| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "She looked around [around]" |
| | dialogueSentences | 8 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 90.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1107 | | 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) | |
| 95.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1107 | | totalAiIsms | 1 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1098 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1044 | | uniqueNames | 10 | | maxNameDensity | 0.77 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Herrera | 6 | | Raven | 2 | | Nest | 2 | | Camden | 2 | | Tube | 2 | | Morris | 3 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Camden" | | 5 | "Morris" | | 6 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1098 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 42.23 | | std | 30.36 | | cv | 0.719 | | sampleLengths | | 0 | 55 | | 1 | 81 | | 2 | 5 | | 3 | 41 | | 4 | 2 | | 5 | 79 | | 6 | 38 | | 7 | 79 | | 8 | 25 | | 9 | 5 | | 10 | 101 | | 11 | 25 | | 12 | 33 | | 13 | 57 | | 14 | 42 | | 15 | 33 | | 16 | 3 | | 17 | 72 | | 18 | 31 | | 19 | 3 | | 20 | 116 | | 21 | 36 | | 22 | 47 | | 23 | 50 | | 24 | 25 | | 25 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 73 | | matches | | |
| 49.34% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 177 | | matches | | 0 | "was running" | | 1 | "was selling" | | 2 | "was sliding" | | 3 | "was haggling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 75 | | ratio | 0.107 | | matches | | 0 | "Herrera was fast—faster than a man carrying a bag had any right to be—and he moved like he knew exactly where every loose flagstone and rusted drainpipe sat." | | 1 | "\"—Camden Tube, the closed one. I'm going in.\"" | | 2 | "Quinn looked at the handset, then at the open black mouth of the door, and made the decision the way she made all the ones that mattered—fast, and against her own better judgement." | | 3 | "She fumbled the torch from her belt and thumbed it on; the beam carved a cone through dust and dripping air." | | 4 | "Voices—low, layered, a murmuring tide that swelled up the corridor from somewhere below." | | 5 | "Lanterns hung from the curved ceiling on chains, but they burned with light that was wrong colours—deep violet, a green like the sign over the Raven's Nest, a red that pulsed as though it breathed." | | 6 | "Figures moved between the stalls—too many of them, and some of them too tall, or too still, or wearing faces that didn't sit right beneath their hoods." | | 7 | "Only fear—and, oddly, something like pity." |
| |
| 98.60% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 625 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.0416 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0064 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 14.64 | | std | 10.93 | | cv | 0.747 | | sampleLengths | | 0 | 22 | | 1 | 33 | | 2 | 3 | | 3 | 2 | | 4 | 23 | | 5 | 23 | | 6 | 30 | | 7 | 3 | | 8 | 2 | | 9 | 3 | | 10 | 4 | | 11 | 34 | | 12 | 2 | | 13 | 14 | | 14 | 28 | | 15 | 2 | | 16 | 35 | | 17 | 9 | | 18 | 20 | | 19 | 5 | | 20 | 4 | | 21 | 18 | | 22 | 39 | | 23 | 8 | | 24 | 14 | | 25 | 3 | | 26 | 22 | | 27 | 5 | | 28 | 19 | | 29 | 26 | | 30 | 4 | | 31 | 23 | | 32 | 6 | | 33 | 23 | | 34 | 9 | | 35 | 6 | | 36 | 2 | | 37 | 2 | | 38 | 6 | | 39 | 33 | | 40 | 4 | | 41 | 32 | | 42 | 5 | | 43 | 8 | | 44 | 8 | | 45 | 16 | | 46 | 26 | | 47 | 33 | | 48 | 3 | | 49 | 7 |
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| 72.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.48 | | totalSentences | 75 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 66 | | matches | | 0 | "Of course he didn't." | | 1 | "Even the hoarding seemed to" | | 2 | "Only fear—and, oddly, something like" |
| | ratio | 0.045 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 66 | | matches | | 0 | "She knew him." | | 1 | "She'd watched the Raven's Nest" | | 2 | "He didn't stop." | | 3 | "He glanced back once, just" | | 4 | "She'd worked these streets for" | | 5 | "He vaulted a low wall" | | 6 | "She hit it half a" | | 7 | "She ran through it." | | 8 | "They came out into a" | | 9 | "He pressed his palm flat" | | 10 | "He vanished into the dark." | | 11 | "Her left hand went to" | | 12 | "She'd gone in after him" | | 13 | "She'd come out alone, and" | | 14 | "You don't know what's down" | | 15 | "She drew her radio." | | 16 | "She looked around for a" | | 17 | "She went in." | | 18 | "She fumbled the torch from" | | 19 | "She followed the slope, ankle" |
| | ratio | 0.364 | |
| 50.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 66 | | matches | | 0 | "Rain came down in sheets," | | 1 | "Detective Harlow Quinn ran with" | | 2 | "She knew him." | | 3 | "She'd watched the Raven's Nest" | | 4 | "He didn't stop." | | 5 | "He glanced back once, just" | | 6 | "The alley funneled the rain" | | 7 | "Herrera was fast—faster than a" | | 8 | "She'd worked these streets for" | | 9 | "He vaulted a low wall" | | 10 | "She hit it half a" | | 11 | "Pain lanced up her shin." | | 12 | "She ran through it." | | 13 | "They came out into a" | | 14 | "Herrera ducked through a chain-link" | | 15 | "A boarded entrance to a" | | 16 | "Quinn had passed a hundred" | | 17 | "Herrera didn't slow." | | 18 | "He pressed his palm flat" | | 19 | "He vanished into the dark." |
| | ratio | 0.818 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 43.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn ran with her coat plastered to her back, her boots throwing up sprays of dirty water, her eyes fixed on the man weaving through the late-…" | | 1 | "Herrera ducked through a chain-link gate that should have been padlocked and wasn't, down a flight of concrete steps slick with moss, toward a hoarding plastere…" | | 2 | "Quinn looked at the handset, then at the open black mouth of the door, and made the decision the way she made all the ones that mattered—fast, and against her o…" | | 3 | "Voices—low, layered, a murmuring tide that swelled up the corridor from somewhere below." | | 4 | "Figures moved between the stalls—too many of them, and some of them too tall, or too still, or wearing faces that didn't sit right beneath their hoods." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.333 | | effectiveRatio | 0.25 | |