| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 73.61% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 758 | | totalAiIsmAdverbs | 4 | | 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) | |
| 20.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 758 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "chill" | | 2 | "sinister" | | 3 | "stomach" | | 4 | "racing" | | 5 | "dancing" | | 6 | "silence" | | 7 | "reverberated" | | 8 | "could feel" | | 9 | "warmth" | | 10 | "weight" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "fists clenched" | | 2 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 763 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 753 | | uniqueNames | 17 | | maxNameDensity | 0.93 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Detective | 2 | | Harlow | 7 | | Quinn | 1 | | Tommy | 2 | | Silas | 3 | | Lights | 1 | | Quit | 1 | | Come | 1 | | You | 1 | | Baptiste | 1 | | Charlotte | 1 | | Percival | 1 | | Baphomet-ridden | 1 | | Silly | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tommy" | | 3 | "Silas" | | 4 | "Lights" | | 5 | "You" | | 6 | "Charlotte" | | 7 | "Percival" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 763 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 42.39 | | std | 23.54 | | cv | 0.555 | | sampleLengths | | 0 | 50 | | 1 | 75 | | 2 | 8 | | 3 | 58 | | 4 | 58 | | 5 | 64 | | 6 | 5 | | 7 | 53 | | 8 | 45 | | 9 | 31 | | 10 | 10 | | 11 | 23 | | 12 | 54 | | 13 | 22 | | 14 | 57 | | 15 | 39 | | 16 | 91 | | 17 | 20 |
| |
| 95.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 71 | | matches | | 0 | "was entangled" | | 1 | "been taken" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 138 | | matches | | 0 | "was leaving" | | 1 | "was trying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 71 | | ratio | 0.056 | | matches | | 0 | "Harlow weighed her options - to stay and intimidate Silas into singing, or to lose precious time hunting a faster lead." | | 1 | "Heat grew in her chest - panic." | | 2 | "A skeleton key - well, a garishly green bone key judging by the shadows it cast - hung from a rotten black rope." | | 3 | "Three coppery fingers poke through the smoke and grab Harlow's hand - her own hand, from behind." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 755 | | adjectiveStacks | 1 | | stackExamples | | 0 | "acrid rain-soaked smell," |
| | adverbCount | 23 | | adverbRatio | 0.030463576158940398 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.015894039735099338 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 10.72 | | std | 5.73 | | cv | 0.535 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 15 | | 3 | 12 | | 4 | 16 | | 5 | 15 | | 6 | 21 | | 7 | 11 | | 8 | 8 | | 9 | 19 | | 10 | 24 | | 11 | 15 | | 12 | 12 | | 13 | 14 | | 14 | 19 | | 15 | 11 | | 16 | 2 | | 17 | 15 | | 18 | 17 | | 19 | 18 | | 20 | 7 | | 21 | 7 | | 22 | 5 | | 23 | 12 | | 24 | 10 | | 25 | 14 | | 26 | 7 | | 27 | 1 | | 28 | 3 | | 29 | 6 | | 30 | 16 | | 31 | 23 | | 32 | 6 | | 33 | 11 | | 34 | 13 | | 35 | 6 | | 36 | 1 | | 37 | 10 | | 38 | 2 | | 39 | 12 | | 40 | 9 | | 41 | 11 | | 42 | 16 | | 43 | 20 | | 44 | 7 | | 45 | 13 | | 46 | 6 | | 47 | 3 | | 48 | 17 | | 49 | 15 |
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| 87.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5352112676056338 | | totalSentences | 71 | | uniqueOpeners | 38 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 64 | | matches | | 0 | "Suddenly, the cobblestones beneath her" | | 1 | "Suddenly, she feels the warmth" |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 64 | | matches | | 0 | "Her blood ran cold as" | | 1 | "Her stomach flipped, and the" | | 2 | "She knew which way she" | | 3 | "She passed by iron gates" | | 4 | "Her sharp jawline squared as" | | 5 | "She shook her head against" | | 6 | "She fists clenched and unclenched," | | 7 | "She'd read about these, hadn't" | | 8 | "She'd gone from a chase" | | 9 | "She'd seen it all, yet" | | 10 | "She sucks in her breath" | | 11 | "She grips her seat so" | | 12 | "Her fingertips tousled down the" | | 13 | "She unzips it." | | 14 | "She slides the folder back" | | 15 | "Her eyes go back to" | | 16 | "she asks the Baphomet-ridden empire" |
| | ratio | 0.266 | |
| 77.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 64 | | matches | | 0 | "The chase began at The" | | 1 | "Silas had assured her Tommy" | | 2 | "Her blood ran cold as" | | 3 | "A chill mingled with the" | | 4 | "The minutes ticked by on" | | 5 | "Harlow weighed her options -" | | 6 | "Her stomach flipped, and the" | | 7 | "She knew which way she" | | 8 | "The week's deluge had morphed" | | 9 | "Harlow didn't know if Tommy" | | 10 | "The chase took her down" | | 11 | "She passed by iron gates" | | 12 | "Her sharp jawline squared as" | | 13 | "Heart racing, Harlow sprinted into" | | 14 | "Water dripped relentlessly from her" | | 15 | "She shook her head against" | | 16 | "Harlow had seen a lot," | | 17 | "The mottled tunnel stretched endlessly," | | 18 | "She fists clenched and unclenched," | | 19 | "Heat grew in her chest" |
| | ratio | 0.766 | |
| 78.13% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 64 | | matches | | 0 | "Though worn, it was still" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "The week's deluge had morphed the quiet streets into evil reflections, as if the devil himself walked among them." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |