fix them in place: "X, not Y" cadence, vague-observer attribution ("it reads as," "appears to"), journalese metaphor, and importance-claims with no specific. Everything else is light touch. > > Reader card (who this is for): {paste reader card}. This is a relational message — its job is to carry an ask and a relationship, not to score on format. Do NOT bullet-ify paragraphs, do NOT impose a structural template or a word cap, do NOT flag or strip collaborative deference, air cover, softened asks, or shared-history callbacks. Those are the point. The format flags in the scan output are noise here; act only on genuine tell flags. > > A mechanical scan already flagged these lines: {paste Step 1 output}. > > Return two things: > 1. Findings — a line-referenced list of genuine tells only. > 2. Rewrite — the same message with those tells fixed and everything else (tone, structure, framing, length) left intact. If a sentence already reads like something a person would say to this reader, leave it.

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The six killers

1. "X, not Y" negation cadence — "a genuine change, not just a strong week"; "the exception, not the rule"; "rather than," "more than a." One contrast is fine; the reflex is the tell. Rewrite as a plain declarative.
2. Smuggled framing — context that states the conclusion as a bare fact before the body earns it; a bottom line that asserts a read a later section walks back. (Analysis deliverables only — OFF for relational prose.)
3. Vague-observer attribution — "it reads as," "that looks like," "appears to." Name the view or state the fact.
4. Internal-analyst shorthand as a bare noun — "the frame," "the lever," "the cut," "the angle," "the engine." Say the thing.
5. Journalese metaphor reaching for vividness — "shrinking pie," "its slice," "left on the table," "did the work," "a tide."
6. Importance-claim without the specific — the fact that makes it matter, not "this matters / underscores."

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Step 3 — Reconcile and present (gate)

Read the subagent's findings and rewrite. Sanity-check: every number and claim preserved, no fabricated evidence, honest limits still present, format on target. Present findings + rewrite to the user. Wait for approval before writing.

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Step 4 — Apply and re-scan

On approval, write the rewrite. Re-run Step 1 on the saved file. It must come back with zero HIGH-severity hits. A remaining hit means the rewrite reintroduced a tell — fix and re-scan until clean. For relational messages the zero-HIGH-tell bar still holds; format flags stay ignored.

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Configuration

| Parameter | Description | |-----------|-------------| | {tell_scan} | Path to your mechanical tell-scan script or grep list | | {style_guide} | Path to your canonical voice and vocabulary guide | | {domain_vocab} | Your accepted domain metric vocabulary file | | {workspace} | Root path for your notes or project files |

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Model

Run the orchestration on the session's normal model. The editor subagent must be Opus-tier — this is fresh-eyes delegation for quality, not token savings. Do not route it to a smaller model.

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Notes

• This skill does not change interpretation. It fixes how the prose reads, never what it claims. If the editor believes a claim is wrong (not just badly phrased), it surfaces that as a finding — it does not silently rewrite the read.
• The scrub does not relocate caveats. They stay where the document's conventions put them (a "Yes, but." block inline, or a supporting section at the back). The scrub ensures they're stated plainly, not buried or softened.
• Wording restraint and format enforcement are different axes. "Don't over-rewrite" applies to wording. A clean sentence inside a fact-stacking paragraph is still a wall — breaking it into bullets is mandatory, not "churning."

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kn0wsnothing
• Source: kn0wsnothing/agentic-ai-skills-and-tools
• License: MIT

Install and usage instructions live in the source repository linked above.