| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1160 | | 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) | |
| 39.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1160 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "could feel" | | 2 | "pulse" | | 3 | "gloom" | | 4 | "standard" | | 5 | "reminder" | | 6 | "scanning" | | 7 | "stomach" | | 8 | "familiar" | | 9 | "comforting" | | 10 | "depths" | | 11 | "silence" | | 12 | "vibrated" | | 13 | "scanned" |
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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 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 102 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1159 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1154 | | uniqueNames | 10 | | maxNameDensity | 0.95 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Camden | 2 | | Morris | 3 | | London | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Soho | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | 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 | 1159 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 96.89% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 50.39 | | std | 24.65 | | cv | 0.489 | | sampleLengths | | 0 | 79 | | 1 | 99 | | 2 | 70 | | 3 | 11 | | 4 | 10 | | 5 | 78 | | 6 | 77 | | 7 | 53 | | 8 | 67 | | 9 | 3 | | 10 | 48 | | 11 | 38 | | 12 | 51 | | 13 | 46 | | 14 | 44 | | 15 | 38 | | 16 | 66 | | 17 | 68 | | 18 | 57 | | 19 | 44 | | 20 | 20 | | 21 | 71 | | 22 | 21 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 102 | | matches | | 0 | "were tiled" | | 1 | "were gone" | | 2 | "were draped" | | 3 | "was outnumbered" |
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| 26.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 192 | | matches | | 0 | "was running" | | 1 | "was running" | | 2 | "was trespassing" | | 3 | "were fueling" | | 4 | "was willing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 103 | | ratio | 0.01 | | matches | | 0 | "Then she saw it—a ventilation grate near the ground, rusted and loose." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1157 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, pressing against her" |
| | adverbCount | 25 | | adverbRatio | 0.021607605877268798 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.00950734658599827 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 11.25 | | std | 6.57 | | cv | 0.584 | | sampleLengths | | 0 | 24 | | 1 | 34 | | 2 | 21 | | 3 | 12 | | 4 | 16 | | 5 | 8 | | 6 | 9 | | 7 | 25 | | 8 | 29 | | 9 | 12 | | 10 | 3 | | 11 | 26 | | 12 | 13 | | 13 | 16 | | 14 | 10 | | 15 | 1 | | 16 | 4 | | 17 | 6 | | 18 | 15 | | 19 | 4 | | 20 | 13 | | 21 | 6 | | 22 | 11 | | 23 | 12 | | 24 | 17 | | 25 | 11 | | 26 | 8 | | 27 | 4 | | 28 | 11 | | 29 | 4 | | 30 | 9 | | 31 | 13 | | 32 | 17 | | 33 | 3 | | 34 | 14 | | 35 | 20 | | 36 | 16 | | 37 | 11 | | 38 | 10 | | 39 | 6 | | 40 | 26 | | 41 | 14 | | 42 | 3 | | 43 | 19 | | 44 | 17 | | 45 | 5 | | 46 | 7 | | 47 | 9 | | 48 | 17 | | 49 | 7 |
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| 30.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.27184466019417475 | | totalSentences | 103 | | uniqueOpeners | 28 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 102 | | matches | | 0 | "Instead, they slammed the door" | | 1 | "Then she saw it—a ventilation" | | 2 | "Too small for a grown" | | 3 | "Then she looked down the" |
| | ratio | 0.039 | |
| 31.76% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 102 | | matches | | 0 | "She didn’t check her watch," | | 1 | "They knew where the cameras" | | 2 | "They knew where the pavement" | | 3 | "She was forty-one, and the" | | 4 | "She hit the brick wall" | | 5 | "Her boots skidded on wet" | | 6 | "She saw the figure pause" | | 7 | "It was locked, reinforced." | | 8 | "She cursed under her breath," | | 9 | "She stepped back, scanning the" | | 10 | "She dropped to her knees," | | 11 | "Her fingers found the rusted" | | 12 | "She shoved the grate aside" | | 13 | "It wasn’t a basement." | | 14 | "It was a tunnel, descending" | | 15 | "It smelled of ozone, copper," | | 16 | "She had chased leads like" | | 17 | "She had sworn then that" | | 18 | "She holstered her radio, knowing" | | 19 | "She checked her sidearm, the" |
| | ratio | 0.471 | |
| 33.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 102 | | matches | | 0 | "Rain lashed against the back" | | 1 | "She didn’t check her watch," | | 2 | "The suspect moved with a" | | 3 | "They knew where the cameras" | | 4 | "They knew where the pavement" | | 5 | "Quinn adjusted her grip on" | | 6 | "She was forty-one, and the" | | 7 | "The suspect darted toward a" | | 8 | "Quinn didn’t hesitate." | | 9 | "She hit the brick wall" | | 10 | "Her boots skidded on wet" | | 11 | "She saw the figure pause" | | 12 | "Quinn’s voice cracked through the" | | 13 | "The figure didn’t freeze." | | 14 | "Quinn hit it a second" | | 15 | "It was locked, reinforced." | | 16 | "She cursed under her breath," | | 17 | "She stepped back, scanning the" | | 18 | "She dropped to her knees," | | 19 | "Her fingers found the rusted" |
| | ratio | 0.853 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 102 | | matches | | 0 | "If she went down, she" | | 1 | "If she turned back, the" | | 2 | "If she went in, she" |
| | ratio | 0.029 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 2 | | matches | | 0 | "The suspect moved with a fluidity that didn’t match the clumsy panic of a standard criminal." | | 1 | "The suspect knew about the alchemical substances that were fueling the violence in Soho." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice cutting through the hum" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |