| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 94 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2146 | | totalAiIsmAdverbs | 1 | | 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) | |
| 81.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2146 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "silence" | | 1 | "weight" | | 2 | "mechanical" | | 3 | "etched" | | 4 | "shattered" | | 5 | "whisper" | | 6 | "flickered" |
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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 | 209 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 209 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 287 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2146 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 99 | | wordCount | 1628 | | uniqueNames | 13 | | maxNameDensity | 2.58 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 42 | | Tube | 1 | | London | 1 | | Eva | 31 | | Kowalski | 1 | | Christmas | 1 | | Rook | 4 | | Basil | 8 | | Morris | 2 | | One | 3 | | Blood | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Rook" | | 5 | "Basil" | | 6 | "Morris" | | 7 | "One" | | 8 | "Blood" |
| | places | | | globalScore | 0.21 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 116 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.466 | | wordCount | 2146 | | matches | | 0 | "not pupils but depth, as if his skull had become a window into a lightless" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 287 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 174 | | mean | 12.33 | | std | 14.03 | | cv | 1.138 | | sampleLengths | | 0 | 10 | | 1 | 9 | | 2 | 5 | | 3 | 51 | | 4 | 6 | | 5 | 50 | | 6 | 62 | | 7 | 5 | | 8 | 14 | | 9 | 3 | | 10 | 5 | | 11 | 6 | | 12 | 34 | | 13 | 54 | | 14 | 6 | | 15 | 5 | | 16 | 40 | | 17 | 4 | | 18 | 3 | | 19 | 1 | | 20 | 4 | | 21 | 30 | | 22 | 10 | | 23 | 6 | | 24 | 42 | | 25 | 29 | | 26 | 14 | | 27 | 42 | | 28 | 8 | | 29 | 5 | | 30 | 4 | | 31 | 10 | | 32 | 3 | | 33 | 3 | | 34 | 4 | | 35 | 3 | | 36 | 50 | | 37 | 11 | | 38 | 19 | | 39 | 16 | | 40 | 6 | | 41 | 4 | | 42 | 3 | | 43 | 7 | | 44 | 3 | | 45 | 74 | | 46 | 21 | | 47 | 7 | | 48 | 4 | | 49 | 6 |
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| 98.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 209 | | matches | | 0 | "been scraped" | | 1 | "were fixed" | | 2 | "was broken" | | 3 | "was clenched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 277 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 287 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1629 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.02578268876611418 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003069367710251688 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 287 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 287 | | mean | 7.48 | | std | 5.99 | | cv | 0.802 | | sampleLengths | | 0 | 10 | | 1 | 9 | | 2 | 5 | | 3 | 6 | | 4 | 12 | | 5 | 5 | | 6 | 9 | | 7 | 19 | | 8 | 6 | | 9 | 28 | | 10 | 14 | | 11 | 8 | | 12 | 4 | | 13 | 7 | | 14 | 21 | | 15 | 18 | | 16 | 12 | | 17 | 5 | | 18 | 14 | | 19 | 3 | | 20 | 5 | | 21 | 6 | | 22 | 3 | | 23 | 25 | | 24 | 3 | | 25 | 3 | | 26 | 20 | | 27 | 5 | | 28 | 13 | | 29 | 16 | | 30 | 6 | | 31 | 5 | | 32 | 15 | | 33 | 13 | | 34 | 12 | | 35 | 4 | | 36 | 3 | | 37 | 1 | | 38 | 4 | | 39 | 10 | | 40 | 20 | | 41 | 6 | | 42 | 4 | | 43 | 6 | | 44 | 7 | | 45 | 24 | | 46 | 11 | | 47 | 5 | | 48 | 8 | | 49 | 7 |
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| 43.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.28222996515679444 | | totalSentences | 287 | | uniqueOpeners | 81 | |
| 94.16% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 177 | | matches | | 0 | "Even the dead air had" | | 1 | "Then her torch caught a" | | 2 | "Too many joints." | | 3 | "Then the dead man laughed." | | 4 | "Then the station lights flickered" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 177 | | matches | | 0 | "They groaned in their tunnels" | | 1 | "Her torch beam cut across" | | 2 | "They had all gone still." | | 3 | "Her boots made no sound." | | 4 | "She looked down." | | 5 | "His throat had been cut." | | 6 | "Her curly red hair had" | | 7 | "She had been twelve the" | | 8 | "She did not look at" | | 9 | "She examined the victim’s shoes," | | 10 | "Her antlers cast branching shadows" | | 11 | "She checked her watch." | | 12 | "Its second hand ticked steadily." | | 13 | "Her watch showed 2:43." | | 14 | "His right hand was clenched" | | 15 | "She leaned closer." | | 16 | "It gleamed like wet coal" | | 17 | "She snapped the torch toward" | | 18 | "They began at the black" | | 19 | "She followed the trail without" |
| | ratio | 0.181 | |
| 44.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 147 | | totalSentences | 177 | | matches | | 0 | "The bone token was warm" | | 1 | "That was the first thing" | | 2 | "The second was the silence." | | 3 | "They groaned in their tunnels" | | 4 | "Water ticked through cracked tiles." | | 5 | "Rats rustled behind advertisements for" | | 6 | "This place had none of" | | 7 | "Quinn stood at the bottom" | | 8 | "Her torch beam cut across" | | 9 | "Stalls crowded the platform beneath" | | 10 | "Shelves displayed stoppered bottles full" | | 11 | "An old woman with antlers" | | 12 | "A man with no visible" | | 13 | "They had all gone still." | | 14 | "Quinn stepped forward." | | 15 | "Her boots made no sound." | | 16 | "That was the third thing" | | 17 | "She looked down." | | 18 | "Dust filmed the concrete in" | | 19 | "None went out." |
| | ratio | 0.831 | |
| 56.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 177 | | matches | | 0 | "Whoever had made them had" | | 1 | "Now it spun." |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 2 | | matches | | 0 | "Even the dead air had a pressure to it, a low mechanical thrum that belonged to the buried city." | | 1 | "Two candles burned on either side of the corpse, their flames standing perfectly upright despite the cold draft moving through the station." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 2 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 94 | | tagDensity | 0.17 | | leniency | 0.34 | | rawRatio | 0.125 | | effectiveRatio | 0.043 | |