| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 131 | | tagDensity | 0.122 | | leniency | 0.244 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2644 | | 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) | |
| 90.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2644 | | totalAiIsms | 5 | | 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 | 183 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 183 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 299 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2644 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 93 | | wordCount | 1735 | | uniqueNames | 13 | | maxNameDensity | 2.07 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 36 | | Tube | 1 | | Imran | 1 | | Bell | 29 | | Directly | 1 | | Museum | 1 | | Kowalski | 2 | | Camden | 1 | | One | 2 | | Eva | 13 | | Vale | 1 | | Dust | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Vale" | | 6 | "Dust" |
| | places | (empty) | | globalScore | 0.463 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 129 | | 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 | 2644 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 299 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 158 | | mean | 16.73 | | std | 17.4 | | cv | 1.04 | | sampleLengths | | 0 | 25 | | 1 | 85 | | 2 | 24 | | 3 | 4 | | 4 | 19 | | 5 | 4 | | 6 | 2 | | 7 | 30 | | 8 | 3 | | 9 | 3 | | 10 | 46 | | 11 | 5 | | 12 | 5 | | 13 | 1 | | 14 | 48 | | 15 | 32 | | 16 | 6 | | 17 | 7 | | 18 | 62 | | 19 | 28 | | 20 | 2 | | 21 | 35 | | 22 | 36 | | 23 | 2 | | 24 | 3 | | 25 | 4 | | 26 | 2 | | 27 | 30 | | 28 | 27 | | 29 | 4 | | 30 | 9 | | 31 | 3 | | 32 | 59 | | 33 | 5 | | 34 | 15 | | 35 | 6 | | 36 | 4 | | 37 | 36 | | 38 | 8 | | 39 | 1 | | 40 | 7 | | 41 | 16 | | 42 | 4 | | 43 | 3 | | 44 | 4 | | 45 | 11 | | 46 | 6 | | 47 | 58 | | 48 | 22 | | 49 | 4 |
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| 95.68% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 183 | | matches | | 0 | "were fastened" | | 1 | "been brushed" | | 2 | "been joined" | | 3 | "were lit" | | 4 | "been given" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 261 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 299 | | ratio | 0.007 | | matches | | 0 | "Quinn had listened to the first seven seconds in the car: Harlow, if someone tells you the Camden station was sealed, check the—" | | 1 | "The maintenance crew had entered from the stairs; their tread had cut deep through dust." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1002 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over polished brown boots." |
| | adverbCount | 11 | | adverbRatio | 0.010978043912175649 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 299 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 299 | | mean | 8.84 | | std | 6.08 | | cv | 0.688 | | sampleLengths | | 0 | 8 | | 1 | 17 | | 2 | 14 | | 3 | 24 | | 4 | 12 | | 5 | 17 | | 6 | 5 | | 7 | 9 | | 8 | 4 | | 9 | 24 | | 10 | 4 | | 11 | 14 | | 12 | 5 | | 13 | 4 | | 14 | 2 | | 15 | 6 | | 16 | 2 | | 17 | 7 | | 18 | 15 | | 19 | 3 | | 20 | 3 | | 21 | 12 | | 22 | 15 | | 23 | 9 | | 24 | 10 | | 25 | 5 | | 26 | 5 | | 27 | 1 | | 28 | 9 | | 29 | 12 | | 30 | 8 | | 31 | 19 | | 32 | 6 | | 33 | 26 | | 34 | 5 | | 35 | 1 | | 36 | 7 | | 37 | 11 | | 38 | 19 | | 39 | 5 | | 40 | 17 | | 41 | 10 | | 42 | 5 | | 43 | 23 | | 44 | 2 | | 45 | 35 | | 46 | 5 | | 47 | 15 | | 48 | 4 | | 49 | 12 |
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| 58.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.36789297658862874 | | totalSentences | 299 | | uniqueOpeners | 110 | |
| 57.80% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 173 | | matches | | 0 | "Directly under the dead man’s" | | 1 | "More strips marked the next" | | 2 | "Then she looked at the" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 173 | | matches | | 0 | "Its cable hung in two" | | 1 | "Their owners had gone." | | 2 | "He glanced at the stalls" | | 3 | "She moved past a stall" | | 4 | "He wore disposable overshoes over" | | 5 | "He wore a dark wool" | | 6 | "His head had fallen forward." | | 7 | "His left lay open against" | | 8 | "She reached for an evidence" | | 9 | "She studied the tiles beneath" | | 10 | "She followed the edge of" | | 11 | "Its surviving compartment held a" | | 12 | "She had not called back." | | 13 | "Its hasp bent outwards, towards" | | 14 | "She pointed to the counter" | | 15 | "It was set close to" | | 16 | "Its rear curtain touched the" | | 17 | "She slipped a torch from" | | 18 | "She lifted her eyes to" | | 19 | "He walked off, shoulders squared" |
| | ratio | 0.173 | |
| 49.60% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 142 | | totalSentences | 173 | | matches | | 0 | "The train display above the" | | 1 | "Its cable hung in two" | | 2 | "Detective Harlow Quinn ducked under" | | 3 | "A draught lifted the edge" | | 4 | "Velvet cloths covered their counters." | | 5 | "Their owners had gone." | | 6 | "A uniformed constable met her" | | 7 | "He glanced at the stalls" | | 8 | "Quinn checked her worn leather" | | 9 | "The call had reached her" | | 10 | "She moved past a stall" | | 11 | "Teeth, she thought, until she" | | 12 | "The writing on them shifted" | | 13 | "A white forensic tent stood" | | 14 | "DS Imran Bell waited outside" | | 15 | "He wore disposable overshoes over" | | 16 | "Bell lifted the tent flap" | | 17 | "Quinn stopped at the flap." | | 18 | "He wore a dark wool" | | 19 | "His head had fallen forward." |
| | ratio | 0.821 | |
| 28.90% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 173 | | matches | | 0 | "Whoever had buttoned it had" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 2 | | matches | | 0 | "A uniformed constable met her beside a tiled sign that read CAMDEN ROAD, though the station of that name stood nowhere near this tunnel." | | 1 | "A constable hovered beside her with the expression of a man who had been given a complicated explanation and found it worse than no explanation." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 131 | | tagDensity | 0.038 | | leniency | 0.076 | | rawRatio | 0 | | effectiveRatio | 0 | |