| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 101 | | tagDensity | 0.05 | | leniency | 0.099 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2306 | | 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) | |
| 76.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2306 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "warmth" | | 1 | "glint" | | 2 | "etching" | | 3 | "silence" | | 4 | "fluttered" | | 5 | "silk" | | 6 | "whisper" | | 7 | "etched" | | 8 | "footsteps" | | 9 | "echoed" | | 10 | "stark" |
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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 | 195 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 195 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 291 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2306 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1631 | | uniqueNames | 10 | | maxNameDensity | 1.53 | | worstName | "Mercer" | | maxWindowNameDensity | 3 | | worstWindowName | "Mercer" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Colin | 1 | | Mercer | 25 | | Singh | 2 | | Camden | 1 | | Reed | 3 | | Varga | 3 | | Kowalski | 1 | | One | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Colin" | | 3 | "Mercer" | | 4 | "Singh" | | 5 | "Reed" | | 6 | "Varga" | | 7 | "Kowalski" |
| | places | | | globalScore | 0.734 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.434 | | wordCount | 2306 | | matches | | 0 | "not from random foot traffic but from a careful scraping, as though someone had opened a door" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 291 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 173 | | mean | 13.33 | | std | 18.28 | | cv | 1.372 | | sampleLengths | | 0 | 102 | | 1 | 10 | | 2 | 10 | | 3 | 1 | | 4 | 7 | | 5 | 57 | | 6 | 78 | | 7 | 4 | | 8 | 10 | | 9 | 9 | | 10 | 26 | | 11 | 35 | | 12 | 4 | | 13 | 2 | | 14 | 1 | | 15 | 4 | | 16 | 8 | | 17 | 51 | | 18 | 1 | | 19 | 20 | | 20 | 9 | | 21 | 5 | | 22 | 15 | | 23 | 2 | | 24 | 19 | | 25 | 41 | | 26 | 59 | | 27 | 3 | | 28 | 4 | | 29 | 4 | | 30 | 10 | | 31 | 4 | | 32 | 19 | | 33 | 59 | | 34 | 4 | | 35 | 2 | | 36 | 7 | | 37 | 3 | | 38 | 8 | | 39 | 8 | | 40 | 4 | | 41 | 7 | | 42 | 34 | | 43 | 13 | | 44 | 1 | | 45 | 4 | | 46 | 8 | | 47 | 26 | | 48 | 5 | | 49 | 1 |
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| 98.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 195 | | matches | | 0 | "been yanked" | | 1 | "been branded" | | 2 | "been found" | | 3 | "been wiped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 277 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 291 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1639 | | adjectiveStacks | 2 | | stackExamples | | 0 | "same sweet metallic scent" | | 1 | "left trapped under him." |
| | adverbCount | 45 | | adverbRatio | 0.027455765710799267 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004270896888346553 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 291 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 291 | | mean | 7.92 | | std | 6.87 | | cv | 0.867 | | sampleLengths | | 0 | 28 | | 1 | 16 | | 2 | 10 | | 3 | 8 | | 4 | 8 | | 5 | 32 | | 6 | 6 | | 7 | 2 | | 8 | 2 | | 9 | 10 | | 10 | 1 | | 11 | 7 | | 12 | 24 | | 13 | 15 | | 14 | 4 | | 15 | 13 | | 16 | 1 | | 17 | 13 | | 18 | 1 | | 19 | 1 | | 20 | 4 | | 21 | 3 | | 22 | 9 | | 23 | 23 | | 24 | 24 | | 25 | 4 | | 26 | 10 | | 27 | 9 | | 28 | 26 | | 29 | 4 | | 30 | 7 | | 31 | 14 | | 32 | 2 | | 33 | 1 | | 34 | 7 | | 35 | 4 | | 36 | 2 | | 37 | 1 | | 38 | 4 | | 39 | 8 | | 40 | 5 | | 41 | 2 | | 42 | 2 | | 43 | 15 | | 44 | 5 | | 45 | 6 | | 46 | 16 | | 47 | 1 | | 48 | 20 | | 49 | 9 |
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| 61.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3917525773195876 | | totalSentences | 291 | | uniqueOpeners | 114 | |
| 60.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 164 | | matches | | 0 | "Too fine, too bright against" | | 1 | "Then the powder circle." | | 2 | "Then the dead man." |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 164 | | matches | | 0 | "Her boot touched the yellow" | | 1 | "Her left wrist turned." | | 2 | "His face had gone waxy" | | 3 | "He looked up with that" | | 4 | "His right hand remained clenched." | | 5 | "She did not touch it" | | 6 | "He looked at her, patient" | | 7 | "She angled her torch." | | 8 | "He smiled without warmth." | | 9 | "She ignored him." | | 10 | "She pinched one with a" | | 11 | "It smudged cold across the" | | 12 | "He stepped closer, looked, then" | | 13 | "She crossed to the powder" | | 14 | "She held the torch closer." | | 15 | "She smelled singed wool." | | 16 | "Her gaze shifted to the" | | 17 | "She held the screen up." | | 18 | "She had seen something close" | | 19 | "Her tone cut enough that" |
| | ratio | 0.207 | |
| 88.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 164 | | matches | | 0 | "The train tunnel had not" | | 1 | "Detective Harlow Quinn stopped at" | | 2 | "Dust hung in the beam" | | 3 | "CAMDEN TOWN, one cracked letter" | | 4 | "Somebody had strung police tape" | | 5 | "Her boot touched the yellow" | | 6 | "A constable near the stairwell" | | 7 | "Quinn lifted two fingers and" | | 8 | "The air carried damp brick," | | 9 | "Blood, but not enough for" | | 10 | "Her left wrist turned." | | 11 | "The worn leather strap of" | | 12 | "DS Colin Mercer waited beside" | | 13 | "His face had gone waxy" | | 14 | "Mercer had crouched with a" | | 15 | "He looked up with that" | | 16 | "Mercer stood and tucked the" | | 17 | "Quinn swept the platform." | | 18 | "Mercer gave one shoulder." | | 19 | "Quinn crouched by the victim." |
| | ratio | 0.744 | |
| 30.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 164 | | matches | | 0 | "Now he lay slightly turned," |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 2 | | matches | | 0 | "The air carried damp brick, engine oil gone sour, and beneath both, a sweet metallic tang that caught at the back of her throat." | | 1 | "Quinn pictured the missing brass compass in the photo, the old rumours passed between vice officers and thieves too frightened to put names in statements, the w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she repeated (repeat)" | | 1 | "she murmured (murmur)" |
| | dialogueSentences | 101 | | tagDensity | 0.05 | | leniency | 0.099 | | rawRatio | 0.4 | | effectiveRatio | 0.04 | |