| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1097 | | 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) | |
| 72.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1097 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "measured" | | 1 | "chill" | | 2 | "velvet" | | 3 | "glinting" | | 4 | "flicked" | | 5 | "perfect" |
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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 | 65 | | matches | (empty) | |
| 76.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1095 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 702 | | uniqueNames | 11 | | maxNameDensity | 2.14 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 15 | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 1 | | Fowler | 10 | | Eva | 7 | | Kowalski | 1 | | Garrow | 3 | | Compass | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Fowler" | | 5 | "Eva" | | 6 | "Kowalski" | | 7 | "Garrow" | | 8 | "Compass" |
| | places | (empty) | | globalScore | 0.432 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | 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 | 1095 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 20.28 | | std | 23.04 | | cv | 1.136 | | sampleLengths | | 0 | 100 | | 1 | 62 | | 2 | 6 | | 3 | 1 | | 4 | 5 | | 5 | 3 | | 6 | 7 | | 7 | 23 | | 8 | 83 | | 9 | 7 | | 10 | 24 | | 11 | 57 | | 12 | 9 | | 13 | 4 | | 14 | 9 | | 15 | 8 | | 16 | 6 | | 17 | 39 | | 18 | 20 | | 19 | 3 | | 20 | 7 | | 21 | 10 | | 22 | 45 | | 23 | 5 | | 24 | 29 | | 25 | 2 | | 26 | 7 | | 27 | 53 | | 28 | 3 | | 29 | 9 | | 30 | 7 | | 31 | 82 | | 32 | 60 | | 33 | 16 | | 34 | 28 | | 35 | 5 | | 36 | 17 | | 37 | 3 | | 38 | 7 | | 39 | 40 | | 40 | 5 | | 41 | 11 | | 42 | 7 | | 43 | 18 | | 44 | 5 | | 45 | 25 | | 46 | 4 | | 47 | 2 | | 48 | 6 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 114 | | matches | (empty) | |
| 47.62% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 90 | | ratio | 0.033 | | matches | | 0 | "No scuffed footprints marked the dust at the base—only a swirl in the dirt, a perfect circle as if something had rotated in place." | | 1 | "The killer hadn't fled down the tunnel; the killer had stepped out of the wall, strangled Garrow, staged the robbery with the pipe and the smashed case, then stepped back through." | | 2 | "The pieces didn't fit Fowler's thief, and they didn't fit Eva's ghost story either—not yet." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 708 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.01694915254237288 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002824858757062147 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.17 | | std | 8.75 | | cv | 0.719 | | sampleLengths | | 0 | 13 | | 1 | 25 | | 2 | 15 | | 3 | 23 | | 4 | 24 | | 5 | 6 | | 6 | 13 | | 7 | 9 | | 8 | 23 | | 9 | 11 | | 10 | 6 | | 11 | 1 | | 12 | 5 | | 13 | 3 | | 14 | 7 | | 15 | 23 | | 16 | 5 | | 17 | 18 | | 18 | 12 | | 19 | 8 | | 20 | 19 | | 21 | 9 | | 22 | 12 | | 23 | 7 | | 24 | 24 | | 25 | 7 | | 26 | 22 | | 27 | 3 | | 28 | 16 | | 29 | 9 | | 30 | 9 | | 31 | 4 | | 32 | 9 | | 33 | 8 | | 34 | 6 | | 35 | 9 | | 36 | 13 | | 37 | 9 | | 38 | 8 | | 39 | 20 | | 40 | 3 | | 41 | 7 | | 42 | 2 | | 43 | 8 | | 44 | 45 | | 45 | 5 | | 46 | 29 | | 47 | 2 | | 48 | 7 | | 49 | 9 |
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| 58.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.36666666666666664 | | totalSentences | 90 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 63 | | matches | | 0 | "She ducked under the police" | | 1 | "Her green eyes tracked Quinn" | | 2 | "She leaned closer." | | 3 | "She probed the scalp wound" | | 4 | "She swept the beam over" | | 5 | "She flipped it open." | | 6 | "She stopped at the bricked" | | 7 | "It ran from Garrow's stall" | | 8 | "She turned and pointed to" | | 9 | "She looked at the drop" |
| | ratio | 0.159 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 63 | | matches | | 0 | "The stairs down to the" | | 1 | "Detective Harlow Quinn descended them" | | 2 | "The black market moved locations" | | 3 | "She ducked under the police" | | 4 | "DC Fowler stood by a" | | 5 | "Her green eyes tracked Quinn" | | 6 | "Fowler's breath misted in the" | | 7 | "Quinn's gaze flicked to Eva." | | 8 | "Eva's fingers tightened on the" | | 9 | "Quinn crouched by the body." | | 10 | "The dead man lay on" | | 11 | "Tobias Garrow, fifty-two, a fence" | | 12 | "Blood pooled beneath his head," | | 13 | "A length of lead pipe" | | 14 | "Shards of ceramic from broken" | | 15 | "The stall reeked of spilt" | | 16 | "Fowler jerked a thumb at" | | 17 | "Quinn kept her torch on" | | 18 | "The platform's dust lay thick" | | 19 | "She leaned closer." |
| | ratio | 0.857 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |