| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1424 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "completely" | | 2 | "carefully" |
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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) | |
| 36.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1424 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "weight" | | 1 | "fractured" | | 2 | "measured" | | 3 | "calculated" | | 4 | "loomed" | | 5 | "familiar" | | 6 | "looming" | | 7 | "scanning" | | 8 | "footsteps" | | 9 | "echoed" | | 10 | "methodical" | | 11 | "chill" | | 12 | "constructed" | | 13 | "velvet" | | 14 | "unravel" | | 15 | "comforting" |
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| 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 | 121 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1412 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.18% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1412 | | uniqueNames | 18 | | maxNameDensity | 1.42 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Harlow | 20 | | Quinn | 1 | | Metropolitan | 1 | | Police | 1 | | Herrera | 1 | | Seville | 1 | | Morris | 5 | | Tomás | 13 | | Saint | 2 | | Christopher | 2 | | South | 1 | | Kentish | 1 | | Town | 1 | | Tube | 1 | | London | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Police" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Tomás" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Metropolitan" | | 1 | "Seville" | | 2 | "South" | | 3 | "Kentish" | | 4 | "Town" | | 5 | "London" |
| | globalScore | 0.792 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 101 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.708 | | wordCount | 1412 | | matches | | 0 | "not a single conversation, but the collective hum of a crowd" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 121 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 42.79 | | std | 24.85 | | cv | 0.581 | | sampleLengths | | 0 | 72 | | 1 | 14 | | 2 | 112 | | 3 | 38 | | 4 | 20 | | 5 | 48 | | 6 | 6 | | 7 | 49 | | 8 | 43 | | 9 | 59 | | 10 | 64 | | 11 | 16 | | 12 | 48 | | 13 | 58 | | 14 | 52 | | 15 | 4 | | 16 | 78 | | 17 | 57 | | 18 | 18 | | 19 | 59 | | 20 | 28 | | 21 | 29 | | 22 | 84 | | 23 | 17 | | 24 | 56 | | 25 | 2 | | 26 | 44 | | 27 | 67 | | 28 | 28 | | 29 | 34 | | 30 | 57 | | 31 | 31 | | 32 | 20 |
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| 96.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 121 | | matches | | 0 | "was terrified" | | 1 | "been closed" | | 2 | "been pried" |
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| 84.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 230 | | matches | | 0 | "was closing" | | 1 | "was teeming" | | 2 | "was slipping" | | 3 | "was taking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 121 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1424 | | adjectiveStacks | 3 | | stackExamples | | 0 | "dense, rain-soaked weeds" | | 1 | "dry, pitch-black interior." | | 2 | "massive, iron-wrought gate" |
| | adverbCount | 24 | | adverbRatio | 0.016853932584269662 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.011235955056179775 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 11.67 | | std | 6.25 | | cv | 0.536 | | sampleLengths | | 0 | 17 | | 1 | 21 | | 2 | 17 | | 3 | 4 | | 4 | 13 | | 5 | 10 | | 6 | 4 | | 7 | 14 | | 8 | 2 | | 9 | 10 | | 10 | 6 | | 11 | 3 | | 12 | 3 | | 13 | 28 | | 14 | 3 | | 15 | 17 | | 16 | 26 | | 17 | 6 | | 18 | 22 | | 19 | 10 | | 20 | 6 | | 21 | 6 | | 22 | 8 | | 23 | 4 | | 24 | 11 | | 25 | 11 | | 26 | 15 | | 27 | 7 | | 28 | 4 | | 29 | 2 | | 30 | 5 | | 31 | 13 | | 32 | 20 | | 33 | 11 | | 34 | 11 | | 35 | 6 | | 36 | 11 | | 37 | 15 | | 38 | 7 | | 39 | 9 | | 40 | 10 | | 41 | 12 | | 42 | 7 | | 43 | 14 | | 44 | 15 | | 45 | 31 | | 46 | 18 | | 47 | 16 | | 48 | 19 | | 49 | 8 |
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| 39.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2975206611570248 | | totalSentences | 121 | | uniqueOpeners | 36 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 116 | | matches | | 0 | "Ever since DS Morris bled" | | 1 | "Instead, he withdrew a pale," |
| | ratio | 0.017 | |
| 61.38% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 116 | | matches | | 0 | "She did not shiver." | | 1 | "She checked the worn leather" | | 2 | "He shifted his weight from" | | 3 | "He was the medic for" | | 4 | "He clutched a heavy canvas" | | 5 | "She wanted him to see" | | 6 | "She wanted to push him" | | 7 | "His warm brown eyes locked" | | 8 | "She took one step off" | | 9 | "Her booted feet slapped the" | | 10 | "She crossed the street in" | | 11 | "She ignored him, her focus" | | 12 | "Her lungs expanded, pulling in" | | 13 | "She maintained a steady, calculated" | | 14 | "His boots slipped on the" | | 15 | "He crashed shoulder-first into a" | | 16 | "He was terrified, but he" | | 17 | "She was closing in." | | 18 | "He veered sharply to the" | | 19 | "It was the old South" |
| | ratio | 0.397 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 116 | | matches | | 0 | "Detective Harlow Quinn stood in" | | 1 | "Water dripped from her closely" | | 2 | "She did not shiver." | | 3 | "She checked the worn leather" | | 4 | "He shifted his weight from" | | 5 | "Harlow knew his file by" | | 6 | "Harlow knew better." | | 7 | "He was the medic for" | | 8 | "Tomás stepped out from the" | | 9 | "A passing car’s headlights swept" | | 10 | "He clutched a heavy canvas" | | 11 | "Harlow stepped out of the" | | 12 | "She wanted him to see" | | 13 | "She wanted to push him" | | 14 | "Tomás turned his head." | | 15 | "His warm brown eyes locked" | | 16 | "Harlow squared her shoulders, her" | | 17 | "She took one step off" | | 18 | "Harlow broke into a sprint." | | 19 | "Her booted feet slapped the" |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 84.15% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 6 | | matches | | 0 | "Stripped of his license for administering what the medical board had called localized hallucinogens and unknown herbal compounds to patients who were nowhere ne…" | | 1 | "She crossed the street in long, measured strides, dodging a late-night delivery cyclist who shouted a curse in her wake." | | 2 | "She ignored him, her focus tunneling entirely on the fleeing ex-paramedic." | | 3 | "It was the old South Kentish Town station, an abandoned Tube stop that had been closed to the public since before she was born." | | 4 | "The rational part of her brain, the seasoned detective who played by the book and gathered actionable evidence, demanded she call for backup." | | 5 | "He stood before a massive, iron-wrought gate that blocked the deeper tunnels." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |