| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 84 | | tagDensity | 0.155 | | leniency | 0.31 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1606 | | 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) | |
| 71.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1606 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "stomach" | | 1 | "electric" | | 2 | "etched" | | 3 | "trembled" | | 4 | "scanned" | | 5 | "velvet" | | 6 | "traced" | | 7 | "flickered" |
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| 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 | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 202 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1606 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1107 | | uniqueNames | 13 | | maxNameDensity | 1.9 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Detective | 2 | | Harlow | 1 | | Quinn | 21 | | Underground | 1 | | Reed | 3 | | Venn | 5 | | Sergeant | 1 | | Lionel | 1 | | Vale | 19 | | Kowalski | 1 | | Bloomsbury | 1 | | Eva | 9 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Reed" | | 4 | "Venn" | | 5 | "Sergeant" | | 6 | "Lionel" | | 7 | "Vale" | | 8 | "Kowalski" | | 9 | "Eva" |
| | places | | | globalScore | 0.551 | | windowScore | 0.5 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared beside the body: narrow soles, sharp heels, each impression filled with a glimmering grey powder" | | 1 | "appeared, thin as a black hair" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.623 | | wordCount | 1606 | | matches | | 0 | "not north, but towards the sealed tunnel" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 202 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 121 | | mean | 13.27 | | std | 14.99 | | cv | 1.129 | | sampleLengths | | 0 | 62 | | 1 | 18 | | 2 | 4 | | 3 | 13 | | 4 | 3 | | 5 | 16 | | 6 | 4 | | 7 | 34 | | 8 | 3 | | 9 | 5 | | 10 | 4 | | 11 | 14 | | 12 | 5 | | 13 | 54 | | 14 | 15 | | 15 | 4 | | 16 | 2 | | 17 | 39 | | 18 | 9 | | 19 | 57 | | 20 | 6 | | 21 | 5 | | 22 | 44 | | 23 | 7 | | 24 | 5 | | 25 | 7 | | 26 | 31 | | 27 | 3 | | 28 | 58 | | 29 | 21 | | 30 | 17 | | 31 | 4 | | 32 | 10 | | 33 | 5 | | 34 | 1 | | 35 | 4 | | 36 | 9 | | 37 | 23 | | 38 | 6 | | 39 | 40 | | 40 | 2 | | 41 | 7 | | 42 | 3 | | 43 | 5 | | 44 | 37 | | 45 | 5 | | 46 | 41 | | 47 | 6 | | 48 | 3 | | 49 | 13 |
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| 97.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 131 | | matches | | 0 | "been closed" | | 1 | "been scrubbed" | | 2 | "been etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 184 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 202 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 760 | | adjectiveStacks | 1 | | stackExamples | | 0 | "clean, black-edged aperture" |
| | adverbCount | 14 | | adverbRatio | 0.018421052631578946 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0013157894736842105 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 202 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 202 | | mean | 7.95 | | std | 6.05 | | cv | 0.761 | | sampleLengths | | 0 | 22 | | 1 | 19 | | 2 | 7 | | 3 | 5 | | 4 | 9 | | 5 | 18 | | 6 | 4 | | 7 | 10 | | 8 | 3 | | 9 | 3 | | 10 | 12 | | 11 | 4 | | 12 | 4 | | 13 | 23 | | 14 | 11 | | 15 | 3 | | 16 | 5 | | 17 | 4 | | 18 | 12 | | 19 | 2 | | 20 | 5 | | 21 | 12 | | 22 | 5 | | 23 | 13 | | 24 | 13 | | 25 | 11 | | 26 | 15 | | 27 | 2 | | 28 | 2 | | 29 | 2 | | 30 | 14 | | 31 | 12 | | 32 | 8 | | 33 | 5 | | 34 | 9 | | 35 | 11 | | 36 | 14 | | 37 | 8 | | 38 | 24 | | 39 | 6 | | 40 | 5 | | 41 | 15 | | 42 | 7 | | 43 | 22 | | 44 | 3 | | 45 | 4 | | 46 | 5 | | 47 | 7 | | 48 | 7 | | 49 | 24 |
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| 60.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.37623762376237624 | | totalSentences | 202 | | uniqueOpeners | 76 | |
| 59.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 113 | | matches | | 0 | "Then it swung back, pointing" | | 1 | "Then the glass face cracked" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 113 | | matches | | 0 | "Its paint had blistered around" | | 1 | "She gave him the look" | | 2 | "Her torch cut across old" | | 3 | "Their wires sagged like vines." | | 4 | "He wore a dark coat," | | 5 | "His shirt had split from" | | 6 | "He held a notebook over" | | 7 | "His hair had thinned to" | | 8 | "Her green eyes moved from" | | 9 | "She crouched without touching it." | | 10 | "It pointed not north, but" | | 11 | "She pointed her torch down." | | 12 | "They began at the edge" | | 13 | "She drew on a pair" | | 14 | "She lifted the compass with" | | 15 | "His boots sat squarely against" | | 16 | "His left hand curled around" | | 17 | "She examined the hole in" | | 18 | "Its edges had browned, not" | | 19 | "It pointed straight down." |
| | ratio | 0.186 | |
| 17.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 113 | | matches | | 0 | "Rain had polished Camden’s pavements" | | 1 | "The station had been closed" | | 2 | "Its paint had blistered around" | | 3 | "Graffiti crawled across the brickwork." | | 4 | "A temporary police barrier sagged" | | 5 | "Constable Reed stood beneath it" | | 6 | "Quinn checked the worn leather" | | 7 | "She gave him the look" | | 8 | "Reed lowered his cup." | | 9 | "Reed glanced at the black" | | 10 | "Quinn ducked under the barrier." | | 11 | "The stairwell descended beneath Camden" | | 12 | "Water tapped from the ceiling." | | 13 | "Each drop struck a different" | | 14 | "Her torch cut across old" | | 15 | "The place smelled of wet" | | 16 | "Someone had strung electric bulbs" | | 17 | "Their wires sagged like vines." | | 18 | "The crime scene occupied the" | | 19 | "Callum Venn lay on his" |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 113 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 2 | | matches | | 0 | "She gave him the look that had ended arguments in interview rooms." | | 1 | "The velvet lining held several strands of dark hair, a coin stamped with a face that had too many eyes and a thumb-sized bottle filled with cloudy blue liquid." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "Vale warned (warn)" | | 1 | "she ordered (order)" |
| | dialogueSentences | 84 | | tagDensity | 0.131 | | leniency | 0.262 | | rawRatio | 0.182 | | effectiveRatio | 0.048 | |