| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "Her voice cracked like [like]" |
| | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.167 | | effectiveRatio | 0.125 | |
| 89.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1449 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "quickly" | | 2 | "truly" |
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| 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) | |
| 10.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1449 | | totalAiIsms | 26 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "fractured" | | 1 | "pounding" | | 2 | "jaw clenched" | | 3 | "flickered" | | 4 | "gloom" | | 5 | "glinting" | | 6 | "sinewy" | | 7 | "footsteps" | | 8 | "echoed" | | 9 | "standard" | | 10 | "shattered" | | 11 | "pulse" | | 12 | "chilled" | | 13 | "glint" | | 14 | "velvet" | | 15 | "charm" | | 16 | "dancing" | | 17 | "tapestry" | | 18 | "flicked" | | 19 | "familiar" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1427 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1269 | | uniqueNames | 17 | | maxNameDensity | 0.87 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | London | 2 | | Station | 1 | | Morris | 3 | | Soho | 2 | | God | 1 | | Camden | 1 | | Spiderweb | 2 | | Veil | 1 | | Market | 3 | | Saint | 1 | | Christopher | 1 | | Herrera | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Morris" | | 4 | "Camden" | | 5 | "Spiderweb" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Herrera" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Soho" | | 3 | "Market" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 2 | | matches | | 0 | "quite hold them" | | 1 | "as if grinding teeth" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.701 | | wordCount | 1427 | | matches | | 0 | "Not just the slap of a single set of shoes, but the strange hum of life below the city’s skin" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 38.57 | | std | 22.94 | | cv | 0.595 | | sampleLengths | | 0 | 90 | | 1 | 60 | | 2 | 57 | | 3 | 66 | | 4 | 10 | | 5 | 53 | | 6 | 3 | | 7 | 86 | | 8 | 54 | | 9 | 64 | | 10 | 36 | | 11 | 69 | | 12 | 56 | | 13 | 62 | | 14 | 9 | | 15 | 18 | | 16 | 29 | | 17 | 21 | | 18 | 15 | | 19 | 39 | | 20 | 46 | | 21 | 28 | | 22 | 13 | | 23 | 20 | | 24 | 80 | | 25 | 48 | | 26 | 54 | | 27 | 31 | | 28 | 17 | | 29 | 26 | | 30 | 21 | | 31 | 28 | | 32 | 22 | | 33 | 23 | | 34 | 37 | | 35 | 26 | | 36 | 10 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 99 | | matches | | 0 | "was gone" | | 1 | "was built" | | 2 | "was supposed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 231 | | matches | | 0 | "wasn’t just running" | | 1 | "was choosing" | | 2 | "was really crawling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 21 | | semicolonCount | 4 | | flaggedSentences | 21 | | totalSentences | 109 | | ratio | 0.193 | | matches | | 0 | "The city offered no mercy—every surface slick, every shadow threatening—but she drove herself on anyway, jaw clenched with a purpose so sharp it bruised bone." | | 1 | "But then she glimpsed a movement—there, beyond the mouth of the alley, a silhouette vaulting the rusted rail at the old station stairs." | | 2 | "The suspect—tall, sinewy, face mostly hidden beneath a hood—flung himself down the chipped steps that once led commuters to daylight." | | 3 | "Eighteen years as a cop, most of them in London’s strangest writhings—yet nothing had ever rattled her quite like the Station." | | 4 | "But the path her mark had taken—he wasn’t just running." | | 5 | "At the bottom, mildew seeped into her lungs and the only light came from her torch, which turned every surface into a study of ruin—cracked tiles, shattered glass, a museum of graffiti and decay." | | 6 | "Rainwater dripped from forgotten archways, and in the distance, she heard—no, felt—movement." | | 7 | "She recognized the object from rumour, the kind of whispers she usually dismissed as pub nonsense—a bone token, the supposed key to London's most dangerous secret." | | 8 | "At the far side of the platform, the tiles glimmered wetly around a ladder leading down—fresh scuffs, the trail too recent to ignore." | | 9 | "If she balked now, she’d never get another chance to stop the pattern—to figure out what was really crawling through her city’s veins." | | 10 | "The narrow corridor buzzed with sound—voices, laughter, the pulse of commerce somewhere ahead." | | 11 | "Beyond, two figures flanked by battered torches blocked the way: one tall, androgynous, with a spiderweb tattoo climbing up one cheek; the other smaller, eyes yellow in the lamplight, arms folded tight." | | 12 | "A shout rose up from inside—a woman’s voice, urgent, muffled by distance and the thick market air." | | 13 | "Stalls lined the platform walls, manned by merchants with eyes too dark, too knowing; the air reeked of petrol, incense, and something faintly sweet, like cut grass after a slaughter." | | 14 | "Nothing here was built for her eyes—sigils scrawled on cardboard, wares wrapped in velvet, teeth sold from battered trays." | | 15 | "There—a flash of the windbreaker over by a stall bristling with old radios." | | 16 | "He moved with a paramedic’s grace, even here; she clocked his olive skin, the Saint Christopher medallion dancing at his throat, a scar slicing his left forearm." | | 17 | "Her throat tightened: Herrera was supposed to be off the board—license revoked, reputation ruined." | | 18 | "She looked past him—beyond, the Market stretched on, coiling and strange." | | 19 | "Here, anything could happen; here, the old ghost of Morris whispered warnings she barely understood." | | 20 | "But retreat wasn’t in her nature, and if Herrera was right—if someone truly dangerous was moving among the stalls—she’d be damned if she left without answers." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 233 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.02145922746781116 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 13.09 | | std | 7.17 | | cv | 0.548 | | sampleLengths | | 0 | 23 | | 1 | 23 | | 2 | 19 | | 3 | 25 | | 4 | 17 | | 5 | 20 | | 6 | 23 | | 7 | 3 | | 8 | 21 | | 9 | 20 | | 10 | 13 | | 11 | 6 | | 12 | 22 | | 13 | 21 | | 14 | 6 | | 15 | 11 | | 16 | 10 | | 17 | 5 | | 18 | 4 | | 19 | 10 | | 20 | 10 | | 21 | 7 | | 22 | 17 | | 23 | 3 | | 24 | 13 | | 25 | 34 | | 26 | 7 | | 27 | 12 | | 28 | 20 | | 29 | 8 | | 30 | 18 | | 31 | 28 | | 32 | 2 | | 33 | 7 | | 34 | 26 | | 35 | 14 | | 36 | 15 | | 37 | 13 | | 38 | 23 | | 39 | 4 | | 40 | 23 | | 41 | 4 | | 42 | 15 | | 43 | 23 | | 44 | 11 | | 45 | 9 | | 46 | 13 | | 47 | 23 | | 48 | 15 | | 49 | 32 |
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| 71.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.43119266055045874 | | totalSentences | 109 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 97 | | matches | | 0 | "Then a pale glimmer caught" | | 1 | "Quickly, she pulled out her" | | 2 | "Then Spiderweb nodded." | | 3 | "Somewhere far off, sirens haunted" |
| | ratio | 0.041 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 97 | | matches | | 0 | "She cut east, boots splashing" | | 1 | "She leaped the curb and" | | 2 | "He vanished into the long-abandoned" | | 3 | "It painted her scalp with" | | 4 | "She flexed her right hand," | | 5 | "She should call backup, wait" | | 6 | "He was choosing this place," | | 7 | "She pressed on." | | 8 | "She followed, torch beam cutting" | | 9 | "She recognized the object from" | | 10 | "She remembered the way they’d" | | 11 | "She moved forward, legs steady" | | 12 | "She followed, feeling the pressure" | | 13 | "They eyed her up and" | | 14 | "He nodded to the bone" | | 15 | "She folded her arms, damp" | | 16 | "It carried a note that" | | 17 | "She edged along the main" | | 18 | "She pressed on, searching for" | | 19 | "He moved with a paramedic’s" |
| | ratio | 0.299 | |
| 99.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 97 | | matches | | 0 | "Detective Harlow Quinn spotted her" | | 1 | "The night pressed in, all" | | 2 | "She cut east, boots splashing" | | 3 | "The city offered no mercy—every" | | 4 | "Quinn didn’t hesitate." | | 5 | "She leaped the curb and" | | 6 | "The suspect—tall, sinewy, face mostly" | | 7 | "He vanished into the long-abandoned" | | 8 | "It painted her scalp with" | | 9 | "She flexed her right hand," | | 10 | "She should call backup, wait" | | 11 | "He was choosing this place," | | 12 | "She pressed on." | | 13 | "The descent narrowed, air thick" | | 14 | "The platforms stretched ahead, swallowed" | | 15 | "Rainwater dripped from forgotten archways," | | 16 | "A burst of footsteps came" | | 17 | "She followed, torch beam cutting" | | 18 | "Sweat slicked her palms despite" | | 19 | "She recognized the object from" |
| | ratio | 0.722 | |
| 51.55% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 97 | | matches | | 0 | "If she balked now, she’d" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn spotted her mark rounding the corner, slipping through a haze of silver rain under the jaundiced glow of a streetlamp." | | 1 | "At the bottom, mildew seeped into her lungs and the only light came from her torch, which turned every surface into a study of ruin—cracked tiles, shattered gla…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "He nodded, as if to clarify the rules" | | 1 | "he said, voice pitched low" |
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| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 16 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0.333 | | effectiveRatio | 0.125 | |