| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1051 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 71.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1051 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "potential" | | 1 | "anticipating" | | 2 | "clandestine" | | 3 | "apprehension" | | 4 | "weight" |
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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 | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 62 | | 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 | 1051 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.35% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1048 | | uniqueNames | 12 | | maxNameDensity | 1.81 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Detective | 1 | | Harlow | 1 | | Quinn | 19 | | Raven | 1 | | Nest | 1 | | Tomás | 9 | | Herrera | 1 | | Camden | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Market" |
| | places | | | globalScore | 0.594 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared ahead and Tomás paused to retrieve the bone token from his possession" |
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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 | 1051 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 74.81% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 11 | | mean | 95.55 | | std | 39.35 | | cv | 0.412 | | sampleLengths | | 0 | 106 | | 1 | 86 | | 2 | 3 | | 3 | 91 | | 4 | 149 | | 5 | 127 | | 6 | 147 | | 7 | 81 | | 8 | 58 | | 9 | 96 | | 10 | 107 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 166 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1048 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.02194656488549618 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.012404580152671756 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 62 | | echoCount | 0 | | echoWords | (empty) | |
| 96.09% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 62 | | mean | 16.95 | | std | 6.61 | | cv | 0.39 | | sampleLengths | | 0 | 42 | | 1 | 22 | | 2 | 18 | | 3 | 24 | | 4 | 22 | | 5 | 38 | | 6 | 17 | | 7 | 9 | | 8 | 3 | | 9 | 13 | | 10 | 13 | | 11 | 17 | | 12 | 20 | | 13 | 17 | | 14 | 11 | | 15 | 16 | | 16 | 20 | | 17 | 15 | | 18 | 9 | | 19 | 18 | | 20 | 17 | | 21 | 13 | | 22 | 16 | | 23 | 12 | | 24 | 13 | | 25 | 11 | | 26 | 18 | | 27 | 12 | | 28 | 24 | | 29 | 11 | | 30 | 11 | | 31 | 12 | | 32 | 15 | | 33 | 13 | | 34 | 18 | | 35 | 24 | | 36 | 13 | | 37 | 15 | | 38 | 15 | | 39 | 7 | | 40 | 17 | | 41 | 23 | | 42 | 15 | | 43 | 13 | | 44 | 20 | | 45 | 16 | | 46 | 16 | | 47 | 16 | | 48 | 22 | | 49 | 14 |
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| 32.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.1935483870967742 | | totalSentences | 62 | | uniqueOpeners | 12 | |
| 54.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 61 | | matches | | 0 | "Further on the path crossed" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 61 | | matches | | 0 | "Her age of forty one" | | 1 | "Her profile matched the target" | | 2 | "She shouted her first order" | | 3 | "She regulated her breathing to" | | 4 | "He inserted the token and" | | 5 | "She held the door open" | | 6 | "She prepared mentally for what" |
| | ratio | 0.115 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 61 | | matches | | 0 | "The rain drenched London night" | | 1 | "The bar's walls held old" | | 2 | "Her age of forty one" | | 3 | "The rain fell in continuous" | | 4 | "Her profile matched the target" | | 5 | "The suspect had olive skin" | | 6 | "Quinn knew his background from" | | 7 | "She shouted her first order" | | 8 | "The command mixed with the" | | 9 | "Quinn adjusted to his path" | | 10 | "The alley contained bins full" | | 11 | "Quinn passed these without incident" | | 12 | "The chase exited the alley" | | 13 | "Quinn crossed with care to" | | 14 | "The run continued through residential" | | 15 | "The residential area featured narrow" | | 16 | "Tomás knocked one pot over" | | 17 | "Quinn avoided the spill by" | | 18 | "The action added variety to" | | 19 | "Tomás used these areas to" |
| | ratio | 0.934 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "The rain fell in continuous sheets that obscured vision beyond certain distances but Quinn ignored the limitation by focusing on the figure directly ahead." | | 1 | "The residential area featured narrow sidewalks in some spots where residents had placed potted plants that narrowed the path further." | | 2 | "The trees swayed slightly in the wind that accompanied the rainstorm creating additional movement in the branches that cast moving shadows on the path." | | 3 | "The market sold enchanted goods and banned substances to a clientele that included supernaturals." |
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| 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 | |