| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 6 | | adverbTags | | 0 | "a voice said suddenly [suddenly]" | | 1 | "She swung back [back]" | | 2 | "The figure pushed back [back]" | | 3 | "Quinn said blandly [blandly]" | | 4 | "Rafe's eyes glittered dangerously [dangerously]" | | 5 | "Tommy shifted subtly [subtly]" |
| | dialogueSentences | 38 | | tagDensity | 0.632 | | leniency | 1 | | rawRatio | 0.25 | | effectiveRatio | 0.25 | |
| 86.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1140 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slowly" | | 1 | "suddenly" | | 2 | "carefully" |
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| 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) | |
| 42.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1140 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "raced" | | 1 | "echoed" | | 2 | "scanning" | | 3 | "glinting" | | 4 | "vibrated" | | 5 | "flicked" | | 6 | "calculating" | | 7 | "scanned" | | 8 | "pulse" | | 9 | "footsteps" | | 10 | "anticipation" |
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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 | 1 | | narrationSentences | 89 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 1136 | | ratio | 0.004 | | matches | | 0 | "LL electronically, her" | | 1 | "tsked" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 35.45% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 873 | | uniqueNames | 12 | | maxNameDensity | 2.29 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 20 | | Keep | 1 | | Tube | 1 | | Spanish | 1 | | Saint | 1 | | Christopher | 1 | | Tommy | 10 | | Chinese | 1 | | Rafe | 1 | | Putting | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Keep" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tommy" | | 6 | "Rafe" |
| | places | | | globalScore | 0.355 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared ahead casting long shadows" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 25.24 | | std | 13.48 | | cv | 0.534 | | sampleLengths | | 0 | 31 | | 1 | 10 | | 2 | 45 | | 3 | 37 | | 4 | 33 | | 5 | 46 | | 6 | 46 | | 7 | 35 | | 8 | 63 | | 9 | 10 | | 10 | 48 | | 11 | 15 | | 12 | 13 | | 13 | 4 | | 14 | 34 | | 15 | 35 | | 16 | 19 | | 17 | 21 | | 18 | 29 | | 19 | 20 | | 20 | 22 | | 21 | 1 | | 22 | 38 | | 23 | 15 | | 24 | 24 | | 25 | 42 | | 26 | 14 | | 27 | 22 | | 28 | 38 | | 29 | 16 | | 30 | 20 | | 31 | 16 | | 32 | 13 | | 33 | 11 | | 34 | 35 | | 35 | 43 | | 36 | 27 | | 37 | 21 | | 38 | 14 | | 39 | 18 | | 40 | 10 | | 41 | 12 | | 42 | 25 | | 43 | 32 | | 44 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 89 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 161 | | matches | (empty) | |
| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "There - a shape disappearing into a warehouse door." | | 1 | "Stacked crates and caged animals filled the space - the market's storeroom, or its zoo." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 320 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.01875 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.92 | | std | 5.92 | | cv | 0.542 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 10 | | 3 | 13 | | 4 | 18 | | 5 | 7 | | 6 | 7 | | 7 | 13 | | 8 | 9 | | 9 | 11 | | 10 | 4 | | 11 | 15 | | 12 | 13 | | 13 | 2 | | 14 | 3 | | 15 | 19 | | 16 | 12 | | 17 | 9 | | 18 | 6 | | 19 | 10 | | 20 | 7 | | 21 | 16 | | 22 | 13 | | 23 | 8 | | 24 | 9 | | 25 | 10 | | 26 | 8 | | 27 | 14 | | 28 | 5 | | 29 | 22 | | 30 | 22 | | 31 | 10 | | 32 | 19 | | 33 | 22 | | 34 | 7 | | 35 | 15 | | 36 | 4 | | 37 | 9 | | 38 | 4 | | 39 | 22 | | 40 | 12 | | 41 | 3 | | 42 | 4 | | 43 | 9 | | 44 | 9 | | 45 | 10 | | 46 | 8 | | 47 | 11 | | 48 | 6 | | 49 | 15 |
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| 89.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5480769230769231 | | totalSentences | 104 | | uniqueOpeners | 57 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 85.12% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 86 | | matches | | 0 | "Her command echoed off the" | | 1 | "Her jacket caught on a" | | 2 | "She yanked it free with" | | 3 | "She dropped down into a" | | 4 | "She sprinted after it, pushing" | | 5 | "Her hand brushed the" | | 6 | "Its surface vibrated against her" | | 7 | "She proceeded slowly, hugging the" | | 8 | "She passed a booth draped" | | 9 | "His gaze flicked to her" | | 10 | "She kept her tone light" | | 11 | "He nodded to an alcove" | | 12 | "he said, extending a hand" | | 13 | "she shook it, not giving" | | 14 | "She described her suspect" | | 15 | "He guided her through the" | | 16 | "She nodded, glancing at Quinn." | | 17 | "She held his gaze meaningfully" | | 18 | "They approached a stall draped" | | 19 | "Her words hung in the" |
| | ratio | 0.337 | |
| 35.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 86 | | matches | | 0 | "Detective Harlow Quinn's shoes slapped" | | 1 | "The man darted down a" | | 2 | "Her command echoed off the" | | 3 | "The suspect vaulted over a" | | 4 | "Quinn grimaced and grabbed the" | | 5 | "Her jacket caught on a" | | 6 | "She yanked it free with" | | 7 | "She dropped down into a" | | 8 | "There - a shape disappearing" | | 9 | "She sprinted after it, pushing" | | 10 | "Metal stairs switchbacked down." | | 11 | "Instinct prickled the back of" | | 12 | "Her hand brushed the" | | 13 | "The stairs ended in a" | | 14 | "A bone token sat at" | | 15 | "Quinn hesitated only a moment" | | 16 | "Its surface vibrated against her" | | 17 | "She proceeded slowly, hugging the" | | 18 | "A murmur of voices grew" | | 19 | "Quinn peered around a final" |
| | ratio | 0.849 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 3 | | matches | | 0 | "She kept, her_wall going up" | | 1 | "she shook, not giving her surname" | | 2 | "They approached, a fluttering laugh escaping from between the folds" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "She described (describe)" |
| | dialogueSentences | 38 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0.167 | | effectiveRatio | 0.053 | |