| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "Dr Chen said quietly [quietly]" |
| | dialogueSentences | 47 | | tagDensity | 0.468 | | leniency | 0.936 | | rawRatio | 0.045 | | effectiveRatio | 0.043 | |
| 63.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1218 | | totalAiIsmAdverbs | 9 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 3 |
| | 4 | | | 5 | |
| | highlights | | 0 | "slightly" | | 1 | "gently" | | 2 | "carefully" | | 3 | "deliberately" | | 4 | "lazily" | | 5 | "completely" |
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| 40.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | 0 | "Chen" | | 1 | "Sarah" | | 2 | "Thompson" |
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| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 63.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1218 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "gloom" | | 2 | "scanning" | | 3 | "pristine" | | 4 | "etched" | | 5 | "magnetic" | | 6 | "footsteps" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1208 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 821 | | uniqueNames | 13 | | maxNameDensity | 2.19 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Camden | 1 | | Northern | 1 | | Line | 1 | | Thompson | 9 | | Aldrich | 6 | | Scratch | 1 | | Morrison | 2 | | Said | 1 | | Tuesday | 1 | | Dr | 3 | | Chen | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Thompson" | | 4 | "Aldrich" | | 5 | "Scratch" | | 6 | "Morrison" | | 7 | "Dr" | | 8 | "Chen" |
| | places | (empty) | | globalScore | 0.404 | | windowScore | 0.333 | |
| 39.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like an expensive suit" | | 1 | "quite make out in the harsh torch light" | | 2 | "looked like a burn, but the edges were to" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.828 | | wordCount | 1208 | | matches | | 0 | "not the expected mustiness of a forgotten tunnel, but something sharper" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 24.16 | | std | 13.58 | | cv | 0.562 | | sampleLengths | | 0 | 48 | | 1 | 38 | | 2 | 43 | | 3 | 9 | | 4 | 25 | | 5 | 25 | | 6 | 46 | | 7 | 25 | | 8 | 51 | | 9 | 12 | | 10 | 42 | | 11 | 5 | | 12 | 8 | | 13 | 48 | | 14 | 14 | | 15 | 13 | | 16 | 41 | | 17 | 30 | | 18 | 21 | | 19 | 11 | | 20 | 29 | | 21 | 4 | | 22 | 23 | | 23 | 24 | | 24 | 26 | | 25 | 4 | | 26 | 33 | | 27 | 3 | | 28 | 23 | | 29 | 39 | | 30 | 27 | | 31 | 6 | | 32 | 26 | | 33 | 16 | | 34 | 41 | | 35 | 7 | | 36 | 28 | | 37 | 21 | | 38 | 27 | | 39 | 18 | | 40 | 2 | | 41 | 28 | | 42 | 44 | | 43 | 30 | | 44 | 21 | | 45 | 30 | | 46 | 10 | | 47 | 34 | | 48 | 5 | | 49 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 118 | | ratio | 0.051 | | matches | | 0 | "The smell hit her first—not the expected mustiness of a forgotten tunnel, but something sharper." | | 1 | "But instead of the usual detritus of abandoned stations—graffiti, empty bottles, makeshift camps—the space looked swept clean." | | 2 | "She checked her worn leather watch—3:47 AM." | | 3 | "She checked his arms through the suit jacket—nothing there either." | | 4 | "While the floor around the body showed recent disturbance, the corners remained pristine—except for one spot near the electrical panels where the dust had been deliberately swept in a rough circle about three feet in diameter." | | 5 | "\"In which case, where's the other party?\" Quinn's torch beam caught something else—scratches on the doorframe, fresh ones." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 415 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.03855421686746988 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.007228915662650603 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 10.24 | | std | 5.93 | | cv | 0.579 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 15 | | 3 | 1 | | 4 | 12 | | 5 | 19 | | 6 | 7 | | 7 | 9 | | 8 | 15 | | 9 | 17 | | 10 | 2 | | 11 | 9 | | 12 | 12 | | 13 | 13 | | 14 | 10 | | 15 | 7 | | 16 | 8 | | 17 | 8 | | 18 | 11 | | 19 | 22 | | 20 | 3 | | 21 | 2 | | 22 | 6 | | 23 | 19 | | 24 | 11 | | 25 | 8 | | 26 | 17 | | 27 | 7 | | 28 | 8 | | 29 | 3 | | 30 | 9 | | 31 | 10 | | 32 | 6 | | 33 | 10 | | 34 | 6 | | 35 | 10 | | 36 | 5 | | 37 | 8 | | 38 | 7 | | 39 | 5 | | 40 | 36 | | 41 | 14 | | 42 | 3 | | 43 | 10 | | 44 | 12 | | 45 | 21 | | 46 | 8 | | 47 | 3 | | 48 | 13 | | 49 | 7 |
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| 82.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5084745762711864 | | totalSentences | 118 | | uniqueOpeners | 60 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 83 | | matches | | 0 | "Her torch beam cut through" | | 1 | "She swept her torch across" | | 2 | "She pushed through, and the" | | 3 | "His mouth hung slightly open," | | 4 | "She checked her worn leather" | | 5 | "She checked his arms through" | | 6 | "She stood, scanning the room" | | 7 | "She directed her beam at" | | 8 | "She'd need proper lighting to" | | 9 | "Her phone buzzed." | | 10 | "She joined him." | | 11 | "She tensed, hand moving instinctively" | | 12 | "She took in the scene" | | 13 | "She lifted Aldrich's left arm," | | 14 | "It looked like a burn," | | 15 | "She photographed the scratches with" | | 16 | "They formed a repetitive pattern," | | 17 | "She'd pulled back Aldrich's eyelids" | | 18 | "his voice crackled through the" |
| | ratio | 0.229 | |
| 56.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 83 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "Her torch beam cut through" | | 2 | "The smell hit her first—not" | | 3 | "PC Thompson's voice echoed off" | | 4 | "The young constable stood twenty" | | 5 | "Quinn's boots crunched on broken" | | 6 | "The tunnel opened into what" | | 7 | "She swept her torch across" | | 8 | "Thompson pointed to a recessed" | | 9 | "Quinn noted the lack of" | | 10 | "Someone had been using this" | | 11 | "She pushed through, and the" | | 12 | "The control room stretched maybe" | | 13 | "Thompson consulted his notebook" | | 14 | "Quinn crouched beside the body," | | 15 | "Aldrich's eyes stared at the" | | 16 | "His mouth hung slightly open," | | 17 | "She checked her worn leather" | | 18 | "The body showed early signs" | | 19 | "Quinn pulled on latex gloves" |
| | ratio | 0.807 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | 0 | "While the floor around the" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 86.17% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "Thompson suggested (suggest)" | | 1 | "she ordered (order)" | | 2 | "Thompson continued (continue)" |
| | dialogueSentences | 47 | | tagDensity | 0.106 | | leniency | 0.213 | | rawRatio | 0.6 | | effectiveRatio | 0.128 | |