| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 83 | | tagDensity | 0.084 | | leniency | 0.169 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1989 | | 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) | |
| 94.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1989 | | totalAiIsms | 2 | | 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 | 191 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 191 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 267 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1989 | | 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 | 58 | | wordCount | 1634 | | uniqueNames | 7 | | maxNameDensity | 2.57 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 42 | | Static | 2 | | Tube | 1 | | Vale | 5 | | Market | 2 | | Mrs | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Static" | | 3 | "Vale" | | 4 | "Market" | | 5 | "Mrs" |
| | places | (empty) | | globalScore | 0.215 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 135 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like letters until she tried to re" |
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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 | 1989 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 267 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 153 | | mean | 13 | | std | 14.71 | | cv | 1.132 | | sampleLengths | | 0 | 33 | | 1 | 2 | | 2 | 27 | | 3 | 25 | | 4 | 19 | | 5 | 14 | | 6 | 29 | | 7 | 4 | | 8 | 3 | | 9 | 31 | | 10 | 4 | | 11 | 6 | | 12 | 28 | | 13 | 3 | | 14 | 4 | | 15 | 24 | | 16 | 3 | | 17 | 5 | | 18 | 46 | | 19 | 7 | | 20 | 4 | | 21 | 11 | | 22 | 8 | | 23 | 4 | | 24 | 6 | | 25 | 2 | | 26 | 32 | | 27 | 10 | | 28 | 4 | | 29 | 10 | | 30 | 66 | | 31 | 11 | | 32 | 54 | | 33 | 8 | | 34 | 47 | | 35 | 2 | | 36 | 35 | | 37 | 8 | | 38 | 7 | | 39 | 5 | | 40 | 39 | | 41 | 5 | | 42 | 3 | | 43 | 11 | | 44 | 13 | | 45 | 4 | | 46 | 3 | | 47 | 5 | | 48 | 28 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 191 | | matches | | 0 | "been prised" | | 1 | "been fixed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 286 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 267 | | ratio | 0.004 | | matches | | 0 | "One wore a leather apron; the other had a silver hook where his left hand should have been." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1415 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.023321554770318022 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0028268551236749115 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 267 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 267 | | mean | 7.45 | | std | 4.58 | | cv | 0.615 | | sampleLengths | | 0 | 10 | | 1 | 23 | | 2 | 2 | | 3 | 5 | | 4 | 11 | | 5 | 11 | | 6 | 7 | | 7 | 18 | | 8 | 12 | | 9 | 7 | | 10 | 2 | | 11 | 12 | | 12 | 9 | | 13 | 4 | | 14 | 16 | | 15 | 4 | | 16 | 3 | | 17 | 10 | | 18 | 7 | | 19 | 10 | | 20 | 4 | | 21 | 4 | | 22 | 6 | | 23 | 2 | | 24 | 9 | | 25 | 5 | | 26 | 12 | | 27 | 3 | | 28 | 4 | | 29 | 8 | | 30 | 11 | | 31 | 5 | | 32 | 3 | | 33 | 5 | | 34 | 3 | | 35 | 8 | | 36 | 14 | | 37 | 7 | | 38 | 14 | | 39 | 7 | | 40 | 4 | | 41 | 3 | | 42 | 8 | | 43 | 5 | | 44 | 3 | | 45 | 4 | | 46 | 6 | | 47 | 2 | | 48 | 7 | | 49 | 12 |
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| 49.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3146067415730337 | | totalSentences | 267 | | uniqueOpeners | 84 | |
| 37.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 178 | | matches | | 0 | "Then they seemed to turn" | | 1 | "Somewhere, a child laughed, then" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 178 | | matches | | 0 | "His hood slipped, showing a" | | 1 | "He clutched a canvas satchel" | | 2 | "She caught herself against a" | | 3 | "she said into the radio" | | 4 | "Her partner’s voice broke through" | | 5 | "His left leg buckled." | | 6 | "He kept going, limping now," | | 7 | "He swung around a corner" | | 8 | "His free hand came up." | | 9 | "He looked past her, not" | | 10 | "He fumbled with the handle." | | 11 | "He slipped through, dragging the" | | 12 | "She aimed her torch down" | | 13 | "Her radio hissed." | | 14 | "She looked at the bare" | | 15 | "She kept her baton out," | | 16 | "Her worn leather watch tapped" | | 17 | "She rose and followed the" | | 18 | "It was bone-white, cut with" | | 19 | "She pressed the transmit button." |
| | ratio | 0.242 | |
| 13.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 159 | | totalSentences | 178 | | matches | | 0 | "Rain blurred the shopfronts into" | | 1 | "Detective Harlow Quinn kept one" | | 2 | "The man ahead glanced back." | | 3 | "His hood slipped, showing a" | | 4 | "He clutched a canvas satchel" | | 5 | "Quinn’s shoes skidded on the" | | 6 | "She caught herself against a" | | 7 | "she said into the radio" | | 8 | "Her partner’s voice broke through" | | 9 | "The suspect vaulted a low" | | 10 | "His left leg buckled." | | 11 | "He kept going, limping now," | | 12 | "Quinn lengthened her stride." | | 13 | "He swung around a corner" | | 14 | "A delivery van blocked the" | | 15 | "The man reached it, grabbed" | | 16 | "The doors stayed shut." | | 17 | "Quinn drew her baton." | | 18 | "Rain ran off his hood" | | 19 | "His free hand came up." |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 178 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 1 | | matches | | 0 | "Behind her, the Market kept talking, stall by stall, as if the old station had swallowed the alarm." |
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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 | 7 | | fancyCount | 2 | | fancyTags | | 0 | "handed man said (hand say)" | | 1 | "Quinn ordered (order)" |
| | dialogueSentences | 83 | | tagDensity | 0.084 | | leniency | 0.169 | | rawRatio | 0.286 | | effectiveRatio | 0.048 | |