| 57.14% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "She stepped around [around]" | | 1 | "he replied quietly [quietly]" |
| | dialogueSentences | 16 | | tagDensity | 0.875 | | leniency | 1 | | rawRatio | 0.143 | | effectiveRatio | 0.143 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1575 | | 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) | |
| 20.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1575 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | |
| | highlights | | 0 | "tracing" | | 1 | "footfall" | | 2 | "calculated" | | 3 | "measured" | | 4 | "lurched" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "etched" | | 8 | "traced" | | 9 | "weight" | | 10 | "reverberated" | | 11 | "flickered" | | 12 | "pulse" | | 13 | "depths" | | 14 | "scanned" | | 15 | "flicked" | | 16 | "apprehension" | | 17 | "eyebrow" | | 18 | "loomed" | | 19 | "flicker" | | 20 | "resolve" | | 21 | "shimmered" |
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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 | 1 | | narrationSentences | 155 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 155 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1562 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.77% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1464 | | uniqueNames | 16 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 15 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | London | 1 | | Static | 1 | | Olympian | 1 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Hampstead | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Silas" | | 6 | "Morris" | | 7 | "Static" | | 8 | "Herrera" | | 9 | "Saint" | | 10 | "Christopher" |
| | places | | | globalScore | 0.988 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | 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 | 1562 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 157 | | matches | | |
| 35.71% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 67.91 | | std | 18.68 | | cv | 0.275 | | sampleLengths | | 0 | 79 | | 1 | 98 | | 2 | 81 | | 3 | 81 | | 4 | 78 | | 5 | 70 | | 6 | 82 | | 7 | 72 | | 8 | 77 | | 9 | 79 | | 10 | 82 | | 11 | 42 | | 12 | 65 | | 13 | 29 | | 14 | 44 | | 15 | 65 | | 16 | 63 | | 17 | 75 | | 18 | 55 | | 19 | 83 | | 20 | 25 | | 21 | 48 | | 22 | 89 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 155 | | matches | | 0 | "were etched" | | 1 | "was framed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 283 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 2 | | flaggedSentences | 13 | | totalSentences | 157 | | ratio | 0.083 | | matches | | 0 | "Her body moved with military precision—each footfall calculated, each breath measured." | | 1 | "Rain spatter blurred the neon haze above The Raven’s Nest—Silas’s bar, its green sign scrawled in chipped letters." | | 2 | "She hesitated for a fraction of a second when the gutter beneath her spurted something dark—an irregular sheen that could have been oil or blood." | | 3 | "The walls were etched with chalk diagrams—gears, alchemical symbols, odd runes." | | 4 | "Stalls lined the rocky walls; each booth bore its own lantern, its own wares." | | 5 | "She spotted the hawkish vendor from earlier—a man with a hooked nose weighing something in a brass scale." | | 6 | "A boy with too-large sleeves stacked bone tokens in a basket; his eyes flickered at her like frightened moths." | | 7 | "A trough sat beneath it, empty now—where one deposited a bone token for passage." | | 8 | "She thought of Morris’s final moments—isolated, pursuing a lead in the depths." | | 9 | "A figure emerged—apron stained with dark red, Olympian posture." | | 10 | "He glanced toward the hawkish vendor—eyes rigid with suspicion." | | 11 | "She remembered parading through precinct halls, showing off her decorated record—twenty-two arrests last year, medals for bravery." | | 12 | "Herrera exhaled and pulled the token free—an animal bone curved into the shape of a crescent moon." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1481 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.017555705604321403 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0054017555705604325 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 9.95 | | std | 5.15 | | cv | 0.518 | | sampleLengths | | 0 | 13 | | 1 | 15 | | 2 | 18 | | 3 | 21 | | 4 | 12 | | 5 | 3 | | 6 | 11 | | 7 | 19 | | 8 | 18 | | 9 | 7 | | 10 | 15 | | 11 | 14 | | 12 | 11 | | 13 | 14 | | 14 | 16 | | 15 | 11 | | 16 | 6 | | 17 | 5 | | 18 | 25 | | 19 | 4 | | 20 | 11 | | 21 | 8 | | 22 | 3 | | 23 | 5 | | 24 | 22 | | 25 | 9 | | 26 | 7 | | 27 | 16 | | 28 | 11 | | 29 | 11 | | 30 | 10 | | 31 | 12 | | 32 | 12 | | 33 | 8 | | 34 | 7 | | 35 | 7 | | 36 | 13 | | 37 | 7 | | 38 | 4 | | 39 | 7 | | 40 | 5 | | 41 | 4 | | 42 | 7 | | 43 | 10 | | 44 | 13 | | 45 | 6 | | 46 | 18 | | 47 | 11 | | 48 | 20 | | 49 | 10 |
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| 46.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.33121019108280253 | | totalSentences | 157 | | uniqueOpeners | 52 | |
| 67.57% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 148 | | matches | | 0 | "Instantly the hush was absolute." | | 1 | "Only the drip of water" | | 2 | "Then she saw the fugitive" |
| | ratio | 0.02 | |
| 36.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 68 | | totalSentences | 148 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She had followed the fugitive" | | 2 | "Her body moved with military" | | 3 | "He darted through a rusted" | | 4 | "She hesitated for a fraction" | | 5 | "She forced herself forward." | | 6 | "He leapt inside, his silhouette" | | 7 | "His last transmission had static" | | 8 | "She had never known what" | | 9 | "She squeezed her eyes shut" | | 10 | "She landed on cold concrete," | | 11 | "She followed, each step soaking" | | 12 | "She tensed, sliding her hand" | | 13 | "She needed to see where" | | 14 | "She fished a flashlight from" | | 15 | "Its beam split the darkness," | | 16 | "She descended into a cavernous" | | 17 | "She traced a finger along" | | 18 | "She felt the weight of" | | 19 | "She advanced, light bobbing with" |
| | ratio | 0.459 | |
| 47.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 148 | | matches | | 0 | "Water sluiced over the edge" | | 1 | "Her closely cropped salt-and-pepper hair" | | 2 | "She had followed the fugitive" | | 3 | "Quinn didn’t falter." | | 4 | "Her body moved with military" | | 5 | "Patrons huddled under its awning," | | 6 | "A waitress pressed her back" | | 7 | "Quinn spared her a glance," | | 8 | "The fugitive kept moving, weaving" | | 9 | "He darted through a rusted" | | 10 | "The alley beyond was narrower," | | 11 | "The fugitive’s boots thudded ahead." | | 12 | "She hesitated for a fraction" | | 13 | "She forced herself forward." | | 14 | "He leapt inside, his silhouette" | | 15 | "Quinn’s heart hammered." | | 16 | "This was no ordinary chase." | | 17 | "The detective’s memories lurched: DS" | | 18 | "His last transmission had static" | | 19 | "She had never known what" |
| | ratio | 0.824 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 148 | | matches | | 0 | "Now he rounded a corner" | | 1 | "If she entered legitimately, she’d" | | 2 | "If she followed, she might" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 1 | | matches | | 0 | "She hesitated for a fraction of a second when the gutter beneath her spurted something dark—an irregular sheen that could have been oil or blood." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, voice low with relief and apprehension" | | 1 | "she said, voice steadier than she felt," |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "She whispered (whisper)" | | 1 | "she barked (bark)" | | 2 | "she interrupted (interrupt)" |
| | dialogueSentences | 16 | | tagDensity | 0.438 | | leniency | 0.875 | | rawRatio | 0.429 | | effectiveRatio | 0.375 | |