| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.237 | | leniency | 0.474 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1823 | | 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) | |
| 75.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1823 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "reminder" | | 1 | "footfall" | | 2 | "resolved" | | 3 | "efficient" | | 4 | "clenching" | | 5 | "flickered" | | 6 | "perfect" | | 7 | "silence" |
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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 | 156 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 156 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1823 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1438 | | uniqueNames | 18 | | maxNameDensity | 1.32 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Hawley | 1 | | Crescent | 1 | | Quinn | 19 | | Camden | 2 | | Lock | 1 | | Victorian | 3 | | Hendon | 1 | | Morris | 3 | | Rotherhithe | 1 | | Internal | 1 | | Affairs | 1 | | London | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 8 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Affairs" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" |
| | places | | 0 | "Hawley" | | 1 | "Crescent" | | 2 | "Camden" | | 3 | "Victorian" | | 4 | "Hendon" | | 5 | "Rotherhithe" | | 6 | "London" |
| | globalScore | 0.839 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 1 | | matches | | 0 | "something like cinnamon and hot metal and ol" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.549 | | wordCount | 1823 | | matches | | 0 | "not running but flowing, the way a school of fish parts before a shadow" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 23.37 | | std | 21.24 | | cv | 0.909 | | sampleLengths | | 0 | 33 | | 1 | 19 | | 2 | 3 | | 3 | 7 | | 4 | 73 | | 5 | 53 | | 6 | 48 | | 7 | 1 | | 8 | 32 | | 9 | 61 | | 10 | 22 | | 11 | 13 | | 12 | 88 | | 13 | 44 | | 14 | 15 | | 15 | 2 | | 16 | 73 | | 17 | 20 | | 18 | 17 | | 19 | 26 | | 20 | 10 | | 21 | 46 | | 22 | 94 | | 23 | 26 | | 24 | 3 | | 25 | 26 | | 26 | 54 | | 27 | 2 | | 28 | 57 | | 29 | 28 | | 30 | 2 | | 31 | 45 | | 32 | 2 | | 33 | 11 | | 34 | 31 | | 35 | 19 | | 36 | 3 | | 37 | 5 | | 38 | 29 | | 39 | 2 | | 40 | 42 | | 41 | 7 | | 42 | 16 | | 43 | 48 | | 44 | 2 | | 45 | 23 | | 46 | 7 | | 47 | 21 | | 48 | 9 | | 49 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 156 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 243 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 184 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1446 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short curly dark hair," |
| | adverbCount | 29 | | adverbRatio | 0.020055325034578148 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005532503457814661 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 9.91 | | std | 7.93 | | cv | 0.8 | | sampleLengths | | 0 | 18 | | 1 | 15 | | 2 | 3 | | 3 | 2 | | 4 | 5 | | 5 | 9 | | 6 | 3 | | 7 | 4 | | 8 | 3 | | 9 | 4 | | 10 | 5 | | 11 | 25 | | 12 | 16 | | 13 | 23 | | 14 | 5 | | 15 | 21 | | 16 | 12 | | 17 | 15 | | 18 | 11 | | 19 | 18 | | 20 | 11 | | 21 | 8 | | 22 | 1 | | 23 | 6 | | 24 | 12 | | 25 | 4 | | 26 | 2 | | 27 | 8 | | 28 | 9 | | 29 | 20 | | 30 | 5 | | 31 | 27 | | 32 | 5 | | 33 | 3 | | 34 | 14 | | 35 | 5 | | 36 | 1 | | 37 | 3 | | 38 | 2 | | 39 | 2 | | 40 | 14 | | 41 | 19 | | 42 | 3 | | 43 | 5 | | 44 | 24 | | 45 | 2 | | 46 | 2 | | 47 | 19 | | 48 | 8 | | 49 | 2 |
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| 72.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.45652173913043476 | | totalSentences | 184 | | uniqueOpeners | 84 | |
| 74.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 134 | | matches | | 0 | "Just the pale oval of" | | 1 | "Then the figure dropped." | | 2 | "Always filed away in the" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 134 | | matches | | 0 | "She caught herself on a" | | 1 | "They never stopped." | | 2 | "She'd lost signal two streets" | | 3 | "Her left knee, the one" | | 4 | "She squeezed past, felt a" | | 5 | "She reached the spot twelve" | | 6 | "She checked her radio again." | | 7 | "She kept a separate notebook" | | 8 | "Her torch beam swung across" | | 9 | "She counted them the way" | | 10 | "She pressed her back to" | | 11 | "She recognised the curved ceiling," | | 12 | "She'd heard the name in" | | 13 | "It was real." | | 14 | "She kept her warrant card" | | 15 | "He gestured at her arm." | | 16 | "He pulled a stool from" | | 17 | "His stall, she noticed, was" | | 18 | "He cleaned the wound without" | | 19 | "She catalogued the detail automatically." |
| | ratio | 0.239 | |
| 83.13% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 101 | | totalSentences | 134 | | matches | | 0 | "The suspect cut left on" | | 1 | "She caught herself on a" | | 2 | "That bothered her more than" | | 3 | "The figure didn't stop." | | 4 | "They never stopped." | | 5 | "Quinn keyed her radio." | | 6 | "Nothing but static hissed back." | | 7 | "She'd lost signal two streets" | | 8 | "Her left knee, the one" | | 9 | "The suspect rounded another corner." | | 10 | "Quinn rounded it three seconds" | | 11 | "She squeezed past, felt a" | | 12 | "The leather band of her" | | 13 | "An alley opened ahead, barely" | | 14 | "Victorian brick on both sides," | | 15 | "The rain fell straight down" | | 16 | "Quinn couldn't make out a" | | 17 | "She reached the spot twelve" | | 18 | "A grate in the pavement," | | 19 | "Stone steps descended into blackness." |
| | ratio | 0.754 | |
| 37.31% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 134 | | matches | | 0 | "Before he could answer, the" |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 3 | | matches | | 0 | "Warm air drifted up, carrying a smell that didn't belong in Camden, something like cinnamon and hot metal and old books left too long in the sun." | | 1 | "Jars of liquid that shifted colour when she looked at them." | | 2 | "A man stood beside a narrow stall wedged between a bookseller and something that smelled of burnt sage." |
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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 | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |