| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 2 | | adverbTags | | 0 | "Harloq turned back [back]" | | 1 | "Harloq walked back [back]" |
| | dialogueSentences | 54 | | tagDensity | 0.315 | | leniency | 0.63 | | rawRatio | 0.118 | | effectiveRatio | 0.074 | |
| 90.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1513 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "lazily" | | 1 | "slightly" | | 2 | "gently" |
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| 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) | |
| 50.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1513 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "echoed" | | 2 | "velvet" | | 3 | "silk" | | 4 | "weight" | | 5 | "etched" | | 6 | "standard" | | 7 | "pristine" | | 8 | "perfect" | | 9 | "racing" | | 10 | "fractured" | | 11 | "scanned" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | 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 | 2 | | markdownWords | 4 | | totalWords | 1508 | | ratio | 0.003 | | matches | | 0 | "plink, plink, plink" | | 1 | "that" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 891 | | uniqueNames | 10 | | maxNameDensity | 2.47 | | worstName | "Harloq" | | maxWindowNameDensity | 5 | | worstWindowName | "Harloq" | | discoveredNames | | Tube | 1 | | Harlow | 3 | | Quinn | 1 | | Veil | 1 | | Market | 1 | | Kowalski | 1 | | Eva | 13 | | Harloq | 22 | | Vane | 5 | | Underground | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Harloq" | | 5 | "Vane" |
| | places | (empty) | | globalScore | 0.265 | | windowScore | 0 | |
| 32.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like dried newts, her boots crunch" | | 1 | "as if expecting the gargoyles carved into the brickwork to come alive and eat him" | | 2 | "as if embracing the grimy ceiling" |
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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 | 1508 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 26.93 | | std | 19.5 | | cv | 0.724 | | sampleLengths | | 0 | 96 | | 1 | 45 | | 2 | 8 | | 3 | 52 | | 4 | 28 | | 5 | 46 | | 6 | 15 | | 7 | 14 | | 8 | 35 | | 9 | 21 | | 10 | 48 | | 11 | 3 | | 12 | 13 | | 13 | 28 | | 14 | 30 | | 15 | 31 | | 16 | 6 | | 17 | 49 | | 18 | 43 | | 19 | 7 | | 20 | 17 | | 21 | 7 | | 22 | 23 | | 23 | 9 | | 24 | 73 | | 25 | 11 | | 26 | 53 | | 27 | 5 | | 28 | 41 | | 29 | 9 | | 30 | 48 | | 31 | 30 | | 32 | 38 | | 33 | 27 | | 34 | 10 | | 35 | 22 | | 36 | 34 | | 37 | 9 | | 38 | 39 | | 39 | 19 | | 40 | 6 | | 41 | 24 | | 42 | 2 | | 43 | 39 | | 44 | 1 | | 45 | 60 | | 46 | 23 | | 47 | 39 | | 48 | 30 | | 49 | 21 |
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| 71.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 82 | | matches | | 0 | "were held" | | 1 | "been torn" | | 2 | "was etched" | | 3 | "was scraped" | | 4 | "was abraded" | | 5 | "was undisturbed" | | 6 | "were stacked" | | 7 | "were made" |
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| 25.71% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 153 | | matches | | 0 | "wasn't pointing" | | 1 | "was pointing" | | 2 | "weren't watching" | | 3 | "were watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 893 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.023516237402015677 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007838745800671893 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 12.67 | | std | 8.49 | | cv | 0.67 | | sampleLengths | | 0 | 23 | | 1 | 16 | | 2 | 24 | | 3 | 12 | | 4 | 3 | | 5 | 18 | | 6 | 14 | | 7 | 31 | | 8 | 8 | | 9 | 10 | | 10 | 24 | | 11 | 9 | | 12 | 2 | | 13 | 7 | | 14 | 28 | | 15 | 13 | | 16 | 17 | | 17 | 16 | | 18 | 15 | | 19 | 5 | | 20 | 9 | | 21 | 35 | | 22 | 16 | | 23 | 5 | | 24 | 16 | | 25 | 15 | | 26 | 17 | | 27 | 3 | | 28 | 13 | | 29 | 2 | | 30 | 13 | | 31 | 13 | | 32 | 6 | | 33 | 9 | | 34 | 15 | | 35 | 4 | | 36 | 12 | | 37 | 15 | | 38 | 6 | | 39 | 20 | | 40 | 29 | | 41 | 3 | | 42 | 13 | | 43 | 5 | | 44 | 8 | | 45 | 14 | | 46 | 7 | | 47 | 17 | | 48 | 7 | | 49 | 2 |
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| 59.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3949579831932773 | | totalSentences | 119 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 79 | | matches | | 0 | "She checked her wristwatch, the" | | 1 | "She was late." | | 2 | "She flashed her badge at" | | 3 | "She kept her gaze forward," | | 4 | "She wouldn't let them see" | | 5 | "She found the crime scene" | | 6 | "She wore a oversized knit" | | 7 | "She gestured to the corpse." | | 8 | "His skin, where it wasn't" | | 9 | "He wore a velvet waistcoat" | | 10 | "She ignored the smell of" | | 11 | "Her satchel, heavy with books," | | 12 | "She looked at the floor" | | 13 | "She reached down and gently" | | 14 | "It wasn't pointing at the" | | 15 | "It was pointing at a" | | 16 | "They were stacked haphazardly, covered" | | 17 | "She lifted the corner of" | | 18 | "It was chewing on something." | | 19 | "She stepped back over the" |
| | ratio | 0.278 | |
| 10.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 79 | | matches | | 0 | "Water dripped from the rusted" | | 1 | "The air down here tasted" | | 2 | "Detective Harlow Quinn stepped over" | | 3 | "She checked her wristwatch, the" | | 4 | "She was late." | | 5 | "She flashed her badge at" | | 6 | "The young man looked green," | | 7 | "Harlow ignored the tremor in" | | 8 | "The market was in full" | | 9 | "She kept her gaze forward," | | 10 | "She wouldn't let them see" | | 11 | "She found the crime scene" | | 12 | "Eva Kowalski crouched by the" | | 13 | "She wore a oversized knit" | | 14 | "Harlow’s oldest friend didn’t look" | | 15 | "Harloq ducked under the tape." | | 16 | "Eva finally stood, tucking a" | | 17 | "She gestured to the corpse." | | 18 | "The victim lay sprawled on" | | 19 | "His skin, where it wasn't" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 79 | | matches | (empty) | | ratio | 0 | |
| 30.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 6 | | matches | | 0 | "Water dripped from the rusted concave roof of the abandoned Tube station, a rhythmic *plink, plink, plink* that echoed against the tiled walls." | | 1 | "The young man looked green, his eyes darting around the flickering stalls of the Veil Market as if expecting the gargoyles carved into the brickwork to come ali…" | | 2 | "She found the crime scene cordoned off near the old ticket booth, a faded barrier of police tape that looked embarrassingly mundane against the backdrop of ench…" | | 3 | "The victim lay sprawled on his back, arms thrown wide as if embracing the grimy ceiling." | | 4 | "She stepped back over the tape, her eyes locking onto a figure in a dark cloak standing near the exit, the hem of their garment stained with a distinctive green…" | | 5 | "Harloq was already moving, cutting through the crowd, her hand drifting to the holster at her hip." |
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| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva suggested, though her voice lacked conviction" | | 1 | "Harloq said, her mind racing, piecing together the fractured narrative" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Eva suggested (suggest)" |
| | dialogueSentences | 54 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0.333 | | effectiveRatio | 0.037 | |