| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 998 | | 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) | |
| 69.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 998 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "predator" | | 1 | "shattered" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "weight" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 992 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 981 | | uniqueNames | 5 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Victorian | 1 | | Tube | 2 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | (empty) | | globalScore | 0.99 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 992 | | matches | (empty) | |
| 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 | 26 | | mean | 38.15 | | std | 21.27 | | cv | 0.557 | | sampleLengths | | 0 | 75 | | 1 | 50 | | 2 | 65 | | 3 | 29 | | 4 | 51 | | 5 | 40 | | 6 | 24 | | 7 | 4 | | 8 | 48 | | 9 | 38 | | 10 | 10 | | 11 | 47 | | 12 | 3 | | 13 | 68 | | 14 | 48 | | 15 | 34 | | 16 | 41 | | 17 | 17 | | 18 | 39 | | 19 | 20 | | 20 | 39 | | 21 | 51 | | 22 | 80 | | 23 | 7 | | 24 | 54 | | 25 | 10 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 81 | | matches | | 0 | "been locked" | | 1 | "was sworn" | | 2 | "been called" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 166 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 82 | | ratio | 0.073 | | matches | | 0 | "She'd shed her coat three blocks back—too heavy, too much drag—and now her shirt clung to her skin like a second layer of cold." | | 1 | "Military precision had its uses; it taught you to ignore the body when the body wanted to quit." | | 2 | "The runner cut left, through a doorway she hadn't noticed—an old service entrance, its paint peeling in long curls like dead skin." | | 3 | "The air changed—cooler, stale, carrying a faint metallic tang she couldn't place." | | 4 | "There was light ahead—dim, greenish, flickering like gas flames." | | 5 | "DS Morris, the way he'd looked in the split second before the case went bad—eyes wide, mouth open, a question that never quite became a word." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.028282828282828285 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.00202020202020202 | |
| 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 | 12.1 | | std | 7.5 | | cv | 0.62 | | sampleLengths | | 0 | 28 | | 1 | 23 | | 2 | 24 | | 3 | 8 | | 4 | 23 | | 5 | 19 | | 6 | 4 | | 7 | 21 | | 8 | 22 | | 9 | 18 | | 10 | 8 | | 11 | 4 | | 12 | 2 | | 13 | 1 | | 14 | 14 | | 15 | 22 | | 16 | 11 | | 17 | 10 | | 18 | 8 | | 19 | 7 | | 20 | 18 | | 21 | 15 | | 22 | 15 | | 23 | 9 | | 24 | 4 | | 25 | 3 | | 26 | 23 | | 27 | 6 | | 28 | 12 | | 29 | 2 | | 30 | 2 | | 31 | 10 | | 32 | 19 | | 33 | 9 | | 34 | 5 | | 35 | 1 | | 36 | 4 | | 37 | 10 | | 38 | 14 | | 39 | 9 | | 40 | 14 | | 41 | 3 | | 42 | 22 | | 43 | 25 | | 44 | 21 | | 45 | 10 | | 46 | 21 | | 47 | 17 | | 48 | 3 | | 49 | 3 |
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| 67.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.43902439024390244 | | totalSentences | 82 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 75 | | matches | | 0 | "Just black, swallowing the shape" | | 1 | "A lot of them." | | 2 | "Just stood there, watching, a" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 75 | | matches | | 0 | "She'd shed her coat three" | | 1 | "He knew these streets." | | 2 | "He vaulted a low railing," | | 3 | "She could hear his breath" | | 4 | "She pulled her torch from" | | 5 | "Her voice came out hard" | | 6 | "She went down." | | 7 | "They stood ajar, chains hanging" | | 8 | "Her heart hammered against her" | | 9 | "It leaked through cracks in" | | 10 | "She edged closer." | | 11 | "She'd heard rumours." | | 12 | "They exchanged words, then the" | | 13 | "He couldn't have seen her." | | 14 | "Her hand went to the" | | 15 | "She had options." | | 16 | "Her partner's face surfaced." | | 17 | "She didn't break stride." | | 18 | "She followed the corridor, and" |
| | ratio | 0.253 | |
| 66.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 75 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn's boots slapped" | | 2 | "She'd shed her coat three" | | 3 | "The figure ahead moved with" | | 4 | "He knew these streets." | | 5 | "Quinn's lungs burned but she" | | 6 | "Military precision had its uses;" | | 7 | "He vaulted a low railing," | | 8 | "Quinn closed the gap." | | 9 | "She could hear his breath" | | 10 | "The runner cut left, through" | | 11 | "Quinn skidded on the wet" | | 12 | "The corridor beyond sloped downward" | | 13 | "She pulled her torch from" | | 14 | "The beam cut a weak" | | 15 | "The sound of his footsteps" | | 16 | "Her voice came out hard" | | 17 | "The footsteps didn't stop." | | 18 | "She went down." | | 19 | "The stairs were narrow, worn" |
| | ratio | 0.787 | |
| 66.67% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 75 | | matches | | 0 | "Even at this distance, even" |
| | ratio | 0.013 | |
| 66.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 5 | | matches | | 0 | "The rain came down in sheets, hammering the pavement so hard it bounced back up in a fine mist that clung to the sodium glow of the streetlamps." | | 1 | "Kept to the narrow passages, the cut-throughs that smelled of rot and urine, the places where the CCTV had blind spots." | | 2 | "The runner had disappeared into a warren of stalls and temporary structures that filled what must have been an abandoned Tube station." | | 3 | "The green light came from lanterns, wrought iron and filigree, swinging gently in a breeze that had no business existing underground." | | 4 | "A child sat cross-legged beside a cage of small, winged things that clicked their beaks like castanets." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |