| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 77 | | tagDensity | 0.221 | | leniency | 0.442 | | rawRatio | 0.059 | | effectiveRatio | 0.026 | |
| 95.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2042 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 75.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2042 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "echoed" | | 1 | "footfall" | | 2 | "weight" | | 3 | "silence" | | 4 | "flicked" | | 5 | "comfortable" | | 6 | "standard" | | 7 | "etched" | | 8 | "trembled" | | 9 | "traced" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 157 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 157 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 217 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2042 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1443 | | uniqueNames | 5 | | maxNameDensity | 1.73 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Patel" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 25 | | Tube | 1 | | Patel | 22 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Patel" |
| | places | (empty) | | globalScore | 0.634 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.49 | | wordCount | 2042 | | matches | | 0 | "not the pool but the thinner smear toward the stairwell" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 217 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 103 | | mean | 19.83 | | std | 17 | | cv | 0.858 | | sampleLengths | | 0 | 33 | | 1 | 16 | | 2 | 67 | | 3 | 14 | | 4 | 3 | | 5 | 33 | | 6 | 3 | | 7 | 75 | | 8 | 5 | | 9 | 43 | | 10 | 43 | | 11 | 9 | | 12 | 13 | | 13 | 40 | | 14 | 10 | | 15 | 63 | | 16 | 32 | | 17 | 8 | | 18 | 7 | | 19 | 8 | | 20 | 27 | | 21 | 9 | | 22 | 6 | | 23 | 9 | | 24 | 22 | | 25 | 3 | | 26 | 33 | | 27 | 33 | | 28 | 4 | | 29 | 16 | | 30 | 2 | | 31 | 6 | | 32 | 14 | | 33 | 19 | | 34 | 27 | | 35 | 54 | | 36 | 13 | | 37 | 3 | | 38 | 8 | | 39 | 3 | | 40 | 30 | | 41 | 18 | | 42 | 23 | | 43 | 29 | | 44 | 25 | | 45 | 26 | | 46 | 11 | | 47 | 3 | | 48 | 4 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 157 | | matches | | 0 | "been dragged" | | 1 | "been fitted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 241 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 217 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1446 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.0338865836791148 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004149377593360996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 217 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 217 | | mean | 9.41 | | std | 8.22 | | cv | 0.874 | | sampleLengths | | 0 | 33 | | 1 | 16 | | 2 | 12 | | 3 | 33 | | 4 | 8 | | 5 | 6 | | 6 | 8 | | 7 | 14 | | 8 | 3 | | 9 | 3 | | 10 | 15 | | 11 | 15 | | 12 | 3 | | 13 | 15 | | 14 | 6 | | 15 | 31 | | 16 | 8 | | 17 | 15 | | 18 | 5 | | 19 | 8 | | 20 | 7 | | 21 | 10 | | 22 | 18 | | 23 | 21 | | 24 | 22 | | 25 | 3 | | 26 | 6 | | 27 | 9 | | 28 | 4 | | 29 | 9 | | 30 | 31 | | 31 | 10 | | 32 | 26 | | 33 | 16 | | 34 | 14 | | 35 | 4 | | 36 | 3 | | 37 | 12 | | 38 | 20 | | 39 | 8 | | 40 | 2 | | 41 | 5 | | 42 | 6 | | 43 | 2 | | 44 | 4 | | 45 | 23 | | 46 | 6 | | 47 | 3 | | 48 | 6 | | 49 | 4 |
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| 51.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.33640552995391704 | | totalSentences | 217 | | uniqueOpeners | 73 | |
| 24.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 138 | | matches | | 0 | "Somewhere in the dark beyond" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 138 | | matches | | 0 | "Its cone cut across cracked" | | 1 | "He stopped when she kept" | | 2 | "Her leather watch rested against" | | 3 | "He was all sharp elbows" | | 4 | "He glanced up." | | 5 | "She looked past him at" | | 6 | "He shifted his weight and" | | 7 | "His coat had ridden up" | | 8 | "His shoes were polished." | | 9 | "His cuffs clean." | | 10 | "She crouched at the tape" | | 11 | "She studied the wall, the" | | 12 | "She crouched and picked it" | | 13 | "She turned and pointed at" | | 14 | "She crouched by the boots" | | 15 | "She looked up" | | 16 | "She turned it once." | | 17 | "She pointed to the casing." | | 18 | "She crossed to him and" | | 19 | "He had already stopped moving." |
| | ratio | 0.246 | |
| 3.48% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 126 | | totalSentences | 138 | | matches | | 0 | "The tunnel breathed cold damp" | | 1 | "Detective Harlow Quinn paused at" | | 2 | "A single sodium work light" | | 3 | "Its cone cut across cracked" | | 4 | "Uniforms moved in careful, clipped" | | 5 | "Boots rang against the station" | | 6 | "Voices echoed and broke apart" | | 7 | "Quinn stepped under the tape" | | 8 | "The constable straightened." | | 9 | "He stopped when she kept" | | 10 | "Her leather watch rested against" | | 11 | "The steps shivered under each" | | 12 | "Someone had painted over the" | | 13 | "Someone else had scratched symbols" | | 14 | "Quinn’s gaze snagged on them." | | 15 | "A bent rail chair sat" | | 16 | "A thermos lay beside it," | | 17 | "A half-eaten bag of crisps" | | 18 | "None of it belonged to" | | 19 | "Patel stood by the edge" |
| | ratio | 0.913 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 138 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 2 | | matches | | 0 | "He was all sharp elbows and too much coffee, the kind of detective who tried to fill dead air before it settled." | | 1 | "The corpse lay on its back, one arm bent awkwardly beneath him, the other flung wide as if he had reached for something out of frame." |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "She crouched, not touching" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "he guessed (guess)" | | 1 | "Quinn muttered (mutter)" |
| | dialogueSentences | 77 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.286 | | effectiveRatio | 0.052 | |