| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.324 | | leniency | 0.647 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1063 | | 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) | |
| 62.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1063 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "perfect" | | 1 | "complex" | | 2 | "silence" | | 3 | "unsettled" | | 4 | "trembled" | | 5 | "magnetic" | | 6 | "etched" | | 7 | "flickered" |
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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 | 59 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 59 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1063 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 757 | | uniqueNames | 10 | | maxNameDensity | 1.19 | | worstName | "Doyle" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Doyle" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Town | 1 | | Unofficially | 1 | | Rob | 1 | | Doyle | 9 | | Brick | 1 | | Lane | 1 | | Morris | 1 | | July | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Rob" | | 3 | "Doyle" | | 4 | "Morris" |
| | places | | 0 | "Town" | | 1 | "Brick" | | 2 | "Lane" | | 3 | "July" |
| | globalScore | 0.906 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1063 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 83 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 23.11 | | std | 16.98 | | cv | 0.735 | | sampleLengths | | 0 | 17 | | 1 | 49 | | 2 | 31 | | 3 | 6 | | 4 | 5 | | 5 | 54 | | 6 | 1 | | 7 | 18 | | 8 | 28 | | 9 | 3 | | 10 | 47 | | 11 | 40 | | 12 | 42 | | 13 | 25 | | 14 | 12 | | 15 | 40 | | 16 | 4 | | 17 | 4 | | 18 | 49 | | 19 | 14 | | 20 | 43 | | 21 | 43 | | 22 | 12 | | 23 | 3 | | 24 | 2 | | 25 | 4 | | 26 | 31 | | 27 | 21 | | 28 | 9 | | 29 | 43 | | 30 | 29 | | 31 | 27 | | 32 | 41 | | 33 | 31 | | 34 | 19 | | 35 | 43 | | 36 | 50 | | 37 | 13 | | 38 | 1 | | 39 | 1 | | 40 | 4 | | 41 | 4 | | 42 | 28 | | 43 | 28 | | 44 | 4 | | 45 | 40 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 59 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 131 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 83 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 289 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.020761245674740483 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.010380622837370242 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 12.81 | | std | 9.18 | | cv | 0.717 | | sampleLengths | | 0 | 17 | | 1 | 28 | | 2 | 7 | | 3 | 14 | | 4 | 15 | | 5 | 16 | | 6 | 6 | | 7 | 5 | | 8 | 13 | | 9 | 32 | | 10 | 9 | | 11 | 1 | | 12 | 18 | | 13 | 19 | | 14 | 3 | | 15 | 6 | | 16 | 3 | | 17 | 9 | | 18 | 38 | | 19 | 4 | | 20 | 15 | | 21 | 21 | | 22 | 11 | | 23 | 16 | | 24 | 15 | | 25 | 8 | | 26 | 17 | | 27 | 12 | | 28 | 7 | | 29 | 18 | | 30 | 8 | | 31 | 7 | | 32 | 4 | | 33 | 4 | | 34 | 20 | | 35 | 29 | | 36 | 3 | | 37 | 11 | | 38 | 23 | | 39 | 20 | | 40 | 2 | | 41 | 19 | | 42 | 22 | | 43 | 12 | | 44 | 3 | | 45 | 2 | | 46 | 4 | | 47 | 3 | | 48 | 28 | | 49 | 4 |
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| 89.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5903614457831325 | | totalSentences | 83 | | uniqueOpeners | 49 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Unofficially, someone had been using" |
| | ratio | 0.019 | |
| 79.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 54 | | matches | | 0 | "His eyes had turned the" | | 1 | "She crouched beside the body," | | 2 | "She pointed to the man's" | | 3 | "His shirt had torn open," | | 4 | "She'd seen a mark like" | | 5 | "She'd told herself it was" | | 6 | "She'd told herself a lot" | | 7 | "She'd learned that silence unsettled" | | 8 | "She swept her torch along" | | 9 | "She crouched, brushed away decades" | | 10 | "She lifted it by the" | | 11 | "He leaned in." | | 12 | "He had no answer for" | | 13 | "She'd seen sigils like these" | | 14 | "she said, holding it out" | | 15 | "She stopped near the tunnel's" | | 16 | "Her breath misted, once, and" | | 17 | "He must have heard something" | | 18 | "He reached for the radio" |
| | ratio | 0.352 | |
| 34.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 54 | | matches | | 0 | "The stench hit her first," | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "Camden Town station, closed since" | | 3 | "DS Rob Doyle crouched by" | | 4 | "Quinn crossed the platform, boots" | | 5 | "The victim lay sprawled against" | | 6 | "His eyes had turned the" | | 7 | "She crouched beside the body," | | 8 | "Someone had been here before" | | 9 | "Doyle nodded at the marks" | | 10 | "Quinn studied the symbols." | | 11 | "Something whiter, chalkier in texture" | | 12 | "She pointed to the man's" | | 13 | "His shirt had torn open," | | 14 | "A spiral, precise as a" | | 15 | "She'd seen a mark like" | | 16 | "She'd told herself it was" | | 17 | "She'd told herself a lot" | | 18 | "Doyle stood, joints cracking, and" | | 19 | "Quinn didn't answer." |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 44.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn ducked under the crime scene tape strung across the mouth of a disused escalator, her torch beam cutting through dust that hadn't settled…" | | 1 | "The victim lay sprawled against the curved tunnel wall, arms flung wide, mouth open in a scream that had gone unheard for, judging by the state of him, at least…" | | 2 | "Cold radiated from it, a cold that had no business existing this far underground in July." | | 3 | "The scraping came again, closer, accompanied by a low sound like breath drawn through something that had once been a throat and had since become considerably le…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.088 | | leniency | 0.176 | | rawRatio | 0 | | effectiveRatio | 0 | |