| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "the licensing board had quietly [quietly]" |
| | dialogueSentences | 15 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.2 | | effectiveRatio | 0.133 | |
| 91.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1130 | | totalAiIsmAdverbs | 2 | | 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) | |
| 82.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1130 | | totalAiIsms | 4 | | found | | 0 | | | 1 | | | 2 | | word | "practiced ease" | | count | 1 |
| | 3 | |
| | highlights | | 0 | "lurched" | | 1 | "streaming" | | 2 | "practiced ease" | | 3 | "scanned" |
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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 | 0 | | narrationSentences | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1130 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1037 | | uniqueNames | 14 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 1 | | High | 1 | | Road | 1 | | Tomás | 1 | | Herrera | 6 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Northern | 1 | | Line | 1 | | Aldwych | 1 | | Saint | 1 | | Christopher | 1 | | Quinn | 11 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Quinn" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Road" | | 3 | "Soho" | | 4 | "Aldwych" |
| | globalScore | 0.97 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like it had once been a military s" |
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| 23.01% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.77 | | wordCount | 1130 | | matches | | 0 | "not the guilt of a man caught, but the particular horror of a man who understands" | | 1 | "not toward her but away, toward the far end of the platform" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 27.56 | | std | 23.91 | | cv | 0.868 | | sampleLengths | | 0 | 59 | | 1 | 7 | | 2 | 59 | | 3 | 51 | | 4 | 56 | | 5 | 41 | | 6 | 57 | | 7 | 45 | | 8 | 5 | | 9 | 47 | | 10 | 4 | | 11 | 78 | | 12 | 10 | | 13 | 3 | | 14 | 83 | | 15 | 59 | | 16 | 3 | | 17 | 64 | | 18 | 5 | | 19 | 11 | | 20 | 18 | | 21 | 3 | | 22 | 13 | | 23 | 4 | | 24 | 41 | | 25 | 4 | | 26 | 36 | | 27 | 37 | | 28 | 5 | | 29 | 35 | | 30 | 23 | | 31 | 11 | | 32 | 7 | | 33 | 57 | | 34 | 32 | | 35 | 17 | | 36 | 14 | | 37 | 12 | | 38 | 2 | | 39 | 9 | | 40 | 3 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 86 | | matches | | 0 | "was redacted" | | 1 | "been sealed" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 169 | | matches | | 0 | "was already moving" | | 1 | "was refusing" | | 2 | "were wearing" | | 3 | "was talking" | | 4 | "was watching" | | 5 | "weren't taking" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 96 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1040 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.038461538461538464 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007692307692307693 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.77 | | std | 10.62 | | cv | 0.902 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 18 | | 3 | 4 | | 4 | 3 | | 5 | 6 | | 6 | 1 | | 7 | 33 | | 8 | 19 | | 9 | 1 | | 10 | 11 | | 11 | 22 | | 12 | 3 | | 13 | 14 | | 14 | 14 | | 15 | 1 | | 16 | 2 | | 17 | 30 | | 18 | 4 | | 19 | 5 | | 20 | 16 | | 21 | 25 | | 22 | 20 | | 23 | 9 | | 24 | 28 | | 25 | 3 | | 26 | 4 | | 27 | 38 | | 28 | 5 | | 29 | 8 | | 30 | 5 | | 31 | 5 | | 32 | 29 | | 33 | 4 | | 34 | 6 | | 35 | 28 | | 36 | 6 | | 37 | 9 | | 38 | 29 | | 39 | 10 | | 40 | 3 | | 41 | 5 | | 42 | 5 | | 43 | 3 | | 44 | 3 | | 45 | 20 | | 46 | 27 | | 47 | 20 | | 48 | 24 | | 49 | 35 |
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| 72.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4791666666666667 | | totalSentences | 96 | | uniqueOpeners | 46 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 77 | | matches | | 0 | "Then a bus had lurched" | | 1 | "Then he was moving again," | | 2 | "Somewhere along the far wall," | | 3 | "Just a statement." | | 4 | "Then, slowly, he turned." |
| | ratio | 0.065 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 77 | | matches | | 0 | "She'd been close twenty seconds" | | 1 | "She didn't shout now." | | 2 | "He was fast, whoever he" | | 3 | "She came around the corner" | | 4 | "She'd read all forty." | | 5 | "She'd been watching the Raven's" | | 6 | "He was already halfway down." | | 7 | "She pulled out her phone" | | 8 | "She went down anyway." | | 9 | "It didn't come from any" | | 10 | "It rose from the floor" | | 11 | "It was full of people." | | 12 | "She pressed herself against the" | | 13 | "She found him within a" | | 14 | "She had a bone-handled knife" | | 15 | "Her hand had found the" | | 16 | "He saw Quinn at the" | | 17 | "He ran, and this time" | | 18 | "She followed him." |
| | ratio | 0.247 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 77 | | matches | | 0 | "Quinn's boots hit the wet" | | 1 | "She'd been close twenty seconds" | | 2 | "She didn't shout now." | | 3 | "Shouting warned them." | | 4 | "He was fast, whoever he" | | 5 | "She came around the corner" | | 6 | "A necklace, maybe." | | 7 | "The file on her desk" | | 8 | "NHS until eighteen months ago," | | 9 | "She'd read all forty." | | 10 | "Half of it was redacted." | | 11 | "She'd been watching the Raven's" | | 12 | "The figure ahead dropped down" | | 13 | "Quinn followed, landing harder, her" | | 14 | "A narrow alley opened onto" | | 15 | "The stairs descended at the" | | 16 | "He was already halfway down." | | 17 | "Quinn stopped at the top" | | 18 | "A train rumbled somewhere overhead." | | 19 | "She pulled out her phone" |
| | ratio | 0.714 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | 0 | "Before she watched him come" |
| | ratio | 0.013 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 7 | | matches | | 0 | "Moved like someone who knew these streets by memory rather than instinct, cutting through the gaps between market stalls that sat shuttered and padlocked, ducki…" | | 1 | "She came around the corner hard, her shoulder catching the brick, rain streaming down the back of her collar." | | 2 | "The stairs descended at the kind of steep angle that meant deep, and the sign above them, bolted crookedly to the fence, read CLOSED FOR MAINTENANCE in letters …" | | 3 | "Somewhere along the far wall, something in a cage shifted against the bars with a sound that wasn't quite animal." | | 4 | "Short and broad-shouldered, wearing something that looked like it had once been a military surplus jacket before someone had spent considerable time taking it a…" | | 5 | "At the thing in the cage that had gone quiet now but was watching her, she was certain of it, with an attention that felt nothing like an animal's." | | 6 | "Across the length of the platform, in the amber glow, his face registered something she read in less than a second: not the guilt of a man caught, but the parti…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |