| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.171 | | leniency | 0.343 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1406 | | 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) | |
| 14.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1406 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "glinting" | | 1 | "hulking" | | 2 | "perfect" | | 3 | "loomed" | | 4 | "churned" | | 5 | "echo" | | 6 | "footsteps" | | 7 | "flickered" | | 8 | "flicked" | | 9 | "gloom" | | 10 | "weight" | | 11 | "etched" | | 12 | "depths" | | 13 | "gleaming" | | 14 | "scanned" | | 15 | "pulse" | | 16 | "echoes" | | 17 | "pulsed" | | 18 | "electric" | | 19 | "throbbed" | | 20 | "crystallized" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 214 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 214 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 242 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1353 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 99 | | wordCount | 1157 | | uniqueNames | 30 | | maxNameDensity | 1.82 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 21 | | Raven | 3 | | Nest | 3 | | Tomás | 18 | | Herrera | 5 | | Saint | 1 | | Christopher | 1 | | Greek | 1 | | Street | 4 | | Meard | 1 | | Oxford | 2 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Fitzrovia | 1 | | Euston | 1 | | Market | 5 | | Morris | 2 | | English | 1 | | Eastern | 1 | | European | 1 | | Eyes | 1 | | Figures | 1 | | Rain | 4 | | Camden | 3 | | Radio | 4 | | Torch | 3 | | Crowd | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Market" | | 9 | "Morris" | | 10 | "Eyes" | | 11 | "Figures" | | 12 | "Rain" | | 13 | "Camden" | | 14 | "Radio" | | 15 | "Torch" | | 16 | "Crowd" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "Meard" | | 4 | "Oxford" | | 5 | "Tottenham" | | 6 | "Court" | | 7 | "Road" | | 8 | "Fitzrovia" | | 9 | "Euston" |
| | globalScore | 0.592 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1353 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 242 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 90 | | mean | 15.03 | | std | 9.02 | | cv | 0.6 | | sampleLengths | | 0 | 46 | | 1 | 25 | | 2 | 9 | | 3 | 35 | | 4 | 38 | | 5 | 14 | | 6 | 26 | | 7 | 9 | | 8 | 36 | | 9 | 23 | | 10 | 34 | | 11 | 13 | | 12 | 30 | | 13 | 6 | | 14 | 8 | | 15 | 33 | | 16 | 17 | | 17 | 7 | | 18 | 32 | | 19 | 21 | | 20 | 20 | | 21 | 19 | | 22 | 20 | | 23 | 25 | | 24 | 22 | | 25 | 4 | | 26 | 12 | | 27 | 10 | | 28 | 14 | | 29 | 24 | | 30 | 22 | | 31 | 20 | | 32 | 26 | | 33 | 21 | | 34 | 10 | | 35 | 5 | | 36 | 3 | | 37 | 20 | | 38 | 10 | | 39 | 29 | | 40 | 15 | | 41 | 20 | | 42 | 13 | | 43 | 20 | | 44 | 8 | | 45 | 16 | | 46 | 21 | | 47 | 14 | | 48 | 7 | | 49 | 7 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 214 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 295 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 37 | | semicolonCount | 1 | | flaggedSentences | 34 | | totalSentences | 242 | | ratio | 0.14 | | matches | | 0 | "Military precision snapped her strides into rhythm—left foot, right, coat flapping like a broken wing." | | 1 | "No hesitation—he pumped legs harder, darted across traffic." | | 2 | "Shadow swallowed him—gone around the bend." | | 3 | "Soho's maze blurred—pub fronts shuttered, umbrellas inverted in wind." | | 4 | "Eighteen years on the force forged this—pursuit like breathing." | | 5 | "Tomás hit the tube entrance stairs—Tottenham Court Road station—but veered left, not down." | | 6 | "Camden loomed—markets shuttered, locks Market's stalls dripping." | | 7 | "He faltered half-second—name hit home?—then vaulted another fence, into the abandoned yard." | | 8 | "Camden's underbelly beckoned—graffiti-slashed walls, derelict arches." | | 9 | "No—boards pried loose, swinging like broken teeth." | | 10 | "Faint echo—footsteps?" | | 11 | "Partner's ghost flickered in mind—DS Morris, vanished three years back in shadows like these." | | 12 | "Beam pierced gloom—steps slick with slime, descending spiral into earth." | | 13 | "Follow into the pit—unfamiliar, rumours of squatters, worse." | | 14 | "Footprint gleamed wet at bottom—his boot tread." | | 15 | "Air thickened—dank, laced with incense, ozone." | | 16 | "Accents tangled—Eastern European, guttural clicks, sibilants." | | 17 | "Jars rattled—something inside thrashed." | | 18 | "Her brown eyes scanned exits—three tunnels, all black." | | 19 | "Vendor locked gaze—pale face, black teeth." | | 20 | "Herrera had flashed bone at entry—saw it now, tucked in his palm." | | 21 | "Crowd pressed—elbow from clawed hand, hiss from scaled lips." | | 22 | "Tomás slipped payment—glinting coin?—and plunged right tunnel." | | 23 | "Stall brushed arm; fur brushed back." | | 24 | "Echoes bounced—drips, murmurs, a scream cut short." | | 25 | "Guards materialized—two hulks, eyes milky." | | 26 | "But partner’s face flashed—swallowed by dark like this." | | 27 | "Market sprawled endless—alcoves peddling dreams in bottles, futures in entrails." | | 28 | "A stall rat—boy with gills—tugged her coat." | | 29 | "Eyes sharpened—unnatural gleam." | | 30 | "Visions flickered—ghosts?" | | 31 | "Crowd surged—tails lashed ankles, claws grazed skin." | | 32 | "Figures closed—clique mates?" | | 33 | "Not guns—crossbows?" |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1206 | | adjectiveStacks | 2 | | stackExamples | | 0 | "against rain-slick skin." | | 1 | "under jury-rigged lanterns." |
| | adverbCount | 22 | | adverbRatio | 0.01824212271973466 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0024875621890547263 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 242 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 242 | | mean | 5.59 | | std | 3.51 | | cv | 0.628 | | sampleLengths | | 0 | 10 | | 1 | 19 | | 2 | 17 | | 3 | 6 | | 4 | 19 | | 5 | 3 | | 6 | 6 | | 7 | 2 | | 8 | 10 | | 9 | 15 | | 10 | 8 | | 11 | 11 | | 12 | 12 | | 13 | 8 | | 14 | 2 | | 15 | 5 | | 16 | 5 | | 17 | 6 | | 18 | 3 | | 19 | 4 | | 20 | 10 | | 21 | 6 | | 22 | 6 | | 23 | 9 | | 24 | 5 | | 25 | 14 | | 26 | 4 | | 27 | 7 | | 28 | 6 | | 29 | 7 | | 30 | 9 | | 31 | 7 | | 32 | 11 | | 33 | 9 | | 34 | 14 | | 35 | 9 | | 36 | 2 | | 37 | 2 | | 38 | 4 | | 39 | 9 | | 40 | 17 | | 41 | 6 | | 42 | 3 | | 43 | 5 | | 44 | 8 | | 45 | 5 | | 46 | 13 | | 47 | 7 | | 48 | 5 | | 49 | 4 |
| |
| 91.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5537190082644629 | | totalSentences | 242 | | uniqueOpeners | 134 | |
| 19.16% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 174 | | matches | | 0 | "Instead, sprawl of stalls under" |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 174 | | matches | | 0 | "She cursed under her breath," | | 1 | "Her worn leather watch ticked" | | 2 | "He glanced back, warm brown" | | 3 | "He didn't break stride." | | 4 | "She rounded the corner, glimpsed" | | 5 | "He cut north, towards Oxford" | | 6 | "He threw a look over" | | 7 | "She powered through, collarbone throbbing." | | 8 | "She holstered pistol, freed hands" | | 9 | "He plunged into the locks," | | 10 | "He faltered half-second-name hit home?—then" | | 11 | "She hurdled after, wire snagging" | | 12 | "He squeezed through, vanished into" | | 13 | "She peered into black." | | 14 | "Her watch glowed 00:47." | | 15 | "She flicked torch on." | | 16 | "Her brown eyes scanned exits—three" | | 17 | "She patted pockets." | | 18 | "He glanced back, eyes wide." | | 19 | "She closed in." |
| | ratio | 0.161 | |
| 40.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 146 | | totalSentences | 174 | | matches | | 0 | "Rain lashed the Soho streets," | | 1 | "Detective Harlow Quinn shoved through" | | 2 | "Quinn's hand flew to her" | | 3 | "Dispatch crackled back." | | 4 | "She cursed under her breath," | | 5 | "Military precision snapped her strides" | | 6 | "Her worn leather watch ticked" | | 7 | "Tomás veered sharp into Greek" | | 8 | "He glanced back, warm brown" | | 9 | "A taxi fishtailed, brakes screaming." | | 10 | "Quinn followed, leaped the bonnet." | | 11 | "Tires hissed inches from her" | | 12 | "He didn't break stride." | | 13 | "Quinn cornered in, pistol drawn" | | 14 | "Shadow swallowed him—gone around the" | | 15 | "Puddles exploded under her soles." | | 16 | "She rounded the corner, glimpsed" | | 17 | "Heart hammered her ribs." | | 18 | "Salt-and-pepper hair plastered to her" | | 19 | "Brown eyes narrowed on the" |
| | ratio | 0.839 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 174 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 92.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás barked (bark)" | | 1 | "Bargains hissed: (hiss)" |
| | dialogueSentences | 35 | | tagDensity | 0.057 | | leniency | 0.114 | | rawRatio | 1 | | effectiveRatio | 0.114 | |