| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 65 | | tagDensity | 0.108 | | leniency | 0.215 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1600 | | totalAiIsmAdverbs | 1 | | 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) | |
| 59.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1600 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "velvet" | | 1 | "pulsed" | | 2 | "trembled" | | 3 | "shattered" | | 4 | "lurched" | | 5 | "vibrated" | | 6 | "perfect" | | 7 | "eyebrow" | | 8 | "raced" | | 9 | "familiar" |
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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 | 181 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 181 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1600 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 66 | | wordCount | 1337 | | uniqueNames | 8 | | maxNameDensity | 2.17 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Bell" | | discoveredNames | | Harlow | 1 | | Quinn | 29 | | Tube | 1 | | Camden | 2 | | Town | 1 | | Bell | 26 | | Morris | 2 | | Three | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Morris" |
| | places | | | globalScore | 0.415 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like glass breaking" |
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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 | 1600 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 239 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 162 | | mean | 9.88 | | std | 11 | | cv | 1.114 | | sampleLengths | | 0 | 6 | | 1 | 47 | | 2 | 6 | | 3 | 2 | | 4 | 1 | | 5 | 1 | | 6 | 28 | | 7 | 50 | | 8 | 13 | | 9 | 4 | | 10 | 5 | | 11 | 6 | | 12 | 25 | | 13 | 23 | | 14 | 6 | | 15 | 6 | | 16 | 3 | | 17 | 4 | | 18 | 2 | | 19 | 48 | | 20 | 4 | | 21 | 1 | | 22 | 7 | | 23 | 3 | | 24 | 25 | | 25 | 5 | | 26 | 4 | | 27 | 7 | | 28 | 5 | | 29 | 4 | | 30 | 6 | | 31 | 5 | | 32 | 5 | | 33 | 1 | | 34 | 9 | | 35 | 12 | | 36 | 21 | | 37 | 6 | | 38 | 7 | | 39 | 2 | | 40 | 2 | | 41 | 5 | | 42 | 9 | | 43 | 11 | | 44 | 5 | | 45 | 6 | | 46 | 5 | | 47 | 32 | | 48 | 1 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 181 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 236 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1339 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.02539208364451083 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0014936519790888724 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 6.69 | | std | 4.83 | | cv | 0.722 | | sampleLengths | | 0 | 6 | | 1 | 13 | | 2 | 34 | | 3 | 6 | | 4 | 2 | | 5 | 1 | | 6 | 1 | | 7 | 4 | | 8 | 16 | | 9 | 5 | | 10 | 3 | | 11 | 12 | | 12 | 14 | | 13 | 8 | | 14 | 16 | | 15 | 13 | | 16 | 4 | | 17 | 5 | | 18 | 6 | | 19 | 6 | | 20 | 19 | | 21 | 23 | | 22 | 6 | | 23 | 6 | | 24 | 3 | | 25 | 4 | | 26 | 2 | | 27 | 3 | | 28 | 18 | | 29 | 14 | | 30 | 13 | | 31 | 4 | | 32 | 1 | | 33 | 7 | | 34 | 3 | | 35 | 11 | | 36 | 14 | | 37 | 5 | | 38 | 4 | | 39 | 7 | | 40 | 5 | | 41 | 4 | | 42 | 6 | | 43 | 5 | | 44 | 5 | | 45 | 1 | | 46 | 9 | | 47 | 11 | | 48 | 1 | | 49 | 2 |
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| 55.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3514644351464435 | | totalSentences | 239 | | uniqueOpeners | 84 | |
| 62.89% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 159 | | matches | | 0 | "Then the station lights came" | | 1 | "Somewhere, a bell rang once." | | 2 | "Then the door closed on" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 159 | | matches | | 0 | "Her leather watch clicked against" | | 1 | "He had broad shoulders, a" | | 2 | "His skin held a faint" | | 3 | "She turned to the forensic" | | 4 | "It contained glints of green." | | 5 | "She touched the grit with" | | 6 | "She placed the swab into" | | 7 | "Her torch found a section" | | 8 | "She withdrew her hand." | | 9 | "His fingers clawed at the" | | 10 | "It skittered across the platform" | | 11 | "It pointed at Quinn." | | 12 | "Its neck stretched, skin pulling" | | 13 | "She had heard him knocking" | | 14 | "She snatched the evidence bag" | | 15 | "She drove her boot into" | | 16 | "It fell backwards, but its" | | 17 | "Its needle swung away from" | | 18 | "It formed a perfect circle" | | 19 | "It had marked a lock." |
| | ratio | 0.138 | |
| 44.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 159 | | matches | | 0 | "Detective Harlow Quinn stopped with" | | 1 | "A body lay beneath the" | | 2 | "Quinn lowered her foot." | | 3 | "Her leather watch clicked against" | | 4 | "The habit steadied her hands." | | 5 | "Nothing else did." | | 6 | "The abandoned station stretched around" | | 7 | "A cracked sign still promised" | | 8 | "The air tasted of coal" | | 9 | "Inspector Bell stood beside the" | | 10 | "Bell pointed with a gloved" | | 11 | "He had broad shoulders, a" | | 12 | "Quinn looked at the victim’s" | | 13 | "Bell’s mouth tightened." | | 14 | "The dead man wore a" | | 15 | "His skin held a faint" | | 16 | "Quinn eased a finger beneath" | | 17 | "A narrow groove circled the" | | 18 | "Bell glanced towards the constable." | | 19 | "The constable bent closer, then" |
| | ratio | 0.83 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 159 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "He had broad shoulders, a broad face and the loose confidence of a man who preferred his conclusions ready-made." | | 1 | "Static poured from the speaker, followed by a woman’s voice speaking in a language that sounded like glass breaking." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "she ordered (order)" | | 1 | "Bell whispered (whisper)" |
| | dialogueSentences | 65 | | tagDensity | 0.092 | | leniency | 0.185 | | rawRatio | 0.333 | | effectiveRatio | 0.062 | |