| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Evans stepped back [back]" |
| | dialogueSentences | 34 | | tagDensity | 0.412 | | leniency | 0.824 | | rawRatio | 0.071 | | effectiveRatio | 0.059 | |
| 88.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1350 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "completely" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1350 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "traced" | | 1 | "firmly" | | 2 | "trembled" | | 3 | "vibrated" | | 4 | "shattered" | | 5 | "fractured" | | 6 | "standard" | | 7 | "quivered" | | 8 | "weight" | | 9 | "pulsed" | | 10 | "intricate" |
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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 | 135 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 135 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 18 | | totalWords | 1350 | | ratio | 0.013 | | matches | | 0 | "The compass is a lie. The needle points to the hunger. Do not let it hear you breathe." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1090 | | uniqueNames | 19 | | maxNameDensity | 0.83 | | worstName | "Evans" | | maxWindowNameDensity | 2 | | worstWindowName | "Evans" | | discoveredNames | | London | 2 | | Tube | 1 | | Camden | 1 | | Met | 1 | | Evans | 9 | | Eva | 2 | | Kowalski | 3 | | Veil | 1 | | Compass | 1 | | Morris | 1 | | Thames | 1 | | Held | 1 | | British | 1 | | Museum | 1 | | Sumerian | 1 | | Tudor | 1 | | Market | 1 | | Shade | 1 | | Oxford | 1 |
| | persons | | 0 | "Met" | | 1 | "Evans" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Compass" | | 5 | "Morris" | | 6 | "Museum" | | 7 | "Tudor" |
| | places | | 0 | "London" | | 1 | "Thames" | | 2 | "Oxford" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1350 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 37.5 | | std | 33.7 | | cv | 0.899 | | sampleLengths | | 0 | 32 | | 1 | 39 | | 2 | 6 | | 3 | 43 | | 4 | 4 | | 5 | 31 | | 6 | 6 | | 7 | 41 | | 8 | 43 | | 9 | 102 | | 10 | 7 | | 11 | 29 | | 12 | 14 | | 13 | 141 | | 14 | 5 | | 15 | 2 | | 16 | 64 | | 17 | 4 | | 18 | 6 | | 19 | 53 | | 20 | 33 | | 21 | 65 | | 22 | 110 | | 23 | 13 | | 24 | 87 | | 25 | 50 | | 26 | 58 | | 27 | 18 | | 28 | 10 | | 29 | 73 | | 30 | 12 | | 31 | 4 | | 32 | 66 | | 33 | 20 | | 34 | 49 | | 35 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 177 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1090 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.022935779816513763 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.011009174311926606 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 8.77 | | std | 5.42 | | cv | 0.618 | | sampleLengths | | 0 | 8 | | 1 | 24 | | 2 | 6 | | 3 | 3 | | 4 | 19 | | 5 | 6 | | 6 | 5 | | 7 | 6 | | 8 | 14 | | 9 | 8 | | 10 | 10 | | 11 | 6 | | 12 | 5 | | 13 | 4 | | 14 | 6 | | 15 | 9 | | 16 | 3 | | 17 | 3 | | 18 | 10 | | 19 | 6 | | 20 | 9 | | 21 | 4 | | 22 | 7 | | 23 | 21 | | 24 | 16 | | 25 | 5 | | 26 | 1 | | 27 | 8 | | 28 | 4 | | 29 | 5 | | 30 | 4 | | 31 | 7 | | 32 | 12 | | 33 | 20 | | 34 | 13 | | 35 | 4 | | 36 | 6 | | 37 | 21 | | 38 | 9 | | 39 | 10 | | 40 | 7 | | 41 | 19 | | 42 | 10 | | 43 | 6 | | 44 | 8 | | 45 | 4 | | 46 | 7 | | 47 | 15 | | 48 | 11 | | 49 | 9 |
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| 48.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 25 | | diversityRatio | 0.38961038961038963 | | totalSentences | 154 | | uniqueOpeners | 60 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 128 | | matches | | 0 | "Just three parallel gashes across" | | 1 | "Only a single folded sheet" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 128 | | matches | | 0 | "I ducked under the yellow" | | 1 | "He did not look up" | | 2 | "His leather satchel rested on" | | 3 | "I recognised the worn leather" | | 4 | "It belonged to Eva Kowalski." | | 5 | "I knelt beside the chalk" | | 6 | "His eyes were bloodshot." | | 7 | "He had been down here" | | 8 | "I traced the air above" | | 9 | "I stood and reached into" | | 10 | "My fingers closed around the" | | 11 | "I had acquired the device" | | 12 | "I Held it level." | | 13 | "It spun in tight, frantic" | | 14 | "I let the device hang" | | 15 | "We crossed the platform." | | 16 | "I raised a palm" | | 17 | "I pointed to the wall" | | 18 | "I knelt and pressed a" | | 19 | "I followed his light" |
| | ratio | 0.281 | |
| 38.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 128 | | matches | | 0 | "The cordons were up before" | | 1 | "I ducked under the yellow" | | 2 | "This was not a London" | | 3 | "This was nowhere." | | 4 | "An abandoned Tube station buried" | | 5 | "The Met usually avoided these" | | 6 | "Tonight, they had no choice." | | 7 | "DS Evans stood by the" | | 8 | "He did not look up" | | 9 | "His leather satchel rested on" | | 10 | "I recognised the worn leather" | | 11 | "It belonged to Eva Kowalski." | | 12 | "I knelt beside the chalk" | | 13 | "The victim lay supine, arms" | | 14 | "Evans finally met my gaze" | | 15 | "His eyes were bloodshot." | | 16 | "He had been down here" | | 17 | "I traced the air above" | | 18 | "The copper stung my nostrils." | | 19 | "Ozone and crushed thyme." |
| | ratio | 0.844 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 1 | | matches | | 0 | "The walls here bore decades of layered advertising posters, their edges peeling back to reveal raw concrete underneath." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "Evans stepped, his torch beam sweeping the corridor" | | 1 | "Evans stepped back, his weapon shaking slightly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 34 | | tagDensity | 0.029 | | leniency | 0.059 | | rawRatio | 1 | | effectiveRatio | 0.059 | |