| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 99 | | tagDensity | 0.051 | | leniency | 0.101 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2338 | | 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) | |
| 89.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2338 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "dance" | | 2 | "scanned" | | 3 | "blown wide" | | 4 | "etching" |
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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 | 180 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 180 | | filterMatches | | | hedgeMatches | | 0 | "started to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 274 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2338 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1488 | | uniqueNames | 9 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 29 | | Camden | 1 | | Sergeant | 1 | | Colin | 1 | | Pike | 25 | | Underground | 1 | | Kowalski | 1 | | Eva | 16 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Colin" | | 4 | "Pike" | | 5 | "Underground" | | 6 | "Kowalski" | | 7 | "Eva" |
| | places | (empty) | | globalScore | 0.526 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he’d slept in his shirt and w" |
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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 | 2338 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 274 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 177 | | mean | 13.21 | | std | 16.48 | | cv | 1.247 | | sampleLengths | | 0 | 102 | | 1 | 16 | | 2 | 8 | | 3 | 10 | | 4 | 4 | | 5 | 8 | | 6 | 5 | | 7 | 102 | | 8 | 48 | | 9 | 4 | | 10 | 25 | | 11 | 7 | | 12 | 43 | | 13 | 52 | | 14 | 3 | | 15 | 22 | | 16 | 3 | | 17 | 9 | | 18 | 35 | | 19 | 4 | | 20 | 4 | | 21 | 17 | | 22 | 24 | | 23 | 22 | | 24 | 2 | | 25 | 7 | | 26 | 6 | | 27 | 50 | | 28 | 3 | | 29 | 3 | | 30 | 10 | | 31 | 3 | | 32 | 2 | | 33 | 56 | | 34 | 4 | | 35 | 6 | | 36 | 3 | | 37 | 2 | | 38 | 4 | | 39 | 25 | | 40 | 4 | | 41 | 22 | | 42 | 4 | | 43 | 17 | | 44 | 5 | | 45 | 66 | | 46 | 3 | | 47 | 3 | | 48 | 11 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 180 | | matches | | 0 | "been removed" | | 1 | "been smeared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 243 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 274 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1494 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.020749665327978582 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002008032128514056 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 274 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 274 | | mean | 8.53 | | std | 7.5 | | cv | 0.879 | | sampleLengths | | 0 | 26 | | 1 | 9 | | 2 | 5 | | 3 | 7 | | 4 | 26 | | 5 | 8 | | 6 | 2 | | 7 | 3 | | 8 | 16 | | 9 | 16 | | 10 | 8 | | 11 | 10 | | 12 | 4 | | 13 | 8 | | 14 | 5 | | 15 | 46 | | 16 | 1 | | 17 | 7 | | 18 | 2 | | 19 | 15 | | 20 | 17 | | 21 | 14 | | 22 | 15 | | 23 | 6 | | 24 | 15 | | 25 | 12 | | 26 | 4 | | 27 | 25 | | 28 | 7 | | 29 | 43 | | 30 | 4 | | 31 | 26 | | 32 | 22 | | 33 | 3 | | 34 | 22 | | 35 | 3 | | 36 | 9 | | 37 | 6 | | 38 | 6 | | 39 | 7 | | 40 | 5 | | 41 | 2 | | 42 | 3 | | 43 | 6 | | 44 | 4 | | 45 | 4 | | 46 | 8 | | 47 | 9 | | 48 | 5 | | 49 | 19 |
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| 57.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3759124087591241 | | totalSentences | 274 | | uniqueOpeners | 103 | |
| 86.58% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 154 | | matches | | 0 | "Instead the blood had pooled" | | 1 | "Too little on the front" | | 2 | "Too much beneath the shoulders." | | 3 | "More green powder." |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 154 | | matches | | 0 | "Her leather watch pressed cold" | | 1 | "His throat held a wound" | | 2 | "He rose when he saw" | | 3 | "His tie hung loose, his" | | 4 | "His pupils looked blown wide." | | 5 | "His lips had gone pale" | | 6 | "She handed the sleeve back" | | 7 | "His ring finger bore a" | | 8 | "She followed the blood line" | | 9 | "She walked a slow circle" | | 10 | "She touched none of it." | | 11 | "Her gaze dropped to the" | | 12 | "He gave a dry laugh." | | 13 | "She handed the sheet back." | | 14 | "She tucked a coil of" | | 15 | "He retreated with the relief" | | 16 | "She had heard stranger words" | | 17 | "She crouched, leaned, then looked" | | 18 | "She bent over the victim’s" | | 19 | "She angled the nearest lamp" |
| | ratio | 0.162 | |
| 47.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 154 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The old Camden station held" | | 2 | "Grime glazed the tiled walls." | | 3 | "Soot stained the curve of" | | 4 | "A line of weak work" | | 5 | "The place smelled wrong for" | | 6 | "Her leather watch pressed cold" | | 7 | "A uniform at the barrier" | | 8 | "Quinn didn’t look back." | | 9 | "The constable shut his mouth." | | 10 | "His throat held a wound" | | 11 | "Detective Sergeant Colin Pike crouched" | | 12 | "He rose when he saw" | | 13 | "Pike looked like he’d slept" | | 14 | "His tie hung loose, his" | | 15 | "Pike gave the corpse a" | | 16 | "Quinn scanned the platform." | | 17 | "Quinn moved closer to the" | | 18 | "The dead man’s eyes remained" | | 19 | "His pupils looked blown wide." |
| | ratio | 0.825 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 154 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 62 | | technicalSentenceCount | 1 | | matches | | 0 | "At the centre of the platform, under an enamel sign that still claimed NORTHBOUND, a man lay on his back with his coat peeled open and both hands spread from hi…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 99 | | tagDensity | 0.04 | | leniency | 0.081 | | rawRatio | 0.25 | | effectiveRatio | 0.02 | |