| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1509 | | 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) | |
| 90.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1509 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulse" | | 1 | "flicked" | | 2 | "footsteps" |
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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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1509 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 89.60% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1490 | | uniqueNames | 10 | | maxNameDensity | 1.21 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 18 | | Silas | 1 | | Camden | 4 | | High | 1 | | Tube | 1 | | Bone | 3 |
| | persons | | 0 | "Nest" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Silas" | | 4 | "Camden" |
| | places | | | globalScore | 0.896 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a courier who had seen too mu" | | 1 | "looked like a hole someone had tried to p" |
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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 | 1509 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 30.18 | | std | 29.08 | | cv | 0.963 | | sampleLengths | | 0 | 80 | | 1 | 15 | | 2 | 66 | | 3 | 54 | | 4 | 13 | | 5 | 7 | | 6 | 49 | | 7 | 19 | | 8 | 3 | | 9 | 27 | | 10 | 3 | | 11 | 27 | | 12 | 100 | | 13 | 32 | | 14 | 46 | | 15 | 8 | | 16 | 68 | | 17 | 7 | | 18 | 58 | | 19 | 53 | | 20 | 10 | | 21 | 6 | | 22 | 6 | | 23 | 78 | | 24 | 15 | | 25 | 70 | | 26 | 5 | | 27 | 6 | | 28 | 25 | | 29 | 90 | | 30 | 41 | | 31 | 1 | | 32 | 23 | | 33 | 43 | | 34 | 4 | | 35 | 17 | | 36 | 2 | | 37 | 3 | | 38 | 11 | | 39 | 1 | | 40 | 53 | | 41 | 65 | | 42 | 4 | | 43 | 3 | | 44 | 2 | | 45 | 50 | | 46 | 6 | | 47 | 103 | | 48 | 10 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 111 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 243 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1493 | | adjectiveStacks | 1 | | stackExamples | | 0 | "plain black unmarked estate" |
| | adverbCount | 47 | | adverbRatio | 0.03148024112525117 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006697923643670462 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 12.79 | | std | 9.1 | | cv | 0.712 | | sampleLengths | | 0 | 11 | | 1 | 25 | | 2 | 26 | | 3 | 18 | | 4 | 15 | | 5 | 22 | | 6 | 7 | | 7 | 9 | | 8 | 7 | | 9 | 21 | | 10 | 21 | | 11 | 7 | | 12 | 1 | | 13 | 2 | | 14 | 2 | | 15 | 21 | | 16 | 13 | | 17 | 7 | | 18 | 24 | | 19 | 10 | | 20 | 15 | | 21 | 19 | | 22 | 3 | | 23 | 12 | | 24 | 15 | | 25 | 3 | | 26 | 27 | | 27 | 4 | | 28 | 23 | | 29 | 7 | | 30 | 19 | | 31 | 11 | | 32 | 36 | | 33 | 13 | | 34 | 8 | | 35 | 11 | | 36 | 8 | | 37 | 7 | | 38 | 31 | | 39 | 8 | | 40 | 11 | | 41 | 5 | | 42 | 6 | | 43 | 8 | | 44 | 38 | | 45 | 7 | | 46 | 11 | | 47 | 13 | | 48 | 14 | | 49 | 8 |
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| 42.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.2966101694915254 | | totalSentences | 118 | | uniqueOpeners | 35 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 102 | | matches | | 0 | "Then the man had slipped" | | 1 | "Then a second set of" | | 2 | "Then a pause, and a" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 102 | | matches | | 0 | "She kept her eyes on" | | 1 | "He wore a charcoal hooded" | | 2 | "He did not look like" | | 3 | "He looked like a courier" | | 4 | "He reached the end of" | | 5 | "He shoved cash at the" | | 6 | "She cut back to her" | | 7 | "She swung into traffic, one" | | 8 | "Her tyres hissed across the" | | 9 | "She came up fast, badge" | | 10 | "He had a head start," | | 11 | "His shoulder clipped a steel" | | 12 | "He glanced back then, just" | | 13 | "His mouth split in a" | | 14 | "She answered by closing the" | | 15 | "He bolted through a service" | | 16 | "Her lungs burned." | | 17 | "He knew the ground better" | | 18 | "He took the steps two" | | 19 | "She heard his boots hit" |
| | ratio | 0.294 | |
| 13.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 102 | | matches | | 0 | "Rain had turned Soho into" | | 1 | "Detective Harlow Quinn stood under" | | 2 | "The worn leather watch on" | | 3 | "She kept her eyes on" | | 4 | "He wore a charcoal hooded" | | 5 | "The other clamped a folded" | | 6 | "He did not look like" | | 7 | "He looked like a courier" | | 8 | "Quinn had spent forty minutes" | | 9 | "A name had surfaced in" | | 10 | "Quinn stepped off the kerb" | | 11 | "He reached the end of" | | 12 | "A cab nosed through a" | | 13 | "The man yanked the rear" | | 14 | "Quinn crossed the road at" | | 15 | "The cabbie’s eyes flicked to" | | 16 | "The man twisted round, face" | | 17 | "He shoved cash at the" | | 18 | "Quinn was already moving." | | 19 | "She cut back to her" |
| | ratio | 0.892 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 102 | | matches | | 0 | "Now he cut down the" | | 1 | "Now it looked like a" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 2 | | matches | | 0 | "His mouth split in a grin that held no humour." | | 1 | "He took the steps two at a time, vanishing beneath the street into a disused entrance framed by old advertising panels and a stained white sign that had once na…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 8 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 1 | | effectiveRatio | 0.25 | |