| 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 | 1189 | | 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) | |
| 74.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1189 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "unraveling" | | 1 | "footsteps" | | 2 | "sense of" | | 3 | "silence" | | 4 | "flickered" |
| |
| 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 | 0 | | narrationSentences | 80 | | matches | (empty) | |
| 35.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 5 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "managed to" | | 2 | "seemed to" | | 3 | "happened to" | | 4 | "appeared to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1199 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1189 | | uniqueNames | 13 | | maxNameDensity | 0.76 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 1 | | Quinn | 9 | | Herrera | 7 | | London | 1 | | Tuesday | 1 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Morris | 3 | | Saint | 1 | | Christopher | 1 | | Tube | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "London" | | 2 | "Raven" | | 3 | "Soho" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 1 | | matches | | 0 | "quite fear, the scar along his forearm pale as he flung his arm out for balance" |
| |
| 33.19% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.668 | | wordCount | 1199 | | matches | | 0 | "Not this stairwell, but this moment: the threshold, the choice, the dark on the othe" | | 1 | "not the cold white of fluorescents but something warmer, amber" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 41.34 | | std | 31.03 | | cv | 0.75 | | sampleLengths | | 0 | 65 | | 1 | 8 | | 2 | 85 | | 3 | 13 | | 4 | 66 | | 5 | 83 | | 6 | 39 | | 7 | 3 | | 8 | 73 | | 9 | 4 | | 10 | 70 | | 11 | 6 | | 12 | 56 | | 13 | 84 | | 14 | 56 | | 15 | 23 | | 16 | 103 | | 17 | 6 | | 18 | 73 | | 19 | 5 | | 20 | 71 | | 21 | 8 | | 22 | 45 | | 23 | 56 | | 24 | 19 | | 25 | 40 | | 26 | 16 | | 27 | 20 | | 28 | 3 |
| |
| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 80 | | matches | | |
| 60.38% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 191 | | matches | | 0 | "was pushing" | | 1 | "was hunting" | | 2 | "was already scrambling" | | 3 | "was already moving" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 82 | | ratio | 0.11 | | matches | | 0 | "The streetlight caught his face for half a second — warm brown eyes wide with something that wasn't quite fear, the scar along his forearm pale as he flung his arm out for balance." | | 1 | "NHS, struck off three years ago for a string of \"unauthorized treatments\" that no hospital board had ever managed to fully explain — patients who shouldn't have survived, charts that didn't add up, a paper trail that dissolved the moment you tried to follow it." | | 2 | "Ahead, the alley dead-ended at a corrugated fence — and Herrera was already scrambling at its base, hauling aside a sheet of warped plywood to reveal a black gap in the brick wall behind it." | | 3 | "And for a moment Quinn thought he might say something — a warning, a plea — but instead he touched the medallion at his throat, the small silver disc of Saint Christopher, patron of travelers, and pressed it to his lips." | | 4 | "Cold air breathed up out of it, smelling of wet stone and rust and something else underneath — something sweet and chemical and wrong, like burnt sugar laced with copper." | | 5 | "There was no abandoned Tube station marked at Camden on any map she'd ever seen — she'd memorized the lot of them years ago, every disused line and ghost platform in the city, the better to know where bodies went and where men like Herrera disappeared to." | | 6 | "with Morris's name in her mouth — that part was already moving." | | 7 | "In the new silence she heard other sounds rise up to meet her — a low murmur of many voices, the clink of glass or coin, a strain of music in some scale she didn't recognize, rising and falling like a tide." | | 8 | "It was bone — yellowed, smooth, carved with markings worn soft by handling, no larger than a domino." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1182 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.025380710659898477 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008460236886632825 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 14.62 | | std | 13.2 | | cv | 0.903 | | sampleLengths | | 0 | 32 | | 1 | 33 | | 2 | 4 | | 3 | 4 | | 4 | 38 | | 5 | 3 | | 6 | 44 | | 7 | 11 | | 8 | 2 | | 9 | 3 | | 10 | 2 | | 11 | 34 | | 12 | 27 | | 13 | 5 | | 14 | 2 | | 15 | 45 | | 16 | 8 | | 17 | 6 | | 18 | 17 | | 19 | 5 | | 20 | 34 | | 21 | 3 | | 22 | 17 | | 23 | 21 | | 24 | 35 | | 25 | 2 | | 26 | 2 | | 27 | 8 | | 28 | 3 | | 29 | 9 | | 30 | 41 | | 31 | 9 | | 32 | 6 | | 33 | 30 | | 34 | 16 | | 35 | 10 | | 36 | 13 | | 37 | 13 | | 38 | 1 | | 39 | 3 | | 40 | 47 | | 41 | 7 | | 42 | 23 | | 43 | 3 | | 44 | 3 | | 45 | 27 | | 46 | 11 | | 47 | 12 | | 48 | 4 | | 49 | 19 |
| |
| 85.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5365853658536586 | | totalSentences | 82 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 72 | | matches | | 0 | "Then he was gone around" | | 1 | "Then he ducked into the" | | 2 | "Further down than any basement" | | 3 | "Then she looked down, into" |
| | ratio | 0.056 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 72 | | matches | | 0 | "She'd give him that." | | 1 | "He cut left at the" | | 2 | "She didn't stop." | | 3 | "she shouted, though the word" | | 4 | "He glanced back." | | 5 | "She knew his file backwards." | | 6 | "He patched up the people" | | 7 | "He looked back at her" | | 8 | "His chest heaved." | | 9 | "She crouched and shone her" | | 10 | "Her rational mind, the part" | | 11 | "She'd been here before." | | 12 | "He'd walked through and the" | | 13 | "They'd given her a leave" | | 14 | "She had not let it" | | 15 | "She thought of the duty" | | 16 | "They already thought she was" | | 17 | "She killed her phone light" | | 18 | "She descended three steps, careful," | | 19 | "She picked it up between" |
| | ratio | 0.333 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 72 | | matches | | 0 | "The rain came sideways off" | | 1 | "Detective Harlow Quinn ran through" | | 2 | "Tomás Herrera could run." | | 3 | "She'd give him that." | | 4 | "He cut left at the" | | 5 | "She didn't stop." | | 6 | "she shouted, though the word" | | 7 | "He glanced back." | | 8 | "The streetlight caught his face" | | 9 | "She knew his file backwards." | | 10 | "NHS, struck off three years" | | 11 | "He patched up the people" | | 12 | "The ones who left no" | | 13 | "The ones who walked into" | | 14 | "Herrera was the loose thread." | | 15 | "The street narrowed into an" | | 16 | "Herrera vaulted a low chain" | | 17 | "He looked back at her" | | 18 | "His chest heaved." | | 19 | "Rain ran off the curls" |
| | ratio | 0.653 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | | ratio | 0.014 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 8 | | matches | | 0 | "The rain came sideways off the Camden rooftops, turning the streetlights into smeared halos and the pavement into a black mirror that threw the city back at its…" | | 1 | "Detective Harlow Quinn ran through all of it, her boots punching up sheets of water with every stride, her breath sawing in her chest, her eyes fixed on the man…" | | 2 | "He cut left at the corner of a shuttered greengrocer, his soaked jacket flaring like a wing, and Quinn felt the old burn in her thighs that meant she was pushin…" | | 3 | "The streetlight caught his face for half a second — warm brown eyes wide with something that wasn't quite fear, the scar along his forearm pale as he flung his …" | | 4 | "Herrera vaulted a low chain barrier without slowing, and Quinn went over it a half-second behind, her coat snagging and tearing." | | 5 | "Ahead, the alley dead-ended at a corrugated fence — and Herrera was already scrambling at its base, hauling aside a sheet of warped plywood to reveal a black ga…" | | 6 | "Below, she could hear his footsteps ringing down a long flight of metal steps, growing fainter." | | 7 | "She thought of the duty sergeant's voice asking her position, and of the report she'd have to file describing a Tube station that didn't exist, and of how every…" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, though the word frayed apart in the wind" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 1 | | effectiveRatio | 0.8 | |