| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1037 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 71.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1037 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "shattered" | | 1 | "flickered" | | 2 | "loomed" | | 3 | "footsteps" | | 4 | "echoing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 110 | | matches | | 0 | "was afraid" | | 1 | "h with fear" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1047 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1042 | | uniqueNames | 13 | | maxNameDensity | 0.96 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 10 | | Ferryman | 2 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Raven | 1 | | Nest | 1 | | Camden | 2 | | Transport | 1 | | Morris | 2 | | Thames | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Ferryman" | | 3 | "Raven" | | 4 | "Morris" |
| | places | | 0 | "London" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Camden" | | 5 | "Thames" |
| | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a disused railway arch" | | 1 | "something like this" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.955 | | wordCount | 1047 | | matches | | 0 | "not a door that had been cut into the wall, but a door" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 29.91 | | std | 20.79 | | cv | 0.695 | | sampleLengths | | 0 | 60 | | 1 | 39 | | 2 | 62 | | 3 | 3 | | 4 | 14 | | 5 | 48 | | 6 | 39 | | 7 | 59 | | 8 | 6 | | 9 | 2 | | 10 | 46 | | 11 | 5 | | 12 | 56 | | 13 | 3 | | 14 | 53 | | 15 | 12 | | 16 | 9 | | 17 | 47 | | 18 | 6 | | 19 | 39 | | 20 | 38 | | 21 | 15 | | 22 | 66 | | 23 | 9 | | 24 | 43 | | 25 | 22 | | 26 | 46 | | 27 | 22 | | 28 | 61 | | 29 | 13 | | 30 | 8 | | 31 | 42 | | 32 | 17 | | 33 | 11 | | 34 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 165 | | matches | | 0 | "was running" | | 1 | "was going" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 112 | | ratio | 0.071 | | matches | | 0 | "The suspect's silhouette flickered through the alley — lean, precise, knowing every dip and stumble of the pavement." | | 1 | "Her knees protested — forty-one years of chasing ghosts and dead ends had taken their toll — but the adrenaline burned clean and hot." | | 2 | "An unsettling, total calm — the stillness of a man who knew the next twenty minutes better than she knew the next five." | | 3 | "The beam cut through the dark and caught the top of the suspect's head — his wet hair plastered to his skull — as he disappeared around a corner at the bottom of the stairwell." | | 4 | "Then a door — not a door that had been cut into the wall, but a door that had always been there, hidden behind the world she knew." | | 5 | "No signal down here — no reception at all, not even static." | | 6 | "He'd been investigating the same circles she'd been treading for weeks — the circles that led to underground markets and bone tokens and doors that weren't doors." | | 7 | "She stumbled through —" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 290 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.02413793103448276 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.010344827586206896 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 9.35 | | std | 7.08 | | cv | 0.757 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 19 | | 3 | 7 | | 4 | 17 | | 5 | 5 | | 6 | 10 | | 7 | 14 | | 8 | 8 | | 9 | 18 | | 10 | 9 | | 11 | 13 | | 12 | 3 | | 13 | 6 | | 14 | 3 | | 15 | 5 | | 16 | 9 | | 17 | 12 | | 18 | 4 | | 19 | 23 | | 20 | 2 | | 21 | 24 | | 22 | 13 | | 23 | 11 | | 24 | 3 | | 25 | 14 | | 26 | 2 | | 27 | 12 | | 28 | 6 | | 29 | 1 | | 30 | 10 | | 31 | 6 | | 32 | 2 | | 33 | 18 | | 34 | 2 | | 35 | 2 | | 36 | 1 | | 37 | 23 | | 38 | 3 | | 39 | 2 | | 40 | 6 | | 41 | 17 | | 42 | 8 | | 43 | 12 | | 44 | 13 | | 45 | 3 | | 46 | 14 | | 47 | 3 | | 48 | 15 | | 49 | 21 |
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| 53.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.4017857142857143 | | totalSentences | 112 | | uniqueOpeners | 45 | |
| 71.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 93 | | matches | | 0 | "Of course he didn't stop." | | 1 | "Then a door — not" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 93 | | matches | | 0 | "She ran hard, breath fogging" | | 1 | "She'd been tracking him for" | | 2 | "Her coat whipped behind her," | | 3 | "He wasn't running like a" | | 4 | "He was running like a" | | 5 | "Her voice came out clipped," | | 6 | "He didn't stop." | | 7 | "He vaulted a low wall" | | 8 | "Her knees protested — forty-one" | | 9 | "She cleared the wall and" | | 10 | "She'd never set foot inside," | | 11 | "He ran north." | | 12 | "She heard it then." | | 13 | "She reached the bottom and" | | 14 | "It stank of mould and" | | 15 | "He had stopped running." | | 16 | "He stood half-turned, his face" | | 17 | "He raised one hand and" | | 18 | "He held it up to" | | 19 | "Her hand went to the" |
| | ratio | 0.29 | |
| 56.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 93 | | matches | | 0 | "The rain hammered London in" | | 1 | "She ran hard, breath fogging" | | 2 | "She'd been tracking him for" | | 3 | "A wiry man with a" | | 4 | "Nothing about this case had" | | 5 | "Quinn vaulted a tipped-over bicycle," | | 6 | "Her coat whipped behind her," | | 7 | "The suspect's silhouette flickered through" | | 8 | "He wasn't running like a" | | 9 | "He was running like a" | | 10 | "Her voice came out clipped," | | 11 | "He didn't stop." | | 12 | "The alley spat them out" | | 13 | "Traffic snarled at the junction," | | 14 | "The suspect didn't hesitate." | | 15 | "He vaulted a low wall" | | 16 | "Her knees protested — forty-one" | | 17 | "She cleared the wall and" | | 18 | "The church grounds opened onto" | | 19 | "The Raven's Nest." |
| | ratio | 0.806 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 51.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 6 | | matches | | 0 | "The rain hammered London in sheets, turning the streets into black mirrors that shattered under Harlow Quinn's boots." | | 1 | "Witnesses who recanted under oath with fear in their eyes." | | 2 | "In that brief flash of face under the green light, Quinn saw something that made her falter mid-stride." | | 3 | "An unsettling, total calm — the stillness of a man who knew the next twenty minutes better than she knew the next five." | | 4 | "He held it up to the wall, where a section of brick that looked no different from any other section of brick seemed to shimmer." | | 5 | "He'd been investigating the same circles she'd been treading for weeks — the circles that led to underground markets and bone tokens and doors that weren't door…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |