| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1271 | | 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) | |
| 64.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1271 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "perfect" | | 1 | "flawless" | | 2 | "traced" | | 3 | "etched" | | 4 | "stark" | | 5 | "flicked" | | 6 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 100 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 100 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1271 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 945 | | uniqueNames | 15 | | maxNameDensity | 1.59 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Met | 1 | | Ray | 1 | | Frayne | 11 | | Kowalski | 2 | | Eva | 7 | | British | 1 | | Museum | 1 | | Aurora | 1 | | Vane | 1 | | Brighton | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Ray" | | 3 | "Frayne" | | 4 | "Kowalski" | | 5 | "Eva" | | 6 | "Market" | | 7 | "Morris" |
| | places | | 0 | "British" | | 1 | "Brighton" | | 2 | "Veil" |
| | globalScore | 0.706 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 42.64% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.574 | | wordCount | 1271 | | matches | | 0 | "No footprints but" | | 1 | "not with the compass, but with her free hand" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 17.65 | | std | 20.36 | | cv | 1.153 | | sampleLengths | | 0 | 18 | | 1 | 37 | | 2 | 84 | | 3 | 1 | | 4 | 17 | | 5 | 4 | | 6 | 17 | | 7 | 4 | | 8 | 24 | | 9 | 4 | | 10 | 5 | | 11 | 4 | | 12 | 2 | | 13 | 42 | | 14 | 64 | | 15 | 4 | | 16 | 8 | | 17 | 7 | | 18 | 2 | | 19 | 11 | | 20 | 7 | | 21 | 53 | | 22 | 10 | | 23 | 25 | | 24 | 3 | | 25 | 6 | | 26 | 45 | | 27 | 21 | | 28 | 8 | | 29 | 41 | | 30 | 4 | | 31 | 57 | | 32 | 12 | | 33 | 2 | | 34 | 3 | | 35 | 5 | | 36 | 2 | | 37 | 27 | | 38 | 66 | | 39 | 3 | | 40 | 6 | | 41 | 6 | | 42 | 3 | | 43 | 6 | | 44 | 4 | | 45 | 11 | | 46 | 6 | | 47 | 7 | | 48 | 46 | | 49 | 52 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 135 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 950 | | adjectiveStacks | 1 | | stackExamples | | 0 | "stark against pale skin." |
| | adverbCount | 17 | | adverbRatio | 0.017894736842105262 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005263157894736842 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 9.41 | | std | 7.35 | | cv | 0.781 | | sampleLengths | | 0 | 18 | | 1 | 14 | | 2 | 2 | | 3 | 21 | | 4 | 15 | | 5 | 12 | | 6 | 22 | | 7 | 24 | | 8 | 11 | | 9 | 1 | | 10 | 11 | | 11 | 6 | | 12 | 4 | | 13 | 17 | | 14 | 4 | | 15 | 4 | | 16 | 20 | | 17 | 4 | | 18 | 5 | | 19 | 4 | | 20 | 2 | | 21 | 42 | | 22 | 3 | | 23 | 14 | | 24 | 6 | | 25 | 11 | | 26 | 6 | | 27 | 24 | | 28 | 4 | | 29 | 8 | | 30 | 7 | | 31 | 2 | | 32 | 11 | | 33 | 7 | | 34 | 3 | | 35 | 15 | | 36 | 6 | | 37 | 4 | | 38 | 5 | | 39 | 14 | | 40 | 6 | | 41 | 10 | | 42 | 7 | | 43 | 14 | | 44 | 2 | | 45 | 2 | | 46 | 3 | | 47 | 6 | | 48 | 3 | | 49 | 8 |
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| 48.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3333333333333333 | | totalSentences | 135 | | uniqueOpeners | 45 | |
| 36.23% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 92 | | matches | | 0 | "Somewhere deeper in the tunnel," |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 92 | | matches | | 0 | "She rose in one smooth" | | 1 | "His shoes crunched in the" | | 2 | "She glanced at him." | | 3 | "She nodded at the body." | | 4 | "His left fist was locked" | | 5 | "His right hand lay open," | | 6 | "She checked the dead man’s" | | 7 | "She turned his torso gently." | | 8 | "She traced the floor with" | | 9 | "She prised the dead man's" | | 10 | "She looked at the right" | | 11 | "Her gaze returned to the" | | 12 | "It strained towards the tunnel" | | 13 | "She tucked a stray curl" | | 14 | "She knew exactly who Eva" | | 15 | "She walked towards the tunnel" | | 16 | "She had eyes only for" | | 17 | "She stopped an arm's length" | | 18 | "He stopped beside her." | | 19 | "She glanced back at the" |
| | ratio | 0.228 | |
| 57.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 92 | | matches | | 0 | "The blood had dried in" | | 1 | "Detective Harlow Quinn didn’t touch" | | 2 | "She rose in one smooth" | | 3 | "The abandoned platform around her" | | 4 | "The air held the dregs" | | 5 | "DC Ray Frayne picked his" | | 6 | "His shoes crunched in the" | | 7 | "She glanced at him." | | 8 | "Frayne was young, eager, the" | | 9 | "She nodded at the body." | | 10 | "Quinn crouched again." | | 11 | "The dead man lay on" | | 12 | "His left fist was locked" | | 13 | "His right hand lay open," | | 14 | "Quinn pointed a gloved finger" | | 15 | "Quinn leaned closer." | | 16 | "The congealed ring was flawless," | | 17 | "She checked the dead man’s" | | 18 | "She turned his torso gently." | | 19 | "A single puncture wound, neat" |
| | ratio | 0.804 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Now the pattern stood in" |
| | ratio | 0.011 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 3 | | matches | | 0 | "Splintered trestle tables sat upended, stripped of the enchanted goods and banned alchemical substances that would return with the next full moon." | | 1 | "No scuff marks disturbed the thick dust beyond the single line of footprints that led from the tunnel mouth to the body and stopped." | | 2 | "The fingertips were stained with verdigris, the same green crust that coated the compass casing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |