| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1425 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 36.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1425 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "footfall" | | 1 | "absolutely" | | 2 | "standard" | | 3 | "rhythmic" | | 4 | "vibrated" | | 5 | "pulsed" | | 6 | "resolving" | | 7 | "shimmered" | | 8 | "velvet" | | 9 | "familiar" | | 10 | "weight" | | 11 | "flicked" | | 12 | "flickered" | | 13 | "could feel" | | 14 | "measured" | | 15 | "warmth" |
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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 | 0 | | narrationSentences | 128 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 142 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1425 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1219 | | uniqueNames | 13 | | maxNameDensity | 0.33 | | worstName | "Morris" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Inverness | 1 | | Street | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Camden | 3 | | High | 1 | | Underground | 3 | | Town | 1 | | London | 1 | | Tube | 1 | | Quinn | 3 | | Morris | 4 |
| | persons | | | places | | 0 | "Inverness" | | 1 | "Street" | | 2 | "Raven" | | 3 | "Soho" | | 4 | "Camden" | | 5 | "High" | | 6 | "Town" | | 7 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like finger bones on velvet" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1425 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 142 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 29.69 | | std | 22.95 | | cv | 0.773 | | sampleLengths | | 0 | 21 | | 1 | 80 | | 2 | 2 | | 3 | 9 | | 4 | 77 | | 5 | 47 | | 6 | 2 | | 7 | 1 | | 8 | 62 | | 9 | 5 | | 10 | 53 | | 11 | 14 | | 12 | 9 | | 13 | 53 | | 14 | 8 | | 15 | 2 | | 16 | 56 | | 17 | 57 | | 18 | 11 | | 19 | 61 | | 20 | 17 | | 21 | 66 | | 22 | 20 | | 23 | 9 | | 24 | 21 | | 25 | 23 | | 26 | 21 | | 27 | 20 | | 28 | 7 | | 29 | 30 | | 30 | 45 | | 31 | 41 | | 32 | 4 | | 33 | 1 | | 34 | 50 | | 35 | 48 | | 36 | 56 | | 37 | 41 | | 38 | 50 | | 39 | 20 | | 40 | 3 | | 41 | 41 | | 42 | 4 | | 43 | 37 | | 44 | 3 | | 45 | 32 | | 46 | 25 | | 47 | 60 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 128 | | matches | | |
| 99.50% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 199 | | matches | | 0 | "was running" | | 1 | "was talking" | | 2 | "was getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 142 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1227 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.022819885900570498 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0024449877750611247 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 142 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 142 | | mean | 10.04 | | std | 8.59 | | cv | 0.856 | | sampleLengths | | 0 | 21 | | 1 | 18 | | 2 | 4 | | 3 | 17 | | 4 | 22 | | 5 | 2 | | 6 | 17 | | 7 | 2 | | 8 | 3 | | 9 | 3 | | 10 | 3 | | 11 | 3 | | 12 | 15 | | 13 | 4 | | 14 | 17 | | 15 | 38 | | 16 | 7 | | 17 | 13 | | 18 | 16 | | 19 | 11 | | 20 | 2 | | 21 | 1 | | 22 | 9 | | 23 | 12 | | 24 | 33 | | 25 | 8 | | 26 | 5 | | 27 | 5 | | 28 | 16 | | 29 | 2 | | 30 | 7 | | 31 | 16 | | 32 | 7 | | 33 | 5 | | 34 | 9 | | 35 | 4 | | 36 | 2 | | 37 | 3 | | 38 | 9 | | 39 | 8 | | 40 | 11 | | 41 | 3 | | 42 | 1 | | 43 | 21 | | 44 | 4 | | 45 | 4 | | 46 | 2 | | 47 | 5 | | 48 | 1 | | 49 | 1 |
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| 69.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.49295774647887325 | | totalSentences | 142 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 106 | | matches | | 0 | "Just darkness and the distant" | | 1 | "Of course not." | | 2 | "Too calmly for someone she'd" | | 3 | "Too many angles." |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 106 | | matches | | 0 | "She'd been tailing him for" | | 1 | "He didn't stop." | | 2 | "He never stopped." | | 3 | "They never did." | | 4 | "Her lungs burned." | | 5 | "She shoved past a group" | | 6 | "He dropped into a stairwell." | | 7 | "She pulled her phone." | | 8 | "Her torch cut a white" | | 9 | "Her gut said wrong." | | 10 | "Her training said follow." | | 11 | "It carried a scent like" | | 12 | "He was talking to the" | | 13 | "His expression shifted." | | 14 | "He said it calmly" | | 15 | "She moved closer, positioning herself" | | 16 | "She caught the movement in" | | 17 | "It smiled, and the mouth" | | 18 | "He knew her name." | | 19 | "He knew her name and" |
| | ratio | 0.226 | |
| 63.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 106 | | matches | | 0 | "The man's trainers slapped against" | | 1 | "Quinn vaulted a wheelie bin," | | 2 | "Rain needled her face." | | 3 | "The suspect, a lanky figure" | | 4 | "She'd been tailing him for" | | 5 | "The one where people went" | | 6 | "He didn't stop." | | 7 | "He never stopped." | | 8 | "They never did." | | 9 | "Her lungs burned." | | 10 | "Morris would have laughed." | | 11 | "Morris would have been fifty" | | 12 | "Morris was dead, and the" | | 13 | "The alley opened onto Camden" | | 14 | "Saturday night crowds spilling from" | | 15 | "The suspect wove through them" | | 16 | "She shoved past a group" | | 17 | "A kebab shop's greasy light" | | 18 | "The smell of grilled meat" | | 19 | "The suspect glanced back, and" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 34.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 7 | | matches | | 0 | "Morris was dead, and the case file on his death sat in her desk drawer, untouched except for the nights she opened it with a glass of whiskey and read the same …" | | 1 | "The suspect glanced back, and for a split second she caught his face under the streetlight, young, mid-twenties maybe, pale, eyes wide with something that looke…" | | 2 | "The stairwell curved, and the sound of the street above faded until all she could hear was her own breathing and a low, rhythmic thrum that vibrated through the…" | | 3 | "She could feel it now, a presence that made her teeth ache and her vision swim at the edges." | | 4 | "The two figures at the stairwell hadn't moved, but their silhouettes had grown, stretched, become something that defied the geometry of the tunnel around them." | | 5 | "Looked at the shimmering air behind her that was getting closer, closer, and colder, and colder, and the cold had fingers now, long ones, threading through her …" | | 6 | "The obsidian-eyed vendor snapped its jaw shut and returned to its wares as though nothing had happened." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 20 | | tagDensity | 0.05 | | leniency | 0.1 | | rawRatio | 0 | | effectiveRatio | 0 | |