| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 115 | | tagDensity | 0.035 | | leniency | 0.07 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2350 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 93.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2350 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "silk" | | 1 | "restrained" | | 2 | "swept away" |
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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 | 192 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 192 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 303 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2350 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1700 | | uniqueNames | 8 | | maxNameDensity | 1.59 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Shaw" | | discoveredNames | | Harlow | 1 | | Quinn | 27 | | Camden | 1 | | Sergeant | 1 | | Nikhil | 1 | | Shaw | 23 | | Eva | 12 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Nikhil" | | 4 | "Shaw" | | 5 | "Eva" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.706 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 129 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.426 | | wordCount | 2350 | | matches | | 0 | "not with a blade but from within" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 303 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 209 | | mean | 11.24 | | std | 13.05 | | cv | 1.16 | | sampleLengths | | 0 | 20 | | 1 | 68 | | 2 | 15 | | 3 | 31 | | 4 | 27 | | 5 | 4 | | 6 | 10 | | 7 | 8 | | 8 | 4 | | 9 | 2 | | 10 | 63 | | 11 | 7 | | 12 | 6 | | 13 | 6 | | 14 | 7 | | 15 | 6 | | 16 | 35 | | 17 | 8 | | 18 | 3 | | 19 | 7 | | 20 | 4 | | 21 | 24 | | 22 | 6 | | 23 | 33 | | 24 | 4 | | 25 | 4 | | 26 | 6 | | 27 | 2 | | 28 | 9 | | 29 | 9 | | 30 | 16 | | 31 | 3 | | 32 | 3 | | 33 | 1 | | 34 | 2 | | 35 | 3 | | 36 | 54 | | 37 | 11 | | 38 | 1 | | 39 | 9 | | 40 | 5 | | 41 | 4 | | 42 | 4 | | 43 | 38 | | 44 | 4 | | 45 | 3 | | 46 | 2 | | 47 | 16 | | 48 | 7 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 192 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 277 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 303 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1707 | | adjectiveStacks | 2 | | stackExamples | | 0 | "pressed against clean tile." | | 1 | "open beneath milk-white films." |
| | adverbCount | 26 | | adverbRatio | 0.015231400117164616 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0017574692442882249 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 303 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 303 | | mean | 7.76 | | std | 5.64 | | cv | 0.728 | | sampleLengths | | 0 | 20 | | 1 | 29 | | 2 | 4 | | 3 | 12 | | 4 | 9 | | 5 | 14 | | 6 | 7 | | 7 | 3 | | 8 | 5 | | 9 | 11 | | 10 | 9 | | 11 | 11 | | 12 | 14 | | 13 | 7 | | 14 | 6 | | 15 | 4 | | 16 | 10 | | 17 | 8 | | 18 | 4 | | 19 | 2 | | 20 | 18 | | 21 | 13 | | 22 | 5 | | 23 | 2 | | 24 | 6 | | 25 | 11 | | 26 | 8 | | 27 | 7 | | 28 | 6 | | 29 | 6 | | 30 | 7 | | 31 | 6 | | 32 | 5 | | 33 | 24 | | 34 | 6 | | 35 | 8 | | 36 | 3 | | 37 | 7 | | 38 | 4 | | 39 | 8 | | 40 | 7 | | 41 | 9 | | 42 | 6 | | 43 | 14 | | 44 | 19 | | 45 | 4 | | 46 | 4 | | 47 | 6 | | 48 | 2 | | 49 | 9 |
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| 60.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.38283828382838286 | | totalSentences | 303 | | uniqueOpeners | 116 | |
| 55.87% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 179 | | matches | | 0 | "Only the pale plastic covers" | | 1 | "Only a black grit caught" | | 2 | "Instead, a dark stain covered" |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 179 | | matches | | 0 | "Their cables snaked over the" | | 1 | "His paper suit strained across" | | 2 | "He looked close to sixty." | | 3 | "His left hand rested palm-up" | | 4 | "His right fist gripped a" | | 5 | "She pointed towards the floor." | | 6 | "It had collected across the" | | 7 | "He swept one gloved hand" | | 8 | "He pointed at three stoppered" | | 9 | "His eyes stood open beneath" | | 10 | "Its bristles carried the same" | | 11 | "He produced an evidence bag." | | 12 | "He trudged towards the stairs." | | 13 | "She checked the station map" | | 14 | "He couldn’t have opened it" | | 15 | "She looked at his left" | | 16 | "They had found no prints" | | 17 | "She released the strap." | | 18 | "She stepped left." | | 19 | "She stepped right." |
| | ratio | 0.151 | |
| 60.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 143 | | totalSentences | 179 | | matches | | 0 | "Detective Harlow Quinn descended past" | | 1 | "Stalls crowded the disused platform" | | 2 | "Jars glimmered on shelves." | | 3 | "A butcher’s display case held" | | 4 | "The station held its breath." | | 5 | "Their cables snaked over the" | | 6 | "Detective Sergeant Nikhil Shaw waited" | | 7 | "His paper suit strained across" | | 8 | "A mask dangled beneath his" | | 9 | "Quinn checked the worn leather" | | 10 | "The stall beside her offered" | | 11 | "A male corpse sat behind" | | 12 | "He looked close to sixty." | | 13 | "His left hand rested palm-up" | | 14 | "His right fist gripped a" | | 15 | "A narrow knife protruded from" | | 16 | "Quinn ducked beneath the scene" | | 17 | "She pointed towards the floor." | | 18 | "Dust lay thick beneath the" | | 19 | "Shaw rocked back on his" |
| | ratio | 0.799 | |
| 27.93% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 179 | | matches | | 0 | "Whoever had cleaned it had" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 73 | | technicalSentenceCount | 3 | | matches | | 0 | "Stalls crowded the disused platform beneath Camden, their patched awnings stitched from funeral-black silk, yellow tarpaulin and one strip of material that ripp…" | | 1 | "The dark powder gathered thickest along the pillar’s base, forming half a ring before vanishing beneath an abandoned rug stall." | | 2 | "Black ash packed the grooves, mixed with pale grains that glittered under her lamp." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "he added (add)" | | 1 | "Shaw muttered (mutter)" |
| | dialogueSentences | 115 | | tagDensity | 0.035 | | leniency | 0.07 | | rawRatio | 0.5 | | effectiveRatio | 0.035 | |