| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 923 | | 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) | |
| 67.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 923 | | totalAiIsms | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "practiced ease" | | count | 1 |
| | 4 | | | 5 | |
| | highlights | | 0 | "flickered" | | 1 | "unreadable" | | 2 | "gloom" | | 3 | "practiced ease" | | 4 | "could feel" | | 5 | "echoing" |
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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 | 56 | | matches | (empty) | |
| 40.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | 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 | 923 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 40.38% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 821 | | uniqueNames | 15 | | maxNameDensity | 2.19 | | worstName | "Tomás" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Tomás" | | discoveredNames | | Tomás | 18 | | Herrera | 1 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Harlow | 1 | | Quinn | 11 | | Saint | 1 | | Christopher | 1 | | Camden | 3 | | Morris | 2 | | London | 1 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Harlow" | | 4 | "Quinn" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Camden" | | 8 | "Morris" |
| | places | | | globalScore | 0.404 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 91.66% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.083 | | wordCount | 923 | | matches | | 0 | "not with a gun drawn, but with the certainty of a woman who had nothing left to lose b" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 40.13 | | std | 24.36 | | cv | 0.607 | | sampleLengths | | 0 | 83 | | 1 | 7 | | 2 | 31 | | 3 | 53 | | 4 | 34 | | 5 | 40 | | 6 | 4 | | 7 | 65 | | 8 | 29 | | 9 | 4 | | 10 | 50 | | 11 | 40 | | 12 | 12 | | 13 | 13 | | 14 | 85 | | 15 | 47 | | 16 | 56 | | 17 | 21 | | 18 | 68 | | 19 | 21 | | 20 | 76 | | 21 | 29 | | 22 | 55 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 68 | | ratio | 0.015 | | matches | | 0 | "Her partner’s death haunted the walls here; she could feel the residue of it in the flickering light and the hush that fell over vendors when they spotted her." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 826 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.015738498789346248 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007263922518159807 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 13.57 | | std | 8.41 | | cv | 0.62 | | sampleLengths | | 0 | 9 | | 1 | 19 | | 2 | 19 | | 3 | 11 | | 4 | 25 | | 5 | 7 | | 6 | 14 | | 7 | 11 | | 8 | 6 | | 9 | 3 | | 10 | 6 | | 11 | 23 | | 12 | 9 | | 13 | 12 | | 14 | 4 | | 15 | 18 | | 16 | 12 | | 17 | 3 | | 18 | 33 | | 19 | 4 | | 20 | 4 | | 21 | 9 | | 22 | 18 | | 23 | 18 | | 24 | 20 | | 25 | 13 | | 26 | 5 | | 27 | 6 | | 28 | 5 | | 29 | 4 | | 30 | 3 | | 31 | 14 | | 32 | 18 | | 33 | 15 | | 34 | 16 | | 35 | 10 | | 36 | 14 | | 37 | 5 | | 38 | 7 | | 39 | 10 | | 40 | 3 | | 41 | 5 | | 42 | 18 | | 43 | 17 | | 44 | 20 | | 45 | 25 | | 46 | 30 | | 47 | 17 | | 48 | 9 | | 49 | 18 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.35294117647058826 | | totalSentences | 68 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 54 | | matches | | 0 | "He burst through the green" | | 1 | "She had waited in the" | | 2 | "His short curly dark brown" | | 3 | "He knew about the supernatural" | | 4 | "They turned onto a street" | | 5 | "His medallion clung to his" | | 6 | "He slipped it into the" | | 7 | "She paused at the threshold," | | 8 | "Her partner’s death haunted the" | | 9 | "She watched Tomás reach a" | | 10 | "She had followed Tomás through" | | 11 | "She had the choice to" | | 12 | "She moved into the market," |
| | ratio | 0.241 | |
| 6.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 54 | | matches | | 0 | "Rain drilled into the collar" | | 1 | "He burst through the green" | | 2 | "Detective Harlow Quinn had watched" | | 3 | "She had waited in the" | | 4 | "Quinn stepped into the downpour," | | 5 | "Militant precision carried her forward," | | 6 | "Tomás glanced back." | | 7 | "His short curly dark brown" | | 8 | "The Saint Christopher medallion banged" | | 9 | "Quinn did not slow." | | 10 | "The rain slashed the narrow" | | 11 | "Tomás’s lips twisted." | | 12 | "He knew about the supernatural" | | 13 | "They turned onto a street" | | 14 | "Tomás sprinted past the entrance" | | 15 | "Quinn matched him step for" | | 16 | "Tomás cut left, then right," | | 17 | "His medallion clung to his" | | 18 | "The alley narrowed." | | 19 | "Graffiti sprawled across brick in" |
| | ratio | 0.907 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 8 | | matches | | 0 | "He knew about the supernatural origins that had swallowed Morris three years ago, the same origins that had cost Tomás his NHS license after he administered una…" | | 1 | "Quinn matched him step for step, her brown eyes unblinking, her military bearing holding her spine like an iron rod." | | 2 | "Tomás reached the mouth of the tunnel that led beneath Camden, a rusted stairwell descending into the dark." | | 3 | "Vendors lined the platform shadow, their stalls lit by green and violet lamps that cast sickly veins across the walls." | | 4 | "Enchanted goods sat behind glass and cloth: potions that glowed like infected wounds, talismans carved from teeth, banned alchemical substances that turned the …" | | 5 | "Tomás disappeared into the crowd, his short curly hair bobbing as he moved with the practiced ease of a man who had treated the clique’s wounds in this very pla…" | | 6 | "She watched Tomás reach a hidden alcove where a man sat on the ground, his face pale, his arm bleeding from a knife wound that mirrored the scar on Tomás’s fore…" | | 7 | "Tomás knelt beside him, pulling supplies from his coat, working with the quick precision of a former paramedic who had lost his license because he refused to le…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 14 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.5 | | effectiveRatio | 0.143 | |