| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 30 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1833 | | totalAiIsmAdverbs | 1 | | 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) | |
| 72.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1833 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "pulse" | | 1 | "silence" | | 2 | "warmth" | | 3 | "echo" | | 4 | "lilt" | | 5 | "throbbed" | | 6 | "stomach" | | 7 | "familiar" | | 8 | "furrowed" | | 9 | "pulsed" |
| |
| 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 | 241 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 241 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 266 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1833 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1705 | | uniqueNames | 11 | | maxNameDensity | 1.52 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 2 | | Park | 2 | | Heartstone | 5 | | Silas | 1 | | Eva | 1 | | London | 1 | | Aurora | 26 | | Cardiff | 1 | | Black | 3 | | Click | 7 | | Click-click | 3 |
| | persons | | 0 | "Heartstone" | | 1 | "Silas" | | 2 | "Eva" | | 3 | "Aurora" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Black" |
| | globalScore | 0.738 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 129 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beside it, then a third, each one emerging from the shadows between leaves" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1833 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 266 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 172 | | mean | 10.66 | | std | 10.83 | | cv | 1.016 | | sampleLengths | | 0 | 27 | | 1 | 9 | | 2 | 39 | | 3 | 8 | | 4 | 29 | | 5 | 10 | | 6 | 26 | | 7 | 2 | | 8 | 49 | | 9 | 18 | | 10 | 2 | | 11 | 6 | | 12 | 7 | | 13 | 13 | | 14 | 28 | | 15 | 7 | | 16 | 39 | | 17 | 6 | | 18 | 6 | | 19 | 6 | | 20 | 23 | | 21 | 5 | | 22 | 8 | | 23 | 1 | | 24 | 1 | | 25 | 1 | | 26 | 5 | | 27 | 57 | | 28 | 8 | | 29 | 2 | | 30 | 3 | | 31 | 24 | | 32 | 3 | | 33 | 4 | | 34 | 11 | | 35 | 3 | | 36 | 33 | | 37 | 9 | | 38 | 12 | | 39 | 4 | | 40 | 5 | | 41 | 42 | | 42 | 4 | | 43 | 4 | | 44 | 1 | | 45 | 1 | | 46 | 1 | | 47 | 8 | | 48 | 16 | | 49 | 36 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 241 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 279 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 266 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1711 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 54 | | adverbRatio | 0.031560490940970194 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.004675628287551139 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 266 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 266 | | mean | 6.89 | | std | 4.83 | | cv | 0.7 | | sampleLengths | | 0 | 27 | | 1 | 9 | | 2 | 13 | | 3 | 2 | | 4 | 1 | | 5 | 23 | | 6 | 8 | | 7 | 12 | | 8 | 10 | | 9 | 7 | | 10 | 3 | | 11 | 7 | | 12 | 18 | | 13 | 8 | | 14 | 2 | | 15 | 18 | | 16 | 10 | | 17 | 7 | | 18 | 14 | | 19 | 10 | | 20 | 8 | | 21 | 2 | | 22 | 6 | | 23 | 7 | | 24 | 2 | | 25 | 2 | | 26 | 9 | | 27 | 5 | | 28 | 3 | | 29 | 20 | | 30 | 7 | | 31 | 2 | | 32 | 2 | | 33 | 16 | | 34 | 6 | | 35 | 13 | | 36 | 6 | | 37 | 6 | | 38 | 6 | | 39 | 5 | | 40 | 18 | | 41 | 5 | | 42 | 4 | | 43 | 4 | | 44 | 1 | | 45 | 1 | | 46 | 1 | | 47 | 5 | | 48 | 12 | | 49 | 4 |
| |
| 41.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.2744360902255639 | | totalSentences | 266 | | uniqueOpeners | 73 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 200 | | matches | | 0 | "More like a heartbeat visible" | | 1 | "Instead, she had climbed on" | | 2 | "Then another flower followed it." | | 3 | "Then a dozen." | | 4 | "Then it unfolded one joint." | | 5 | "Then her reflection smiled." | | 6 | "Only one person had called" | | 7 | "Then the Heartstone pulsed beneath" |
| | ratio | 0.04 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 200 | | matches | | 0 | "She had followed the map" | | 1 | "She had put the phone" | | 2 | "Its crimson centre held a" | | 3 | "It never touched the trees" | | 4 | "It grew in long vertical" | | 5 | "Her map had marked an" | | 6 | "She had considered calling Silas." | | 7 | "She sucked in a breath" | | 8 | "It had a rhythm." | | 9 | "Their branches knit overhead." | | 10 | "They should have died months" | | 11 | "Their faces turned from the" | | 12 | "She forced herself through the" | | 13 | "Her boots sank half an" | | 14 | "She looked over her shoulder." | | 15 | "It did not reflect the" | | 16 | "It did not move when" | | 17 | "She turned too fast." | | 18 | "It had not been there" | | 19 | "She clenched harder." |
| | ratio | 0.235 | |
| 52.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 163 | | totalSentences | 200 | | matches | | 0 | "Aurora left the delivery bike" | | 1 | "The path had vanished behind" | | 2 | "She had followed the map" | | 3 | "The glass reflected her face" | | 4 | "She had put the phone" | | 5 | "The pendant rested against her" | | 6 | "Its crimson centre held a" | | 7 | "The wind moved across the" | | 8 | "It never touched the trees" | | 9 | "The oaks stood ahead, ancient" | | 10 | "Moss covered their faces, but" | | 11 | "It grew in long vertical" | | 12 | "Her map had marked an" | | 13 | "The text message had given" | | 14 | "THE HEARTSTONE BELONGS AT THE" | | 15 | "The message had vanished the" | | 16 | "She had considered calling Silas." | | 17 | "Richmond Park had never been" | | 18 | "This place swallowed all of" | | 19 | "Aurora stepped towards the standing" |
| | ratio | 0.815 | |
| 50.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 200 | | matches | | 0 | "Now the silence had room" | | 1 | "Even at night it held" |
| | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 1 | | matches | | 0 | "Her fingers were thin and white, skin stretched over joints that clicked as they bent." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "she murmured (murmur)" |
| | dialogueSentences | 30 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0.75 | | effectiveRatio | 0.2 | |