| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1041 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "suddenly" | | 2 | "completely" |
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| 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) | |
| 66.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1041 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "glint" | | 1 | "synchronized" | | 2 | "weight" | | 3 | "shimmered" | | 4 | "scanned" | | 5 | "silence" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1041 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.27% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 836 | | uniqueNames | 12 | | maxNameDensity | 1.67 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Quinn | 3 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 8 | | Harlow | 14 | | Tube | 1 | | Camden | 1 | | Shadows | 1 | | Met | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tomás" | | 5 | "Harlow" | | 6 | "Shadows" |
| | places | | | globalScore | 0.663 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | 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 | 1041 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 28.14 | | std | 16.58 | | cv | 0.589 | | sampleLengths | | 0 | 70 | | 1 | 57 | | 2 | 42 | | 3 | 20 | | 4 | 45 | | 5 | 30 | | 6 | 23 | | 7 | 34 | | 8 | 37 | | 9 | 19 | | 10 | 39 | | 11 | 37 | | 12 | 33 | | 13 | 15 | | 14 | 26 | | 15 | 48 | | 16 | 20 | | 17 | 41 | | 18 | 26 | | 19 | 18 | | 20 | 46 | | 21 | 43 | | 22 | 8 | | 23 | 28 | | 24 | 37 | | 25 | 9 | | 26 | 2 | | 27 | 33 | | 28 | 15 | | 29 | 1 | | 30 | 6 | | 31 | 7 | | 32 | 59 | | 33 | 27 | | 34 | 17 | | 35 | 19 | | 36 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 107 | | 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 | 0 | | flaggedSentences | 0 | | totalSentences | 122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 836 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.023923444976076555 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005980861244019139 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 8.53 | | std | 4.74 | | cv | 0.556 | | sampleLengths | | 0 | 19 | | 1 | 15 | | 2 | 15 | | 3 | 5 | | 4 | 16 | | 5 | 11 | | 6 | 14 | | 7 | 22 | | 8 | 10 | | 9 | 9 | | 10 | 6 | | 11 | 12 | | 12 | 3 | | 13 | 4 | | 14 | 8 | | 15 | 14 | | 16 | 6 | | 17 | 2 | | 18 | 11 | | 19 | 14 | | 20 | 18 | | 21 | 13 | | 22 | 17 | | 23 | 5 | | 24 | 6 | | 25 | 12 | | 26 | 5 | | 27 | 8 | | 28 | 15 | | 29 | 4 | | 30 | 2 | | 31 | 7 | | 32 | 12 | | 33 | 3 | | 34 | 15 | | 35 | 6 | | 36 | 13 | | 37 | 15 | | 38 | 3 | | 39 | 13 | | 40 | 8 | | 41 | 7 | | 42 | 1 | | 43 | 1 | | 44 | 13 | | 45 | 15 | | 46 | 6 | | 47 | 6 | | 48 | 10 | | 49 | 11 |
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| 45.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.319672131147541 | | totalSentences | 122 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 93 | | matches | (empty) | | ratio | 0 | |
| 43.66% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 93 | | matches | | 0 | "Her boots hit puddles with" | | 1 | "She tracked the silhouette ahead." | | 2 | "He waited, shoulders hunched, the" | | 3 | "He wiped rain from his" | | 4 | "Her breath hammered against her" | | 5 | "She didn't shout." | | 6 | "She didn't need to." | | 7 | "Her voice cut through the" | | 8 | "His warm brown eyes held" | | 9 | "He checked the watch on" | | 10 | "He gestured toward the rusted" | | 11 | "Her boots splashed through a" | | 12 | "He pulled a bone token" | | 13 | "It gleamed with an unnatural" | | 14 | "He held it up." | | 15 | "She didn’t draw." | | 16 | "He tossed the token into" | | 17 | "It didn't fall." | | 18 | "It hovered for a heartbeat" | | 19 | "It smelled of dust and" |
| | ratio | 0.441 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 93 | | matches | | 0 | "Rain sheeted down the London" | | 1 | "Harlow Quinn ran with the" | | 2 | "Her boots hit puddles with" | | 3 | "She tracked the silhouette ahead." | | 4 | "A dark shape darting between" | | 5 | "Tomás Herrera slipped into the" | | 6 | "He waited, shoulders hunched, the" | | 7 | "The Saint Christopher medallion on" | | 8 | "He wiped rain from his" | | 9 | "Quinn skidded to a halt" | | 10 | "Her breath hammered against her" | | 11 | "The smell of ozone mixed" | | 12 | "She didn't shout." | | 13 | "She didn't need to." | | 14 | "The distance between them closed" | | 15 | "Her voice cut through the" | | 16 | "His warm brown eyes held" | | 17 | "He checked the watch on" | | 18 | "Both hands moved in unison," | | 19 | "He gestured toward the rusted" |
| | ratio | 0.925 | |
| 53.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 93 | | matches | | 0 | "If she followed him now," |
| | ratio | 0.011 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "A dark shape darting between the parked vans, moving with a confidence that bordered on arrogance." | | 1 | "The faint hum of electricity drifted up from below, a vibration that rattled Harlow’s teeth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.143 | | effectiveRatio | 0.071 | |