| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn moved back [back]" |
| | dialogueSentences | 45 | | tagDensity | 0.289 | | leniency | 0.578 | | rawRatio | 0.077 | | effectiveRatio | 0.044 | |
| 96.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | 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) | |
| 59.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "traced" | | 1 | "mechanical" | | 2 | "perfect" | | 3 | "echoed" | | 4 | "stomach" | | 5 | "chill" | | 6 | "pristine" | | 7 | "flickered" | | 8 | "pulsed" |
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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 | 88 | | matches | (empty) | |
| 94.16% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1338 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 44.18% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 945 | | uniqueNames | 26 | | maxNameDensity | 2.12 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 20 | | Victorian | 2 | | Chen | 5 | | Veil | 1 | | Market | 1 | | Blitz | 1 | | Transport | 1 | | London | 1 | | Seven | 1 | | Round | 1 | | Mesopotamian | 1 | | British | 1 | | Museum | 1 | | Eva | 1 | | Kowalski | 1 | | Research | 1 | | Assistant | 1 | | Department | 1 | | Ancient | 1 | | History | 1 | | One | 1 | | Morris | 1 | | Deptford | 1 | | Recent | 1 | | Greek | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Chen" | | 3 | "Market" | | 4 | "London" | | 5 | "Eva" | | 6 | "Kowalski" | | 7 | "Morris" | | 8 | "Recent" |
| | places | | | globalScore | 0.442 | | windowScore | 0.5 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 4 | | matches | | 0 | "countants and, apparently, whatever clientele" | | 1 | "darkness that seemed to press against her torch beam" | | 2 | "looked like Greek characters" | | 3 | "omething she'd apparently found" |
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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 | 1338 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 120 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 25.25 | | std | 19.42 | | cv | 0.769 | | sampleLengths | | 0 | 4 | | 1 | 60 | | 2 | 31 | | 3 | 40 | | 4 | 3 | | 5 | 14 | | 6 | 46 | | 7 | 48 | | 8 | 37 | | 9 | 3 | | 10 | 1 | | 11 | 73 | | 12 | 13 | | 13 | 39 | | 14 | 11 | | 15 | 11 | | 16 | 39 | | 17 | 15 | | 18 | 36 | | 19 | 2 | | 20 | 53 | | 21 | 16 | | 22 | 42 | | 23 | 4 | | 24 | 49 | | 25 | 7 | | 26 | 49 | | 27 | 10 | | 28 | 50 | | 29 | 31 | | 30 | 11 | | 31 | 4 | | 32 | 29 | | 33 | 2 | | 34 | 32 | | 35 | 1 | | 36 | 1 | | 37 | 6 | | 38 | 50 | | 39 | 28 | | 40 | 3 | | 41 | 6 | | 42 | 46 | | 43 | 18 | | 44 | 6 | | 45 | 22 | | 46 | 47 | | 47 | 29 | | 48 | 45 | | 49 | 7 |
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| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "been met" | | 1 | "been sketched" | | 2 | "been cleaned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 160 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 120 | | 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 | 29 | | adverbRatio | 0.030623020063357972 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009503695881731784 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 11.15 | | std | 7.79 | | cv | 0.698 | | sampleLengths | | 0 | 4 | | 1 | 25 | | 2 | 14 | | 3 | 21 | | 4 | 12 | | 5 | 19 | | 6 | 4 | | 7 | 9 | | 8 | 11 | | 9 | 16 | | 10 | 3 | | 11 | 14 | | 12 | 13 | | 13 | 33 | | 14 | 12 | | 15 | 3 | | 16 | 2 | | 17 | 31 | | 18 | 23 | | 19 | 14 | | 20 | 3 | | 21 | 1 | | 22 | 14 | | 23 | 10 | | 24 | 22 | | 25 | 27 | | 26 | 8 | | 27 | 5 | | 28 | 18 | | 29 | 14 | | 30 | 7 | | 31 | 11 | | 32 | 4 | | 33 | 7 | | 34 | 6 | | 35 | 6 | | 36 | 10 | | 37 | 17 | | 38 | 15 | | 39 | 13 | | 40 | 23 | | 41 | 2 | | 42 | 12 | | 43 | 24 | | 44 | 6 | | 45 | 11 | | 46 | 5 | | 47 | 11 | | 48 | 22 | | 49 | 11 |
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| 81.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5166666666666667 | | totalSentences | 120 | | uniqueOpeners | 62 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 81 | | matches | | 0 | "She pulled out her torch," | | 1 | "She held up the card" | | 2 | "Her torch beam found what" | | 3 | "She bagged the token, moved" | | 4 | "It was waiting for something." | | 5 | "She released the handle, stepped" | | 6 | "He retrieved the bag without" | | 7 | "It had simply decided to" | | 8 | "She had commendations for clear" | | 9 | "She had lost a partner" | | 10 | "She pulled out her phone," |
| | ratio | 0.136 | |
| 89.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 81 | | matches | | 0 | "The blood pooled wrong." | | 1 | "Detective Harlow Quinn crouched at" | | 2 | "Blood followed gravity, sought the" | | 3 | "This blood had stopped six" | | 4 | "DS Chen's voice echoed through" | | 5 | "Quinn didn't look up." | | 6 | "The Veil Market operated from" | | 7 | "The bone token in the" | | 8 | "The station hadn't seen regular" | | 9 | "Quinn walked the perimeter of" | | 10 | "The body lay on its" | | 11 | "Quinn's stomach clenched." | | 12 | "She pulled out her torch," | | 13 | "A worn leather satchel lay" | | 14 | "Chen moved closer, his own" | | 15 | "Quinn photographed the scene on" | | 16 | "The official photographer would document" | | 17 | "Plenty of time to stage" | | 18 | "Quinn approached the satchel, crouched" | | 19 | "A worn notebook filled with" |
| | ratio | 0.741 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 81 | | matches | | 0 | "Now she stood, knees protesting" | | 1 | "Now she stared at evidence" |
| | ratio | 0.025 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "The station hadn't seen regular service since the forties, mothballed during the Blitz and forgotten by everyone except Transport for London's accountants and, …" | | 1 | "The notebook's final entry caught her attention: coordinates, a date that matched today, and a single phrase in what looked like Greek characters." | | 2 | "Now she stared at evidence that rewrote itself, at doors that shouldn't exist, at the arranged body of a researcher who had been looking for something she'd app…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |