| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.105 | | leniency | 0.211 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1179 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 91.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1179 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 0 | | narrationSentences | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 984 | | uniqueNames | 14 | | maxNameDensity | 0.91 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Ahmed" | | discoveredNames | | Keurig | 1 | | Xerox | 1 | | Ahmed | 8 | | Gerald | 1 | | Pritchard | 5 | | Callahan | 2 | | Morris | 2 | | God | 1 | | Quinn | 9 | | Bermondsey | 2 | | Kowalski | 2 | | British | 1 | | Museum | 1 | | Aurora | 1 |
| | persons | | 0 | "Ahmed" | | 1 | "Gerald" | | 2 | "Pritchard" | | 3 | "Callahan" | | 4 | "Morris" | | 5 | "God" | | 6 | "Quinn" | | 7 | "Kowalski" | | 8 | "Museum" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | 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 | 1179 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 130 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 18.42 | | std | 20.54 | | cv | 1.115 | | sampleLengths | | 0 | 7 | | 1 | 77 | | 2 | 2 | | 3 | 32 | | 4 | 4 | | 5 | 18 | | 6 | 3 | | 7 | 19 | | 8 | 51 | | 9 | 50 | | 10 | 3 | | 11 | 14 | | 12 | 56 | | 13 | 7 | | 14 | 25 | | 15 | 5 | | 16 | 1 | | 17 | 5 | | 18 | 1 | | 19 | 18 | | 20 | 26 | | 21 | 3 | | 22 | 12 | | 23 | 57 | | 24 | 6 | | 25 | 1 | | 26 | 1 | | 27 | 14 | | 28 | 2 | | 29 | 2 | | 30 | 55 | | 31 | 7 | | 32 | 49 | | 33 | 36 | | 34 | 15 | | 35 | 1 | | 36 | 8 | | 37 | 58 | | 38 | 2 | | 39 | 10 | | 40 | 10 | | 41 | 2 | | 42 | 58 | | 43 | 1 | | 44 | 1 | | 45 | 6 | | 46 | 22 | | 47 | 55 | | 48 | 1 | | 49 | 23 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 96 | | matches | | 0 | "been pressed" | | 1 | "been hauled" | | 2 | "been closed" | | 3 | "was, weighted" | | 4 | "been ringed" | | 5 | "was crouched" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 163 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 130 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.024242424242424242 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0030303030303030303 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 9.07 | | std | 8.91 | | cv | 0.983 | | sampleLengths | | 0 | 7 | | 1 | 26 | | 2 | 2 | | 3 | 2 | | 4 | 13 | | 5 | 34 | | 6 | 2 | | 7 | 15 | | 8 | 17 | | 9 | 4 | | 10 | 18 | | 11 | 3 | | 12 | 19 | | 13 | 3 | | 14 | 29 | | 15 | 19 | | 16 | 1 | | 17 | 16 | | 18 | 3 | | 19 | 2 | | 20 | 15 | | 21 | 13 | | 22 | 3 | | 23 | 8 | | 24 | 6 | | 25 | 5 | | 26 | 7 | | 27 | 20 | | 28 | 6 | | 29 | 18 | | 30 | 7 | | 31 | 4 | | 32 | 21 | | 33 | 5 | | 34 | 1 | | 35 | 5 | | 36 | 1 | | 37 | 15 | | 38 | 3 | | 39 | 4 | | 40 | 22 | | 41 | 3 | | 42 | 12 | | 43 | 4 | | 44 | 11 | | 45 | 18 | | 46 | 3 | | 47 | 21 | | 48 | 6 | | 49 | 1 |
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| 71.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4846153846153846 | | totalSentences | 130 | | uniqueOpeners | 63 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | | ratio | 0.013 | |
| 69.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 77 | | matches | | 0 | "He was young enough that" | | 1 | "She made a habit of" | | 2 | "He had walked to the" | | 3 | "She crouched by the machine." | | 4 | "His knees rested on the" | | 5 | "His arms hung loose at" | | 6 | "She pulled a pen torch" | | 7 | "She could see the matted" | | 8 | "She eased the lid back" | | 9 | "Her left knee made a" | | 10 | "She went to the desk." | | 11 | "His phone sat screen-down on" | | 12 | "She lifted the phone." | | 13 | "She set it back at" | | 14 | "She couldn't read it through" | | 15 | "She bagged the top sheet" | | 16 | "She went back." | | 17 | "He angled the lid up" | | 18 | "She thought about that." | | 19 | "She heard the rustle, the" |
| | ratio | 0.377 | |
| 44.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 77 | | matches | | 0 | "The body lay folded into" | | 1 | "Quinn stopped at the threshold" | | 2 | "A Keurig on the credenza" | | 3 | "Everything in the room screamed" | | 4 | "DC Ahmed stood by the" | | 5 | "He was young enough that" | | 6 | "Quinn moved in." | | 7 | "She made a habit of" | | 8 | "The victim, one Gerald Pritchard," | | 9 | "He had walked to the" | | 10 | "She crouched by the machine." | | 11 | "Pritchard's head and shoulders were" | | 12 | "The lid had been closed" | | 13 | "His knees rested on the" | | 14 | "His arms hung loose at" | | 15 | "The glass of the flatbed" | | 16 | "Quinn tilted her head." | | 17 | "She pulled a pen torch" | | 18 | "She could see the matted" | | 19 | "Ahmed shrugged one shoulder." |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "Low pile, industrial grey, vacuumed in overlapping arcs by someone who took pride in small things." | | 1 | "The lid had been closed down onto the back of his neck like a guillotine that had gone off half-cocked." | | 2 | "Eva Kowalski, who worked in the British Museum's restricted archives and who was, according to a surveillance file Quinn wasn't technically supposed to have, cl…" | | 3 | "He was young, but he wasn't stupid, and something in her voice had gone quiet in the way that meant she'd already moved three moves ahead of him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.026 | | leniency | 0.053 | | rawRatio | 0 | | effectiveRatio | 0 | |