| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said finally [finally]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0.222 | | effectiveRatio | 0.114 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1161 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 91.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1161 | | 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 | 65 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 9 | | totalWords | 1161 | | ratio | 0.008 | | matches | | 0 | "Look first. Theories are for people who've stopped looking." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.92% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 942 | | uniqueNames | 15 | | maxNameDensity | 1.06 | | worstName | "Patel" | | maxWindowNameDensity | 2 | | worstWindowName | "Patel" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Northern | 1 | | Line | 1 | | Camden | 2 | | Priya | 1 | | Patel | 10 | | Morris | 3 | | Yorkshire | 1 | | London | 1 | | Blitz | 1 | | Deptford | 2 | | High | 1 | | Street | 1 | | June | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Line" | | 3 | "Priya" | | 4 | "Patel" | | 5 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "Yorkshire" | | 2 | "London" | | 3 | "Deptford" | | 4 | "High" | | 5 | "Street" | | 6 | "June" |
| | globalScore | 0.969 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1161 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 27.64 | | std | 26.03 | | cv | 0.942 | | sampleLengths | | 0 | 15 | | 1 | 61 | | 2 | 39 | | 3 | 3 | | 4 | 45 | | 5 | 17 | | 6 | 5 | | 7 | 36 | | 8 | 8 | | 9 | 27 | | 10 | 51 | | 11 | 3 | | 12 | 17 | | 13 | 4 | | 14 | 19 | | 15 | 67 | | 16 | 80 | | 17 | 31 | | 18 | 11 | | 19 | 3 | | 20 | 4 | | 21 | 69 | | 22 | 17 | | 23 | 53 | | 24 | 8 | | 25 | 5 | | 26 | 40 | | 27 | 3 | | 28 | 2 | | 29 | 10 | | 30 | 68 | | 31 | 11 | | 32 | 11 | | 33 | 48 | | 34 | 13 | | 35 | 55 | | 36 | 4 | | 37 | 72 | | 38 | 15 | | 39 | 1 | | 40 | 11 | | 41 | 99 |
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| 99.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 65 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 149 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 91 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 947 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.03695881731784583 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.014783526927138331 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 12.76 | | std | 11.03 | | cv | 0.864 | | sampleLengths | | 0 | 15 | | 1 | 24 | | 2 | 8 | | 3 | 29 | | 4 | 24 | | 5 | 15 | | 6 | 3 | | 7 | 14 | | 8 | 8 | | 9 | 23 | | 10 | 6 | | 11 | 11 | | 12 | 5 | | 13 | 5 | | 14 | 10 | | 15 | 21 | | 16 | 6 | | 17 | 2 | | 18 | 27 | | 19 | 7 | | 20 | 16 | | 21 | 28 | | 22 | 3 | | 23 | 12 | | 24 | 5 | | 25 | 4 | | 26 | 19 | | 27 | 3 | | 28 | 32 | | 29 | 2 | | 30 | 30 | | 31 | 26 | | 32 | 7 | | 33 | 3 | | 34 | 7 | | 35 | 37 | | 36 | 6 | | 37 | 4 | | 38 | 21 | | 39 | 3 | | 40 | 8 | | 41 | 3 | | 42 | 4 | | 43 | 5 | | 44 | 31 | | 45 | 6 | | 46 | 27 | | 47 | 8 | | 48 | 9 | | 49 | 20 |
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| 97.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.6263736263736264 | | totalSentences | 91 | | uniqueOpeners | 57 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 57 | | matches | | 0 | "Somewhere above, the ordinary world" | | 1 | "Just that one bench, and" | | 2 | "Somewhere above her, the city" |
| | ratio | 0.053 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 57 | | matches | | 0 | "She lifted the man's hand." | | 1 | "She turned the wrist toward" | | 2 | "She let her eyes travel" | | 3 | "She reached out and touched" | | 4 | "She thought of another wall," | | 5 | "She'd called it nothing at" | | 6 | "she said finally" | | 7 | "It curved, deliberate, almost calligraphic," | | 8 | "she said slowly" | | 9 | "They bled generously, indiscriminately, in" | | 10 | "She'd smelled it once before." | | 11 | "She'd told herself for three" | | 12 | "She reached into her coat" | | 13 | "Her eye caught on the" |
| | ratio | 0.246 | |
| 65.26% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 57 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The platform hadn't seen a" | | 2 | "Water pooled black in the" | | 3 | "DC Priya Patel crouched beside" | | 4 | "Quinn crouched opposite her, careful" | | 5 | "The victim's wrist lay palm-up," | | 6 | "A pale band of untanned" | | 7 | "She lifted the man's hand." | | 8 | "A gold signet, old-fashioned, sat" | | 9 | "Patel's mouth pulled to one" | | 10 | "She turned the wrist toward" | | 11 | "The skin beneath the missing" | | 12 | "Patel flipped her notebook shut" | | 13 | "Quinn said nothing." | | 14 | "She let her eyes travel" | | 15 | "Theories are for people who've" | | 16 | "Frost rimed the edge of" | | 17 | "Nothing else in the tunnel" | | 18 | "She reached out and touched" | | 19 | "Quinn didn't answer straight away." |
| | ratio | 0.789 | |
| 87.72% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 57 | | matches | | 0 | "As though the watch had" |
| | ratio | 0.018 | |
| 8.93% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 6 | | matches | | 0 | "Somewhere above, the ordinary world rattled on, buses and market stalls and the general roar of a city that had no idea what sat forty feet beneath its feet." | | 1 | "As though the watch had simply ceased to exist and left an outline behind, the way a photograph left on a windowsill bleaches a rectangle into old wallpaper." | | 2 | "Theories are for people who've stopped looking.* She could still hear him say it, three years on, in that flat Yorkshire voice he never lost despite two decades…" | | 3 | "Frost rimed the edge of the nearest bench, a fine lace of it clinging to iron scrollwork that hadn't seen a heating system since the Blitz." | | 4 | "Just that one bench, and the stretch of wall behind the body, where damp brickwork glistened with something that caught the torchlight wrong, a greenish sheen, …" | | 5 | "Above the tunnel, faint and rhythmic, came the thrum of a lorry idling on Camden High Street, oblivious, ordinary, a world away from a bench wearing frost in Ju…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |