| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.185 | | leniency | 0.37 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1137 | | totalAiIsmAdverbs | 3 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | |
| | highlights | | 0 | "deliberately" | | 1 | "softly" | | 2 | "gently" |
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| 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) | |
| 20.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1137 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "macabre" | | 3 | "etched" | | 4 | "pulse" | | 5 | "otherworldly" | | 6 | "whisper" | | 7 | "imposing" | | 8 | "racing" | | 9 | "pulsed" | | 10 | "amidst" | | 11 | "resolve" | | 12 | "unravel" | | 13 | "beacon" | | 14 | "churning" | | 15 | "determined" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 59 | | matches | | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 35.68% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 831 | | uniqueNames | 10 | | maxNameDensity | 2.29 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 19 | | Tube | 2 | | Veil | 5 | | Market | 5 | | Kowalski | 1 | | Thomas | 2 | | Shade | 1 | | London | 1 | | Eva | 12 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Kowalski" | | 4 | "Thomas" | | 5 | "Eva" |
| | places | | | globalScore | 0.357 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "ting towards a seemingly solid wall" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1134 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 35.44 | | std | 19.07 | | cv | 0.538 | | sampleLengths | | 0 | 63 | | 1 | 60 | | 2 | 41 | | 3 | 28 | | 4 | 56 | | 5 | 19 | | 6 | 28 | | 7 | 28 | | 8 | 23 | | 9 | 67 | | 10 | 28 | | 11 | 36 | | 12 | 27 | | 13 | 27 | | 14 | 11 | | 15 | 25 | | 16 | 19 | | 17 | 55 | | 18 | 48 | | 19 | 38 | | 20 | 29 | | 21 | 22 | | 22 | 12 | | 23 | 12 | | 24 | 35 | | 25 | 31 | | 26 | 47 | | 27 | 22 | | 28 | 22 | | 29 | 13 | | 30 | 78 | | 31 | 84 |
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| 75.53% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 59 | | matches | | 0 | "was clutched" | | 1 | "was found" | | 2 | "was etched" | | 3 | "was determined" | | 4 | "was laid" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 126 | | matches | | |
| 37.04% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 81 | | ratio | 0.037 | | matches | | 0 | "The token was a pass to the Veil Market, a place Quinn knew only by reputation—a hidden bazaar for the occult and the supernatural." | | 1 | "A shadow compass—a tool for finding the supernatural." | | 2 | "But the real question remained—who was pulling the strings, and to what end?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 835 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.025149700598802394 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010778443113772455 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 14 | | std | 6.2 | | cv | 0.443 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 26 | | 3 | 16 | | 4 | 20 | | 5 | 24 | | 6 | 20 | | 7 | 14 | | 8 | 7 | | 9 | 23 | | 10 | 5 | | 11 | 17 | | 12 | 27 | | 13 | 12 | | 14 | 16 | | 15 | 3 | | 16 | 7 | | 17 | 21 | | 18 | 14 | | 19 | 3 | | 20 | 7 | | 21 | 4 | | 22 | 23 | | 23 | 7 | | 24 | 21 | | 25 | 16 | | 26 | 15 | | 27 | 8 | | 28 | 10 | | 29 | 12 | | 30 | 6 | | 31 | 12 | | 32 | 24 | | 33 | 6 | | 34 | 21 | | 35 | 12 | | 36 | 15 | | 37 | 5 | | 38 | 6 | | 39 | 11 | | 40 | 14 | | 41 | 8 | | 42 | 11 | | 43 | 13 | | 44 | 12 | | 45 | 12 | | 46 | 18 | | 47 | 11 | | 48 | 15 | | 49 | 22 |
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| 61.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.37037037037037035 | | totalSentences | 81 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 59 | | matches | | 0 | "She adjusted the collar of" | | 1 | "she greeted, pushing her round" | | 2 | "She gestured to the body's" | | 3 | "She paused, her gaze falling" | | 4 | "She picked it up, turning" | | 5 | "She placed her hand against" | | 6 | "Her fingers found a hairline" | | 7 | "She pressed, and with a" | | 8 | "They emerged into a cavernous" | | 9 | "She looked back at the" | | 10 | "She would follow the evidence," | | 11 | "She would not rest until" |
| | ratio | 0.203 | |
| 10.85% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 59 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The air was thick with" | | 2 | "She adjusted the collar of" | | 3 | "The body lay at an" | | 4 | "A bone token, etched with" | | 5 | "The token was a pass" | | 6 | "Eva Kowalski, her red hair" | | 7 | "she greeted, pushing her round" | | 8 | "Quinn's worn leather watch ticked" | | 9 | "Eva tucked a curl behind" | | 10 | "She gestured to the body's" | | 11 | "Quinn's gaze lingered on the" | | 12 | "Eva replied, her voice a" | | 13 | "Quinn stood, her height giving" | | 14 | "Something was wrong." | | 15 | "The evidence was too neat," | | 16 | "Eva said, her green eyes" | | 17 | "Quinn walked the perimeter, her" | | 18 | "The Veil Market moved locations" | | 19 | "She paused, her gaze falling" |
| | ratio | 0.898 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 3 | | matches | | 0 | "Eva replied, her voice a whisper" | | 1 | "Eva said, her green eyes reflecting the tragedy of the scene" | | 2 | "Eva whispered, her voice echoing in the vast space" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.333 | | effectiveRatio | 0.074 | |