| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said softly [softly]" | | 1 | "She peeled back [back]" |
| | dialogueSentences | 43 | | tagDensity | 0.512 | | leniency | 1 | | rawRatio | 0.091 | | effectiveRatio | 0.091 | |
| 96.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1358 | | 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) | |
| 30.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1358 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "echoing" | | 1 | "flickered" | | 2 | "shattered" | | 3 | "scanning" | | 4 | "etched" | | 5 | "quivered" | | 6 | "pulse" | | 7 | "flicked" | | 8 | "trembled" | | 9 | "depths" | | 10 | "echoed" | | 11 | "quickened" | | 12 | "spectral" | | 13 | "aftermath" | | 14 | "scanned" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1343 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 20.13% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 924 | | uniqueNames | 12 | | maxNameDensity | 2.6 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 24 | | Camden | 1 | | Tube | 1 | | Eva | 14 | | Kowalski | 1 | | Veil | 2 | | Compass | 1 | | Shade | 1 | | Morris | 3 | | Portal | 1 | | Supernatural | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Veil" | | 6 | "Compass" | | 7 | "Morris" |
| | places | (empty) | | globalScore | 0.201 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | 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 | 1343 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 85.37% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 39.5 | | std | 17.73 | | cv | 0.449 | | sampleLengths | | 0 | 63 | | 1 | 59 | | 2 | 56 | | 3 | 32 | | 4 | 47 | | 5 | 42 | | 6 | 64 | | 7 | 21 | | 8 | 34 | | 9 | 50 | | 10 | 30 | | 11 | 12 | | 12 | 54 | | 13 | 41 | | 14 | 37 | | 15 | 35 | | 16 | 10 | | 17 | 73 | | 18 | 51 | | 19 | 16 | | 20 | 47 | | 21 | 38 | | 22 | 52 | | 23 | 61 | | 24 | 9 | | 25 | 39 | | 26 | 9 | | 27 | 48 | | 28 | 21 | | 29 | 34 | | 30 | 59 | | 31 | 12 | | 32 | 57 | | 33 | 30 |
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| 95.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 106 | | matches | | 0 | "been sealed" | | 1 | "was caved" | | 2 | "were cauterized" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 28.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 2 | | flaggedSentences | 5 | | totalSentences | 125 | | ratio | 0.04 | | matches | | 0 | "Quinn checked her leather‐strapped watch—worn from years on duty—and noted the time: just past midnight." | | 1 | "The compass needle quivered, then snapped north—toward the far end of the platform—where the tunnel descended into shadow." | | 2 | "Three parallel slashes—too straight, too precise—lay just above the bone." | | 3 | "No blood oozed; the cuts were cauterized, crisp as if seared shut." | | 4 | "“No. These are ritual scars. They line up with glyphs I’ve seen in Shade texts. Protection wards, inverted.” She dabbed her glove on the platform; the dark smear glowed faintly under her torch’s UV setting." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 933 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.03858520900321544 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00857449088960343 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 10.74 | | std | 7.53 | | cv | 0.701 | | sampleLengths | | 0 | 20 | | 1 | 10 | | 2 | 16 | | 3 | 17 | | 4 | 15 | | 5 | 12 | | 6 | 12 | | 7 | 20 | | 8 | 17 | | 9 | 19 | | 10 | 13 | | 11 | 7 | | 12 | 8 | | 13 | 15 | | 14 | 9 | | 15 | 10 | | 16 | 10 | | 17 | 12 | | 18 | 12 | | 19 | 3 | | 20 | 5 | | 21 | 18 | | 22 | 19 | | 23 | 7 | | 24 | 18 | | 25 | 14 | | 26 | 8 | | 27 | 11 | | 28 | 6 | | 29 | 4 | | 30 | 7 | | 31 | 10 | | 32 | 5 | | 33 | 6 | | 34 | 17 | | 35 | 6 | | 36 | 6 | | 37 | 15 | | 38 | 15 | | 39 | 12 | | 40 | 2 | | 41 | 12 | | 42 | 18 | | 43 | 2 | | 44 | 10 | | 45 | 11 | | 46 | 12 | | 47 | 17 | | 48 | 7 | | 49 | 7 |
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| 55.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.36 | | totalSentences | 125 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 98 | | matches | | 0 | "Away from the weak station" | | 1 | "Only cratered shadows and the" | | 2 | "Somewhere in the shadows, Quinn" |
| | ratio | 0.031 | |
| 77.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 98 | | matches | | 0 | "She brushed a lock of" | | 1 | "She inhaled once, tasted damp" | | 2 | "She drew a chalk outline" | | 3 | "She didn’t see shattered glass," | | 4 | "Her jaw tightened." | | 5 | "She bent closer, scanning the" | | 6 | "She lifted it with a" | | 7 | "she said, voice light" | | 8 | "It pointed rigidly down the" | | 9 | "She cut off, mindful of" | | 10 | "She checked her left wrist," | | 11 | "She remembered DS Morris, partner" | | 12 | "She felt the old ache" | | 13 | "she said softly, returning the" | | 14 | "She noted the faint scratch" | | 15 | "She peeled back the man’s" | | 16 | "She glanced up at Quinn." | | 17 | "She tucked another strand of" | | 18 | "She heard herself say it," | | 19 | "She set the torch on" |
| | ratio | 0.357 | |
| 5.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 98 | | matches | | 0 | "Detective Harlow Quinn stepped onto" | | 1 | "The tunnel lights flickered, revealing" | | 2 | "She brushed a lock of" | | 3 | "A thin sheet of mist" | | 4 | "Quinn checked her leather‐strapped watch—worn" | | 5 | "The station had been sealed" | | 6 | "She inhaled once, tasted damp" | | 7 | "A body lay half on" | | 8 | "Detective Eva Kowalski crouched beside" | | 9 | "Eva’s curly red hair framed" | | 10 | "Tonight, that knowledge might come" | | 11 | "Eva said, unzipping a chalk" | | 12 | "She drew a chalk outline" | | 13 | "Quinn knelt a few feet" | | 14 | "She didn’t see shattered glass," | | 15 | "The victim’s forehead was caved" | | 16 | "Her jaw tightened." | | 17 | "Quinn’s voice was cool" | | 18 | "She bent closer, scanning the" | | 19 | "A small brass object lay" |
| | ratio | 0.908 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 98 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "A thin sheet of mist curled along the tracks, low and insistent, as if reluctant to dissipate." | | 1 | "She remembered DS Morris, partner lost three years ago in a case that defied explanation." | | 2 | "She noted the faint scratch marks on the rail ties, shallow grooves as if something dragged toe‐first." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 1 | | matches | | 0 | "A jagged memory pricked, voice trembling, shouting about a door in the wall" |
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| 80.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "She heard (hear)" | | 1 | "she whispered (whisper)" | | 2 | "She snapped on (snap on)" |
| | dialogueSentences | 43 | | tagDensity | 0.163 | | leniency | 0.326 | | rawRatio | 0.429 | | effectiveRatio | 0.14 | |