| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow turned back [back]" |
| | dialogueSentences | 45 | | tagDensity | 0.467 | | leniency | 0.933 | | rawRatio | 0.048 | | effectiveRatio | 0.044 | |
| 92.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1264 | | totalAiIsmAdverbs | 2 | | 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) | |
| 52.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1264 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "oppressive" | | 1 | "silence" | | 2 | "fluttered" | | 3 | "stark" | | 4 | "gloom" | | 5 | "scanned" | | 6 | "facade" | | 7 | "etched" | | 8 | "flickered" | | 9 | "racing" | | 10 | "dancing" | | 11 | "whisper" |
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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 | 84 | | matches | (empty) | |
| 74.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1264 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 814 | | uniqueNames | 11 | | maxNameDensity | 2.58 | | worstName | "Harlow" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Harlow | 21 | | Quinn | 2 | | Tube | 1 | | Veil | 2 | | Market | 1 | | Eva | 14 | | Kowalski | 1 | | Oxford | 1 | | Compass | 1 | | Morris | 3 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Compass" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.21 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 1 | | matches | | 0 | "sigils that seemed to shift when she looked at them directly" |
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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 | 1264 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 33.26 | | std | 22.02 | | cv | 0.662 | | sampleLengths | | 0 | 94 | | 1 | 63 | | 2 | 31 | | 3 | 37 | | 4 | 64 | | 5 | 41 | | 6 | 24 | | 7 | 37 | | 8 | 66 | | 9 | 25 | | 10 | 31 | | 11 | 12 | | 12 | 84 | | 13 | 7 | | 14 | 5 | | 15 | 44 | | 16 | 1 | | 17 | 66 | | 18 | 7 | | 19 | 40 | | 20 | 29 | | 21 | 3 | | 22 | 52 | | 23 | 32 | | 24 | 30 | | 25 | 20 | | 26 | 30 | | 27 | 36 | | 28 | 25 | | 29 | 36 | | 30 | 17 | | 31 | 20 | | 32 | 6 | | 33 | 60 | | 34 | 25 | | 35 | 17 | | 36 | 16 | | 37 | 31 |
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| 88.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 84 | | matches | | 0 | "was supposed" | | 1 | "was cropped" | | 2 | "was etched" | | 3 | "been laughed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 152 | | matches | | 0 | "was fidgeting" | | 1 | "were already tracking" | | 2 | "was using" | | 3 | "were ticking" | | 4 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 108 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 817 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.028151774785801713 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.011015911872705019 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 11.7 | | std | 7.74 | | cv | 0.661 | | sampleLengths | | 0 | 21 | | 1 | 28 | | 2 | 20 | | 3 | 1 | | 4 | 24 | | 5 | 12 | | 6 | 11 | | 7 | 20 | | 8 | 20 | | 9 | 15 | | 10 | 16 | | 11 | 14 | | 12 | 17 | | 13 | 6 | | 14 | 23 | | 15 | 5 | | 16 | 20 | | 17 | 4 | | 18 | 2 | | 19 | 10 | | 20 | 7 | | 21 | 19 | | 22 | 15 | | 23 | 5 | | 24 | 12 | | 25 | 1 | | 26 | 2 | | 27 | 4 | | 28 | 12 | | 29 | 25 | | 30 | 6 | | 31 | 13 | | 32 | 9 | | 33 | 18 | | 34 | 17 | | 35 | 3 | | 36 | 7 | | 37 | 18 | | 38 | 8 | | 39 | 4 | | 40 | 16 | | 41 | 3 | | 42 | 12 | | 43 | 9 | | 44 | 7 | | 45 | 9 | | 46 | 10 | | 47 | 23 | | 48 | 19 | | 49 | 2 |
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| 52.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.37383177570093457 | | totalSentences | 107 | | uniqueOpeners | 40 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Just a look of absolute" |
| | ratio | 0.014 | |
| 74.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 74 | | matches | | 0 | "She descended the rusted grate" | | 1 | "Her leather watch, worn soft" | | 2 | "She was fidgeting with the" | | 3 | "Her salt-and-pepper hair was cropped" | | 4 | "She tucked a curl behind" | | 5 | "She stopped beside the body." | | 6 | "She pulled a pair of" | | 7 | "She pulled a handkerchief from" | | 8 | "She reached into the victim's" | | 9 | "It was a compass, but" | | 10 | "It pointed directly at the" | | 11 | "She remembered DS Morris, three" | | 12 | "She had spent the last" | | 13 | "She had been laughed at" | | 14 | "It was fresh." | | 15 | "She looked at Eva" | | 16 | "It felt heavy, colder than" | | 17 | "She checked her watch again." | | 18 | "She thought of the empty" | | 19 | "She thought of the colleagues" |
| | ratio | 0.365 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 74 | | matches | | 0 | "The air beneath Camden tasted" | | 1 | "She descended the rusted grate" | | 2 | "Her leather watch, worn soft" | | 3 | "The Veil Market was supposed" | | 4 | "She was fidgeting with the" | | 5 | "Detective Quinn approached, her movement" | | 6 | "Her salt-and-pepper hair was cropped" | | 7 | "Eva said, her voice trembling" | | 8 | "She tucked a curl behind" | | 9 | "Harlow replied, though her eyes" | | 10 | "She stopped beside the body." | | 11 | "The victim was a man," | | 12 | "Eva said, kneeling" | | 13 | "She pulled a pair of" | | 14 | "Harlow crouched, her knees popping." | | 15 | "She pulled a handkerchief from" | | 16 | "Eva said, her green eyes" | | 17 | "Harlow stood, her sharp jaw" | | 18 | "She reached into the victim's" | | 19 | "It was a compass, but" |
| | ratio | 0.932 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 6 | | matches | | 0 | "The air beneath Camden tasted of wet iron and ozone, a metallic tang that coated the back of Harlow Quinn’s throat." | | 1 | "She descended the rusted grate of the abandoned Tube station, her boots clicking against the concrete steps with a rhythm that felt too loud in the oppressive s…" | | 2 | "Yellow police tape fluttered in a draft that shouldn't exist underground." | | 3 | "She was fidgeting with the strap of her worn leather satchel, her fingers worrying the leather until the fabric squeaked." | | 4 | "The casing was small brass, tarnished with a patina of verdigris that smelled of old pennies and rain." | | 5 | "She had spent the last three years chasing ghosts, convinced the clique was using the city's underbelly to move things that shouldn't exist." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 7 | | matches | | 0 | "Eva said, her voice trembling slightly" | | 1 | "Harlow replied, though her eyes were already tracking the scuff marks on the tiles" | | 2 | "Eva said, her green eyes wide behind her round glasses" | | 3 | "Eva suggested, though her voice lacked conviction" | | 4 | "Harlow said, the realization settling in her gut like a stone" | | 5 | "Harlow said, her voice hardening" | | 6 | "Harlow said, her voice dropping to a whisper" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 3 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "Eva suggested (suggest)" | | 2 | "Harlow shouted (shout)" |
| | dialogueSentences | 45 | | tagDensity | 0.356 | | leniency | 0.711 | | rawRatio | 0.188 | | effectiveRatio | 0.133 | |