Merge pull request #7 from JuliusBrussee/claude/deslopify-skill-rewrite-zwtcvy

Tighten deslopify prose and clarify detection workflow

skills/deslopify/SKILL.md

@@ -1,26 +1,27 @@
 ---
 name: deslopify
 description: >
-  De-slop pass for any text: detects and erases the statistical fingerprints of
-  AI writing (negative parallelism / "not X but Y", em-dash abuse, rule-of-three,
-  false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing) and
-  rewrites the text into its target register — academic article, tweet, reddit
-  post, email, blog, anything between. Use when the user says "deslopify",
-  "deslop", "de-slop this", "remove the AI tells", "humanize this",
-  "make this not sound like AI", or invokes /deslopify. Also use before
-  publishing any agent-drafted prose.
+  De-slop pass for any text. Scans for the statistical fingerprints of AI
+  writing (negative parallelism / "not X but Y", em-dash density,
+  rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged
+  both-sidesing), rewrites by meaning, then re-scans until the text is clean
+  and sits in the right register for its genre: academic article, tweet,
+  reddit post, email, blog, docs, marketing. Use when the user says
+  "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize
+  this", "make this not sound like AI", or invokes /deslopify. Also use
+  before publishing any agent-drafted prose.
 ---
 
 # Deslopify
 
-Strip every mark of AI writing from a text and make it good in its genre. Not "make it pass a detector" — make it read like a specific person with a specific point wrote it for a specific audience.
+Strip the AI fingerprints out of a text and make it good in its genre. The target is prose that reads like one person wrote it for one audience about something they actually know. Detector scores are beside the point; text can score human and still be unreadable.
 
-## Why this is a loop, not a style guide
+## Why this loops
 
-The worst tells — above all the **"not X but Y"** family — are not vocabulary mistakes. They are emergent properties of how LLMs generate text: preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move is baked into the model's priors. Two consequences drive this skill's architecture:
+The "not X but Y" family and its relatives are generative habits. Preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move sits deep in the model's priors and surfaces about once a paragraph. Two consequences shape this skill:
 
-1. **You cannot reliably see your own slop.** The same priors that produce the pattern make it invisible on re-read. Detection must be mechanical — regex against a fixed catalog — never "does this look AI to me?"
-2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and it produces "this is less about X than Y" — the same move in a wig. So every rewrite gets re-scanned, and the loop runs until the scan is clean.
+1. **You cannot see your own slop.** The priors that produce the pattern also make it invisible on re-read. So detection runs mechanically, as regex against a fixed catalog. "Does this look AI to me?" is not a detection method.
+2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and out comes "this is less about X than Y", the same move in a wig. Every rewrite therefore gets re-scanned, and the loop runs until a scan comes back clean.
 
 Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.**
 
@@ -28,59 +29,59 @@ Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat)
 
 Before touching the text, establish:
 
-- **Genre and venue** — academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. If not stated and not obvious from the text, ask. Genre decides which tells are fatal and what "good" means; see [references/voices.md](references/voices.md).
-- **Audience and stance** — who reads it, and what the author actually claims. Slop is what fills the space where a claim should be; you cannot remove it without knowing the claim.
-- **Constraints** — length limits, required citations, house style.
+- **Genre and venue.** Academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. Ask when it isn't stated and isn't obvious from the text. Genre decides which tells are fatal and what "good" means; see [references/voices.md](references/voices.md).
+- **Audience and stance.** Who reads it, and what the author actually claims. Slop fills the space where a claim should be, so you cannot remove it without knowing the claim.
+- **Constraints.** Length limits, required citations, house style.
 
 ## Phase 1: Mechanical scan
 
-Run the detection patterns from [references/tells.md](references/tells.md) against the text. If the text is in a file (or you can write it to a temp file), run the grep commands in that reference literally — the catalog is written as runnable `grep -Ein` patterns. Otherwise apply each pattern by hand, line by line.
+Run the detection patterns from [references/tells.md](references/tells.md) against the text. If the text is in a file, or you can write it to a temp file, run the grep commands in that reference literally: the catalog is written as runnable `grep -Ein` patterns. Otherwise apply each pattern by hand, line by line.
 
-Produce a finding list: line/sentence, matched pattern, tell category. Also run the two structural checks that regex can't fully catch:
+Produce a finding list: line or sentence, matched pattern, tell category. Then run the two structural checks regex can't catch:
 
-- **Cadence**: flag any run of 3+ consecutive sentences within ±4 words of the same length, and any paragraph where every sentence has the same shape (subject–verb–elaboration).
-- **Formatting**: bold scattered through prose, emoji-decorated headers or bullets, "**Term:** definition" bullet lists, headers on a text too short to need them, a tidy intro–three-points–conclusion skeleton.
+- **Cadence.** Flag any run of 3+ consecutive sentences within ±4 words of the same length, and any paragraph where every sentence has the same shape (subject, verb, elaboration).
+- **Formatting.** Bold scattered through prose, emoji-decorated headers or bullets, "**Term:** definition" bullet lists, headers on a text too short to need them, a tidy intro-three-points-conclusion skeleton.
 
-Report the findings to the user as a short table before rewriting (category, count, worst example). This is the diagnosis; the user should see what was wrong.
+Report the findings to the user as a short table before rewriting: category, count, worst example. The user should see the diagnosis.
 
-## Phase 2: Rewrite by meaning, not by frame
+## Phase 2: Rewrite by meaning
 
-Go finding by finding. The cardinal rule: **never fix a pattern by paraphrasing the pattern.** Fix it by deciding what the sentence actually asserts, then asserting that.
+Go finding by finding. The cardinal rule: never fix a pattern by paraphrasing the pattern. Decide what the sentence asserts, then assert that.
 
-### The "not X but Y" family — three-way triage
+### The "not X but Y" family: three-way triage
 
 Every negative parallelism gets exactly one of these treatments:
 
-1. **The negation is a strawman** (nobody believes X). Delete the X half entirely and assert Y directly, with whatever evidence the text has.
+1. **Strawman negation** (nobody believes X). Delete the X half and assert Y directly, with whatever evidence the text has.
    - *"It's not just a tool, it's a fundamental shift in how teams work"* → *"Teams that adopted it stopped holding standups within a month."*
-2. **The contrast is real** (people genuinely hold X). Then earn it: name who holds X, say concretely why Y beats it. A real contrast survives being made specific; slop doesn't.
-3. **The sentence asserts nothing** (the contrast is decoration on an empty claim). Delete the whole sentence. Most cases are this one.
+2. **Real contrast** (people genuinely hold X). Earn it: name who holds X, say concretely why Y beats it. A real contrast survives being made specific; slop doesn't.
+3. **Empty claim** (the contrast decorates a sentence that asserts nothing). Delete the sentence. Most cases are this one.
 
-Banned escape hatches — these are the same move and count as new findings: "less about X than Y", "X matters, but Y matters more", "the real X is Y", "the question isn't X, it's Y", "X? Y." (rhetorical-question variant), and the em-dash variant "— not X, but Y".
+Banned escape hatches, all the same move, all counted as new findings: "less about X than Y", "X matters, but Y matters more", "the real X is Y", "the question isn't X, it's Y", "X? Y." (the rhetorical-question variant), and the em-dash form "— not X, but Y".
 
 ### Everything else
 
-- **Puffery and inflated vocabulary** (pivotal, seismic, testament, tapestry, landscape, delve…): replace with the plain word, or with the concrete fact the puffery was hiding. "Plays a vital role in" → "does".
-- **Rule-of-three lists**: keep the strongest item, cut the rest — unless all three carry distinct information, in which case keep them and break the rhythm (different lengths, different syntax).
-- **False ranges** ("from X to Y"): if you can't name a meaningful midpoint between X and Y, it's not a range — name the two things or cut one.
-- **Hedged both-sidesing** ("it's worth noting", auto-counterpoints, "while X, it's also true that Y"): commit. One opinion, stated, owned. A counterpoint stays only if the author genuinely concedes it.
-- **Uniform cadence**: vary deliberately. Follow a long sentence with a short one. Fragments are legal. Don't apply a formula (alternating long/short is its own tell) — read the paragraph aloud and break wherever the rhythm is metronomic.
-- **Low specificity**: replace "many companies" / "studies show" / "recent research" with the actual names, numbers, and dates — **only from the source text, the conversation, or verifiable research you actually do**. Never invent specifics. If the author needs to supply one, leave a marked placeholder: `[ADD: which study?]`.
-- **Stock skeleton**: kill throat-clearing openers ("In today's fast-paced world…"), summary conclusions ("In conclusion… Ultimately…"), and engagement-bait endings ("What do you think?"). Start where the point starts; stop when it's made.
+- **Puffery and inflated vocabulary** (pivotal, seismic, testament, tapestry, landscape, delve…). Replace with the plain word, or with the concrete fact the puffery hides. "Plays a vital role in" → "does".
+- **Rule-of-three lists.** Keep the strongest item, cut the rest. Where all three carry distinct information, keep them and break the rhythm with different lengths and different syntax.
+- **False ranges** ("from X to Y"). If you can't name a meaningful midpoint between X and Y, name the two things plainly or cut one.
+- **Hedged both-sidesing** ("it's worth noting", auto-counterpoints, "while X, it's also true that Y"). Commit. One opinion, stated, owned. A counterpoint stays only where the author genuinely concedes it.
+- **Uniform cadence.** Vary deliberately. Follow a long sentence with a short one. Fragments are legal. Avoid formulas, since alternating long and short is its own tell; read the paragraph aloud and break wherever the rhythm goes metronomic.
+- **Low specificity.** Replace "many companies" / "studies show" / "recent research" with actual names, numbers, and dates, drawn **only** from the source text, the conversation, or research you actually do. Never invent specifics. Where the author has to supply one, leave a marked placeholder: `[ADD: which study?]`.
+- **Stock skeleton.** Kill throat-clearing openers ("In today's fast-paced world…"), summary conclusions ("In conclusion… Ultimately…"), and engagement-bait endings ("What do you think?"). Start where the point starts; stop when it's made.
 
-### What not to do — overcorrection is also slop
+### Overcorrection is also slop
 
 - No fake typos, forced slang, or manufactured "voice". Humanizer-tool output is its own genre of slop.
-- Em dashes are not banned. Humans use them. The tell is density and the double-dash "— not X, but —" move. Budget: at most one em dash per ~150 words, never two in a sentence.
-- Don't trade precision for personality in academic or technical text. There, de-slopping means cutting puffery and committing to claims — not adding attitude.
-- Preserve the author's meaning, claims, and facts exactly. This is a style pass, not a content edit. Flag, don't silently fix, anything that looks factually wrong.
+- Em dashes stay legal. Humans use them. The tell is density, plus the contrast form "— not X, but Y". Budget: at most one em dash per ~150 words, never two in a sentence.
+- Keep precision in academic and technical text. There, de-slopping means cutting puffery and committing to claims. Adding attitude makes it worse.
+- Preserve the author's meaning, claims, and facts exactly. This is a style pass. Flag anything that looks factually wrong rather than silently fixing it.
 
 ## Phase 3: Verify loop
 
-Re-run the full Phase 1 scan **on your rewritten text**. This step is not optional and not a formality — expect your own rewrite to contain new tells, because the model writing it has the same priors that created them. Fix and re-scan until a pass produces zero pattern hits and the cadence check passes. Cap at 4 passes; if a pattern survives 4 passes, rewrite that sentence from scratch starting from its bare claim ("what fact or opinion is this sentence for?").
+Re-run the full Phase 1 scan on your rewritten text. Expect the rewrite to carry fresh tells, because the model producing it has the same priors that produced the originals. Skipping this step is how slop survives the pass. Fix and re-scan until one pass returns zero pattern hits and the cadence check passes. Cap at 4 passes. If a pattern survives 4 passes, rewrite that sentence from scratch, starting from its bare claim: what fact or opinion is this sentence for?
 
 ## Phase 4: Register check
 
-Check the clean text against its genre profile in [references/voices.md](references/voices.md): right length, right formality, right person, genre-specific tells gone (e.g. on reddit: no bold, no bullet essay; in academic prose: no first-person hot takes added). Then the final test — read it aloud. Anywhere you wouldn't say it to the actual audience, rewrite that sentence.
+Check the clean text against its genre profile in [references/voices.md](references/voices.md): right length, right formality, right person, genre-specific tells gone. On reddit that means no bold and no bullet essay. In academic prose it means no first-person hot takes added. Then read it aloud. Anywhere you wouldn't say it to the actual audience, rewrite that sentence.
 
-Deliver: the rewritten text, plus a brief change log (categories fixed, counts, and number of verify passes it took).
+Deliver the rewritten text and a short change log: categories fixed, counts, and how many verify passes it took.