| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.15 | | leniency | 0.3 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1634 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 96.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1634 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 1 | | narrationSentences | 89 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 89 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 174 | | 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 | 1634 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 86 | | wordCount | 1015 | | uniqueNames | 11 | | maxNameDensity | 3.35 | | worstName | "Aurora" | | maxWindowNameDensity | 6 | | worstWindowName | "Nia" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Silas | 9 | | Raven | 1 | | Nest | 1 | | Cardiff | 2 | | Hughes | 1 | | Aurora | 34 | | Nia | 34 | | London | 1 | | Evan | 1 |
| | persons | | 0 | "Empress" | | 1 | "Silas" | | 2 | "Raven" | | 3 | "Hughes" | | 4 | "Aurora" | | 5 | "Nia" | | 6 | "Evan" |
| | places | | 0 | "Golden" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1634 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 174 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 112 | | mean | 14.59 | | std | 17.09 | | cv | 1.171 | | sampleLengths | | 0 | 47 | | 1 | 6 | | 2 | 9 | | 3 | 4 | | 4 | 49 | | 5 | 2 | | 6 | 36 | | 7 | 1 | | 8 | 71 | | 9 | 24 | | 10 | 6 | | 11 | 5 | | 12 | 23 | | 13 | 5 | | 14 | 3 | | 15 | 2 | | 16 | 15 | | 17 | 3 | | 18 | 8 | | 19 | 10 | | 20 | 10 | | 21 | 5 | | 22 | 50 | | 23 | 10 | | 24 | 1 | | 25 | 11 | | 26 | 4 | | 27 | 4 | | 28 | 47 | | 29 | 4 | | 30 | 8 | | 31 | 18 | | 32 | 4 | | 33 | 4 | | 34 | 15 | | 35 | 2 | | 36 | 1 | | 37 | 76 | | 38 | 11 | | 39 | 4 | | 40 | 6 | | 41 | 6 | | 42 | 33 | | 43 | 10 | | 44 | 8 | | 45 | 1 | | 46 | 30 | | 47 | 4 | | 48 | 6 | | 49 | 26 |
| |
| 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 | 166 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 174 | | ratio | 0.006 | | matches | | 0 | "She had read those messages again, Aurora thought; she knew what order they had come in." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1017 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.02064896755162242 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 174 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 174 | | mean | 9.39 | | std | 7.04 | | cv | 0.75 | | sampleLengths | | 0 | 18 | | 1 | 13 | | 2 | 16 | | 3 | 6 | | 4 | 3 | | 5 | 6 | | 6 | 4 | | 7 | 8 | | 8 | 21 | | 9 | 20 | | 10 | 2 | | 11 | 9 | | 12 | 27 | | 13 | 1 | | 14 | 3 | | 15 | 20 | | 16 | 21 | | 17 | 27 | | 18 | 10 | | 19 | 14 | | 20 | 6 | | 21 | 5 | | 22 | 2 | | 23 | 7 | | 24 | 14 | | 25 | 5 | | 26 | 3 | | 27 | 2 | | 28 | 8 | | 29 | 7 | | 30 | 3 | | 31 | 8 | | 32 | 7 | | 33 | 3 | | 34 | 6 | | 35 | 4 | | 36 | 5 | | 37 | 13 | | 38 | 9 | | 39 | 17 | | 40 | 11 | | 41 | 10 | | 42 | 1 | | 43 | 11 | | 44 | 4 | | 45 | 4 | | 46 | 8 | | 47 | 20 | | 48 | 19 | | 49 | 4 |
| |
| 43.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.2988505747126437 | | totalSentences | 174 | | uniqueOpeners | 52 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 86 | | matches | | 0 | "Afterwards they had dried their" | | 1 | "Once he’d gone, Nia picked" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 86 | | matches | | 0 | "She lifted the bag onto" | | 1 | "He took a fork from" | | 2 | "It had once filled the" | | 3 | "Her hair, once long and" | | 4 | "She wore a charcoal suit" | | 5 | "His silver ring clicked against" | | 6 | "Its top edge bore a" | | 7 | "She had made Aurora hold" | | 8 | "It caught on the edge" | | 9 | "She had dusted that frame" | | 10 | "His bad leg made him" | | 11 | "she told Aurora" | | 12 | "She had read those messages" | | 13 | "he told them, nodding at" | | 14 | "He moved off towards the" | | 15 | "She turned towards Aurora" | | 16 | "She’d put the call on" | | 17 | "Its damp corner clung to" | | 18 | "She folded it along an" | | 19 | "She drew the wet glass" |
| | ratio | 0.233 | |
| 6.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 86 | | matches | | 0 | "Aurora came down to the" | | 1 | "The handles had cut a" | | 2 | "She lifted the bag onto" | | 3 | "Silas looked inside." | | 4 | "He took a fork from" | | 5 | "Rain struck the front window" | | 6 | "Aurora had just opened her" | | 7 | "Aurora knew the voice before" | | 8 | "It had once filled the" | | 9 | "Nia Hughes stood." | | 10 | "Her hair, once long and" | | 11 | "She wore a charcoal suit" | | 12 | "Aurora remembered her with a" | | 13 | "Nia told her" | | 14 | "Nia’s glass pressed cold against" | | 15 | "Silas set a clean fork" | | 16 | "His silver ring clicked against" | | 17 | "Silas told Nia" | | 18 | "Nia raised her eyebrows at" | | 19 | "Silas took the bag of" |
| | ratio | 0.907 | |
| 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 | 42 | | technicalSentenceCount | 1 | | matches | | 0 | "It had once filled the back row of a lecture theatre in Cardiff, demanding an answer from a visiting barrister who had planned to take no questions." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 100 | | tagDensity | 0.12 | | leniency | 0.24 | | rawRatio | 0 | | effectiveRatio | 0 | |