| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1109 | | 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) | |
| 14.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1109 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "shattered" | | 1 | "echoed" | | 2 | "calculating" | | 3 | "flickered" | | 4 | "rhythmic" | | 5 | "echoing" | | 6 | "pulse" | | 7 | "glinting" | | 8 | "beacon" | | 9 | "gleaming" | | 10 | "etched" | | 11 | "hulking" | | 12 | "echoes" | | 13 | "footsteps" | | 14 | "jaw clenched" | | 15 | "maw" |
| |
| 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 | 189 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 189 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 191 | | 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 | 1111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1099 | | uniqueNames | 24 | | maxNameDensity | 1.09 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Tomás | 1 | | Herrera | 12 | | Soho | 1 | | Saint | 1 | | Christopher | 1 | | Dean | 1 | | Street | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Raven | 1 | | Nest | 2 | | Silas | 3 | | Torch | 1 | | TfL | 1 | | Morris | 3 | | Tube | 1 | | Camden | 1 | | Bovril | 1 | | Brylcreem | 1 | | Quinn | 10 | | Veil | 1 | | Market | 2 | | Gate | 3 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Silas" | | 7 | "Torch" | | 8 | "Morris" | | 9 | "Brylcreem" | | 10 | "Quinn" | | 11 | "Market" | | 12 | "Gate" |
| | places | | 0 | "Soho" | | 1 | "Dean" | | 2 | "Street" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" |
| | globalScore | 0.954 | | windowScore | 1 | |
| 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 | 1111 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 191 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 25.84 | | std | 17.08 | | cv | 0.661 | | sampleLengths | | 0 | 30 | | 1 | 55 | | 2 | 9 | | 3 | 43 | | 4 | 37 | | 5 | 47 | | 6 | 63 | | 7 | 42 | | 8 | 4 | | 9 | 11 | | 10 | 45 | | 11 | 39 | | 12 | 39 | | 13 | 40 | | 14 | 50 | | 15 | 57 | | 16 | 32 | | 17 | 39 | | 18 | 39 | | 19 | 20 | | 20 | 36 | | 21 | 8 | | 22 | 3 | | 23 | 39 | | 24 | 22 | | 25 | 34 | | 26 | 24 | | 27 | 28 | | 28 | 11 | | 29 | 33 | | 30 | 8 | | 31 | 25 | | 32 | 18 | | 33 | 11 | | 34 | 8 | | 35 | 22 | | 36 | 9 | | 37 | 5 | | 38 | 4 | | 39 | 2 | | 40 | 10 | | 41 | 4 | | 42 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 189 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 235 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 1 | | flaggedSentences | 16 | | totalSentences | 191 | | ratio | 0.084 | | matches | | 0 | "She glimpsed his warm brown eyes in the rear-view of a parked cab – panicked, calculating." | | 1 | "The green neon sign of The Raven's Nest flickered ahead – distinctive, mocking." | | 2 | "A bartender – Silas, probably – gripped a rag, knuckles white." | | 3 | "Dank air punched her nostrils – mould, incense, something metallic." | | 4 | "Footprints smeared the mud – his boots, fresh." | | 5 | "She holstered the pistol – too risky in tight quarters – and sprinted." | | 6 | "Left forked to blackness; right glowed faint orange." | | 7 | "Figures huddled under them – cloaked, hooded, eyes gleaming unnatural." | | 8 | "He flashed a carved bone token at a hulking guard – trollish brute, skin like wet stone." | | 9 | "These weren't her usual punters – junkies, pimps." | | 10 | "He bartered quick with a stall-keep – herbs?" | | 11 | "– then grabbed a satchel and ran." | | 12 | "A hand grabbed her wrist – spindly fingers, cold as death." | | 13 | "A blade flashed – curved, obsidian-edged." | | 14 | "Echoes multiplied – footsteps?" | | 15 | "No – sacrifice in progress." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1097 | | adjectiveStacks | 1 | | stackExamples | | 0 | "harder, short curly hair" |
| | adverbCount | 18 | | adverbRatio | 0.016408386508659983 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004557885141294439 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 191 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 191 | | mean | 5.82 | | std | 3.67 | | cv | 0.631 | | sampleLengths | | 0 | 11 | | 1 | 6 | | 2 | 13 | | 3 | 11 | | 4 | 5 | | 5 | 6 | | 6 | 16 | | 7 | 17 | | 8 | 9 | | 9 | 4 | | 10 | 9 | | 11 | 13 | | 12 | 4 | | 13 | 5 | | 14 | 8 | | 15 | 5 | | 16 | 8 | | 17 | 6 | | 18 | 18 | | 19 | 13 | | 20 | 14 | | 21 | 16 | | 22 | 3 | | 23 | 1 | | 24 | 7 | | 25 | 7 | | 26 | 7 | | 27 | 13 | | 28 | 11 | | 29 | 10 | | 30 | 2 | | 31 | 6 | | 32 | 5 | | 33 | 12 | | 34 | 7 | | 35 | 4 | | 36 | 3 | | 37 | 11 | | 38 | 4 | | 39 | 7 | | 40 | 2 | | 41 | 2 | | 42 | 7 | | 43 | 2 | | 44 | 9 | | 45 | 7 | | 46 | 1 | | 47 | 10 | | 48 | 9 | | 49 | 13 |
| |
| 86.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5287958115183246 | | totalSentences | 191 | | uniqueOpeners | 101 | |
| 22.83% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 146 | | matches | | 0 | "– then grabbed a satchel" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 146 | | matches | | 0 | "He spun, elbow cracking her" | | 1 | "She grunted, breath exploding from" | | 2 | "She hurdled them in one" | | 3 | "Her shout echoed off graffiti-smeared" | | 4 | "He didn't glance back." | | 5 | "She knew that mark." | | 6 | "They burst onto Dean Street." | | 7 | "She glimpsed his warm brown" | | 8 | "He suspected her." | | 9 | "He veered right, towards Charing" | | 10 | "She matched his zigzag, coat" | | 11 | "She'd tailed him here earlier," | | 12 | "It swung wide, spilling amber" | | 13 | "She crashed in after, pistol" | | 14 | "She hit the hidden bookshelf" | | 15 | "It gave, swinging into the" | | 16 | "She dropped through without hesitation," | | 17 | "Her torch caught cobwebs thick" | | 18 | "She holstered the pistol –" | | 19 | "She followed, emerging into a" |
| | ratio | 0.247 | |
| 59.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 117 | | totalSentences | 146 | | matches | | 0 | "Quinn's gloved hand snagged the" | | 1 | "Fabric tore with a wet" | | 2 | "He spun, elbow cracking her" | | 3 | "She grunted, breath exploding from" | | 4 | "Boots skidded on slick cobbles." | | 5 | "Puddles shattered underfoot, spraying her" | | 6 | "Herrera vaulted a stack of" | | 7 | "She hurdled them in one" | | 8 | "Her shout echoed off graffiti-smeared" | | 9 | "He didn't glance back." | | 10 | "The scar on his left" | | 11 | "She knew that mark." | | 12 | "Knife fight, three years back." | | 13 | "They burst onto Dean Street." | | 14 | "Cars hissed past, tyres slicing" | | 15 | "Pedestrians scattered, umbrellas flipping inside" | | 16 | "Herrera darted between a cluster" | | 17 | "Quinn shoved through the gap," | | 18 | "She glimpsed his warm brown" | | 19 | "He suspected her." |
| | ratio | 0.801 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 146 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |