| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 85.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1403 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "slightly" | | 2 | "loosely" |
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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) | |
| 53.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1403 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "chill" | | 2 | "scanned" | | 3 | "perfect" | | 4 | "weight" | | 5 | "charm" | | 6 | "footsteps" | | 7 | "echoed" | | 8 | "vibrated" | | 9 | "flickered" | | 10 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 140 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 140 | | filterMatches | | | hedgeMatches | | 0 | "try to" | | 1 | "tried to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 140 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1403 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1403 | | uniqueNames | 13 | | maxNameDensity | 0.86 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Tube | 2 | | Camden | 1 | | Mills | 9 | | Morris | 2 | | Kowalski | 1 | | Eva | 6 | | Veil | 3 | | Compass | 1 | | Market | 2 | | London | 1 | | Detective | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Mills" | | 3 | "Morris" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Compass" | | 7 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 112 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like random tags formed repeating" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1403 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 140 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 32.63 | | std | 24.06 | | cv | 0.737 | | sampleLengths | | 0 | 84 | | 1 | 41 | | 2 | 23 | | 3 | 31 | | 4 | 50 | | 5 | 10 | | 6 | 15 | | 7 | 67 | | 8 | 49 | | 9 | 5 | | 10 | 5 | | 11 | 6 | | 12 | 28 | | 13 | 66 | | 14 | 31 | | 15 | 2 | | 16 | 10 | | 17 | 35 | | 18 | 60 | | 19 | 29 | | 20 | 29 | | 21 | 10 | | 22 | 41 | | 23 | 75 | | 24 | 70 | | 25 | 11 | | 26 | 7 | | 27 | 9 | | 28 | 11 | | 29 | 25 | | 30 | 17 | | 31 | 51 | | 32 | 8 | | 33 | 24 | | 34 | 71 | | 35 | 14 | | 36 | 23 | | 37 | 57 | | 38 | 68 | | 39 | 15 | | 40 | 16 | | 41 | 80 | | 42 | 24 |
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| 97.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 140 | | matches | | 0 | "was arranged" | | 1 | "been posed" | | 2 | "was silenced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 249 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 140 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1404 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.02492877492877493 | | lyAdverbCount | 19 | | lyAdverbRatio | 0.013532763532763533 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 140 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 140 | | mean | 10.02 | | std | 5.6 | | cv | 0.559 | | sampleLengths | | 0 | 26 | | 1 | 19 | | 2 | 19 | | 3 | 20 | | 4 | 13 | | 5 | 13 | | 6 | 6 | | 7 | 9 | | 8 | 10 | | 9 | 13 | | 10 | 1 | | 11 | 3 | | 12 | 6 | | 13 | 15 | | 14 | 6 | | 15 | 7 | | 16 | 15 | | 17 | 11 | | 18 | 5 | | 19 | 6 | | 20 | 6 | | 21 | 10 | | 22 | 3 | | 23 | 3 | | 24 | 9 | | 25 | 5 | | 26 | 15 | | 27 | 12 | | 28 | 10 | | 29 | 12 | | 30 | 13 | | 31 | 11 | | 32 | 4 | | 33 | 13 | | 34 | 5 | | 35 | 16 | | 36 | 5 | | 37 | 5 | | 38 | 6 | | 39 | 7 | | 40 | 8 | | 41 | 5 | | 42 | 8 | | 43 | 19 | | 44 | 6 | | 45 | 12 | | 46 | 7 | | 47 | 22 | | 48 | 12 | | 49 | 8 |
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| 57.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.37142857142857144 | | totalSentences | 140 | | uniqueOpeners | 52 | |
| 24.69% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 135 | | matches | | 0 | "Ever seen anything like this?" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 135 | | matches | | 0 | "She checked the worn leather" | | 1 | "His shoulders carried the slump" | | 2 | "She crouched beside the corpse" | | 3 | "His expensive coat lay undisturbed" | | 4 | "She lifted one cold hand." | | 5 | "She stood and walked a" | | 6 | "Her sharp jaw flexed as" | | 7 | "Her gaze caught on an" | | 8 | "She worked it free." | | 9 | "She held the instrument out." | | 10 | "We see this pattern weekly" | | 11 | "She ran her fingertips across" | | 12 | "She pulled a small bone" | | 13 | "They fought, he lost, they" | | 14 | "She pressed her palm flat" | | 15 | "She ignored him and studied" | | 16 | "Her mind turned over the" | | 17 | "She tucked a curl behind" | | 18 | "Your message mentioned unusual symbols" | | 19 | "You brought in a civilian" |
| | ratio | 0.23 | |
| 45.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 135 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "The air carried the bite" | | 2 | "She checked the worn leather" | | 3 | "A body rested near the" | | 4 | "Blood pooled beneath the skull" | | 5 | "Detective Mills looked up from" | | 6 | "His shoulders carried the slump" | | 7 | "This reads as a straightforward" | | 8 | "Victim comes down for who" | | 9 | "These tunnels draw trouble after" | | 10 | "She crouched beside the corpse" | | 11 | "The man's shoes gleamed with" | | 12 | "His expensive coat lay undisturbed" | | 13 | "She lifted one cold hand." | | 14 | "The fingernails remained clean and" | | 15 | "Mills shifted his weight from" | | 16 | "Thieves surprised him." | | 17 | "Quinn turned the head gently." | | 18 | "The temple wound looked precise" | | 19 | "Gravity had not pulled it" |
| | ratio | 0.83 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 135 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn descended the concrete stairs into the abandoned Tube station beneath Camden, her steps measured against the damp chill that clung to the…" | | 1 | "Near the wall the dust showed one clear print, smaller than hers or Mills's, stopping abruptly at the bricks as if the person had stepped through solid stone." | | 2 | "The station sat directly above rumored entrances to a moving black market, a place that shifted location with each full moon and traded in items that defied ord…" | | 3 | "The staged blood, the untouched watch, the deliberate print that ended at nothing." | | 4 | "The same unnatural hum that claimed Morris now vibrated beneath her fingers." |
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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 | |