| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva gestured vaguely [vaguely]" | | 1 | "Eva corrected gently [gently]" |
| | dialogueSentences | 42 | | tagDensity | 0.452 | | leniency | 0.905 | | rawRatio | 0.105 | | effectiveRatio | 0.095 | |
| 88.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1686 | | 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) | |
| 28.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1686 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "silence" | | 1 | "familiar" | | 2 | "resolved" | | 3 | "chaotic" | | 4 | "velvet" | | 5 | "shattered" | | 6 | "pulsed" | | 7 | "intricate" | | 8 | "implication" | | 9 | "etched" | | 10 | "traced" | | 11 | "crystal" | | 12 | "perfect" | | 13 | "variables" | | 14 | "scanning" | | 15 | "crystalline" | | 16 | "etching" | | 17 | "raced" | | 18 | "chill" | | 19 | "magnetic" |
| |
| 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 | 1 | | narrationSentences | 115 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1679 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1201 | | uniqueNames | 15 | | maxNameDensity | 1.83 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 22 | | Veil | 1 | | Market | 1 | | Scene | 1 | | Crime | 1 | | Officers | 1 | | Kowalski | 2 | | Eva | 16 | | Aurora | 1 | | Laine | 1 | | Porter | 3 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Aurora" | | 5 | "Laine" | | 6 | "Porter" | | 7 | "Morris" |
| | places | | | globalScore | 0.584 | | windowScore | 0.333 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 83 | | glossingSentenceCount | 5 | | matches | | 0 | "looked like bone—and muttered instruction" | | 1 | "feathers that seemed to shift colour, books bound in strange leather" | | 2 | "looked like powdered crystal, catching th" | | 3 | "something like hope in her eyes" | | 4 | "felt like a key, and for the first time" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 2.382 | | wordCount | 1679 | | matches | | 0 | "not a scorch, but a kind of shadow" | | 1 | "not damaged, but different" | | 2 | "not a piece of metal, but another fragment of the brass casing" | | 3 | "Not just between the items, but between this crime" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 136 | | matches | | 0 | "gemstones that pulsed" | | 1 | "saw that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 37.31 | | std | 23.49 | | cv | 0.63 | | sampleLengths | | 0 | 80 | | 1 | 56 | | 2 | 89 | | 3 | 31 | | 4 | 60 | | 5 | 12 | | 6 | 26 | | 7 | 27 | | 8 | 46 | | 9 | 3 | | 10 | 44 | | 11 | 3 | | 12 | 9 | | 13 | 69 | | 14 | 5 | | 15 | 25 | | 16 | 16 | | 17 | 27 | | 18 | 62 | | 19 | 7 | | 20 | 29 | | 21 | 15 | | 22 | 65 | | 23 | 58 | | 24 | 34 | | 25 | 64 | | 26 | 69 | | 27 | 13 | | 28 | 14 | | 29 | 64 | | 30 | 71 | | 31 | 29 | | 32 | 32 | | 33 | 51 | | 34 | 35 | | 35 | 30 | | 36 | 32 | | 37 | 26 | | 38 | 45 | | 39 | 4 | | 40 | 21 | | 41 | 14 | | 42 | 81 | | 43 | 37 | | 44 | 49 |
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| 80.85% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 115 | | matches | | 0 | "been closed" | | 1 | "been met" | | 2 | "were shoved" | | 3 | "been brought" | | 4 | "was smashed" | | 5 | "was blasted" | | 6 | "was found" | | 7 | "been laughed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 206 | | matches | | 0 | "were focusing" | | 1 | "was forming" | | 2 | "was handing" |
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| 37.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 136 | | ratio | 0.037 | | matches | | 0 | "She’d been met at a service door by a nervous uniformed constable who handed her a small, cold object—a token carved from what looked like bone—and muttered instructions about a market." | | 1 | "Yet the most valuable-looking items—gemstones that pulsed with inner light, intricate silver mechanisms—lay untouched on the floor around it." | | 2 | "The outline was crisp, detailed—she could even make out the suggestion of frayed cuffs on the jacket." | | 3 | "“Or modifying it. The compass wasn’t just on display; it was being worked on. The thief didn’t just take a finished product. They took a project.”" | | 4 | "What others had missed—what she had been missing—was the connection." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1214 | | adjectiveStacks | 2 | | stackExamples | | 0 | "distinctive blue-green verdigris." | | 1 | "perfect, human-shaped silhouette," |
| | adverbCount | 29 | | adverbRatio | 0.023887973640856673 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005766062602965404 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 12.35 | | std | 9.22 | | cv | 0.747 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 14 | | 3 | 18 | | 4 | 16 | | 5 | 11 | | 6 | 5 | | 7 | 31 | | 8 | 9 | | 9 | 12 | | 10 | 15 | | 11 | 34 | | 12 | 5 | | 13 | 15 | | 14 | 8 | | 15 | 18 | | 16 | 13 | | 17 | 16 | | 18 | 44 | | 19 | 6 | | 20 | 6 | | 21 | 10 | | 22 | 16 | | 23 | 24 | | 24 | 3 | | 25 | 4 | | 26 | 7 | | 27 | 9 | | 28 | 15 | | 29 | 11 | | 30 | 3 | | 31 | 10 | | 32 | 34 | | 33 | 3 | | 34 | 9 | | 35 | 5 | | 36 | 14 | | 37 | 8 | | 38 | 14 | | 39 | 19 | | 40 | 9 | | 41 | 5 | | 42 | 5 | | 43 | 5 | | 44 | 15 | | 45 | 16 | | 46 | 16 | | 47 | 11 | | 48 | 4 | | 49 | 14 |
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| 41.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3161764705882353 | | totalSentences | 136 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 111 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 111 | | matches | | 0 | "Her sharp jaw was set," | | 1 | "She’d been met at a" | | 2 | "She’d heard whispers, always dismissed" | | 3 | "She tucked a curl behind" | | 4 | "She took in Eva’s freckled" | | 5 | "She moved past Eva, her" | | 6 | "She bent down, careful not" | | 7 | "She pointed her torch at" | | 8 | "It was a crescent-shaped fragment" | | 9 | "She traced an arc in" | | 10 | "She’d spent three years rejecting" | | 11 | "She used a pencil from" | | 12 | "It looked like powdered crystal," | | 13 | "She pointed her pencil toward" | | 14 | "She walked to the wall," | | 15 | "It was older, more worn," | | 16 | "She reached out, then stopped" | | 17 | "She nodded at the shadow" | | 18 | "Her watch felt heavy on" | | 19 | "They’d been laughed out of" |
| | ratio | 0.243 | |
| 32.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 111 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn stood on" | | 2 | "The station had been closed" | | 3 | "Her sharp jaw was set," | | 4 | "The worn leather watch on" | | 5 | "The call had been bizarre," | | 6 | "A possible homicide, location classified." | | 7 | "She’d been met at a" | | 8 | "A flickering, sourceless greenish light" | | 9 | "Stalls made of repurposed piping" | | 10 | "This was the Veil Market." | | 11 | "She’d heard whispers, always dismissed" | | 12 | "Eva Kowalski looked up from" | | 13 | "She tucked a curl behind" | | 14 | "Eva said, her voice tight" | | 15 | "Quinn’s tone was flat, procedural" | | 16 | "She took in Eva’s freckled" | | 17 | "Eva gestured vaguely at the" | | 18 | "Quinn didn’t like it." | | 19 | "She moved past Eva, her" |
| | ratio | 0.856 | |
| 90.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 111 | | matches | | 0 | "Now, the token felt heavy" | | 1 | "Now, the evidence was literal" |
| | ratio | 0.018 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "She’d been met at a service door by a nervous uniformed constable who handed her a small, cold object—a token carved from what looked like bone—and muttered ins…" | | 1 | "Stalls made of repurposed piping and moth-eaten velvet were shoved aside, their wares spilled across the tracks: vials of shimmering liquid, bundles of feathers…" | | 2 | "It was a crescent-shaped fragment of a casing, etched with intricate, swirling lines that made her eyes water if she looked too long." | | 3 | "A perfect, human-shaped silhouette, as if someone had been standing there and the very brick had forgotten them." | | 4 | "Quinn turned from the wall, her eyes scanning the stall again, forcing herself to see." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 6 | | matches | | 0 | "Eva said, her voice tight" | | 1 | "Quinn said, her voice hardening" | | 2 | "Quinn insisted, but the words sounded hollow even to her" | | 3 | "Eva said, her voice low" | | 4 | "Quinn said, her voice betraying none of the chill spreading through her" | | 5 | "Quinn said, her voice quiet but precise as a blade" |
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| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 3 | | fancyTags | | 0 | "Eva corrected gently (correct)" | | 1 | "Quinn insisted (insist)" | | 2 | "Quinn murmured (murmur)" |
| | dialogueSentences | 42 | | tagDensity | 0.31 | | leniency | 0.619 | | rawRatio | 0.231 | | effectiveRatio | 0.143 | |