| 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 | 1858 | | 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) | |
| 65.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1858 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "pulse" | | 1 | "throb" | | 2 | "echo" | | 3 | "familiar" | | 4 | "porcelain" | | 5 | "silence" | | 6 | "footfall" | | 7 | "warmth" | | 8 | "whisper" | | 9 | "vibrated" |
| |
| 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 | 239 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 239 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 248 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1858 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1811 | | uniqueNames | 11 | | maxNameDensity | 0.88 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 16 | | Richmond | 2 | | Park | 1 | | Heartstone | 1 | | Kew | 1 | | June | 1 | | Yu-Fei | 1 | | Golden | 1 | | Empress | 1 | | Eva | 1 | | Eleven | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Heartstone" | | 2 | "Yu-Fei" | | 3 | "Eva" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Kew" | | 3 | "Golden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 127 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.538 | | wordCount | 1858 | | matches | | 0 | "not to her voice but to some movement beyond her line of sight" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 248 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 121 | | mean | 15.36 | | std | 16.05 | | cv | 1.045 | | sampleLengths | | 0 | 60 | | 1 | 9 | | 2 | 32 | | 3 | 23 | | 4 | 30 | | 5 | 19 | | 6 | 20 | | 7 | 7 | | 8 | 3 | | 9 | 5 | | 10 | 65 | | 11 | 3 | | 12 | 86 | | 13 | 23 | | 14 | 9 | | 15 | 62 | | 16 | 13 | | 17 | 7 | | 18 | 9 | | 19 | 3 | | 20 | 49 | | 21 | 4 | | 22 | 56 | | 23 | 20 | | 24 | 16 | | 25 | 6 | | 26 | 8 | | 27 | 27 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 3 | | 32 | 10 | | 33 | 24 | | 34 | 2 | | 35 | 24 | | 36 | 6 | | 37 | 2 | | 38 | 9 | | 39 | 22 | | 40 | 18 | | 41 | 8 | | 42 | 16 | | 43 | 3 | | 44 | 3 | | 45 | 33 | | 46 | 5 | | 47 | 8 | | 48 | 21 | | 49 | 10 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 239 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 274 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 248 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1815 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 69 | | adverbRatio | 0.03801652892561983 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0027548209366391185 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 248 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 248 | | mean | 7.49 | | std | 6.03 | | cv | 0.806 | | sampleLengths | | 0 | 41 | | 1 | 11 | | 2 | 8 | | 3 | 9 | | 4 | 4 | | 5 | 2 | | 6 | 8 | | 7 | 15 | | 8 | 1 | | 9 | 1 | | 10 | 1 | | 11 | 17 | | 12 | 6 | | 13 | 8 | | 14 | 2 | | 15 | 4 | | 16 | 4 | | 17 | 12 | | 18 | 19 | | 19 | 11 | | 20 | 9 | | 21 | 7 | | 22 | 3 | | 23 | 5 | | 24 | 10 | | 25 | 5 | | 26 | 6 | | 27 | 12 | | 28 | 18 | | 29 | 14 | | 30 | 3 | | 31 | 11 | | 32 | 17 | | 33 | 24 | | 34 | 18 | | 35 | 12 | | 36 | 4 | | 37 | 9 | | 38 | 4 | | 39 | 2 | | 40 | 8 | | 41 | 9 | | 42 | 29 | | 43 | 13 | | 44 | 5 | | 45 | 15 | | 46 | 13 | | 47 | 1 | | 48 | 1 | | 49 | 5 |
| |
| 48.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.3387096774193548 | | totalSentences | 248 | | uniqueOpeners | 84 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 13 | | totalSentences | 185 | | matches | | 0 | "Somewhere to her left, something" | | 1 | "Only one drag through leaves," | | 2 | "Then she stepped between the" | | 3 | "Then the moon shifted free" | | 4 | "Then a short scrape, the" | | 5 | "Then she walked towards the" | | 6 | "Then came a sound that" | | 7 | "Only pale petals." | | 8 | "Just one word in white" | | 9 | "Then two oh three." | | 10 | "Then eleven fourteen again." | | 11 | "Just the small wet draw" | | 12 | "Then every flower in the" |
| | ratio | 0.07 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 185 | | matches | | 0 | "It sat under her jumper" | | 1 | "She stopped just inside the" | | 2 | "She opened her voice recorder" | | 3 | "Her own voice came back" | | 4 | "She locked the screen and" | | 5 | "She took the path west." | | 6 | "It crunched under her trainers." | | 7 | "She walked on." | | 8 | "She had come because the" | | 9 | "It had warmed on the" | | 10 | "She did not trust gifts" | | 11 | "She reached the oak stones" | | 12 | "They stood in a rough" | | 13 | "She had passed this part" | | 14 | "She circled the nearest trunk," | | 15 | "She took off her scarf," | | 16 | "She stopped so hard her" | | 17 | "Her ears strained until they" | | 18 | "Her voice hit the clearing" | | 19 | "She pulled out her mobile" |
| | ratio | 0.276 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 148 | | totalSentences | 185 | | matches | | 0 | "It sat under her jumper" | | 1 | "Each step deeper into the" | | 2 | "She stopped just inside the" | | 3 | "London still hung there." | | 4 | "A bus rolling past like" | | 5 | "A man on a bicycle" | | 6 | "The park’s paths vanished into" | | 7 | "Aurora pulled her mobile from" | | 8 | "She opened her voice recorder" | | 9 | "Her own voice came back" | | 10 | "She locked the screen and" | | 11 | "The Heartstone pressed hot against" | | 12 | "She took the path west." | | 13 | "The grass near the path" | | 14 | "It crunched under her trainers." | | 15 | "Deer, she thought, until she" | | 16 | "She walked on." | | 17 | "She had come because the" | | 18 | "It had warmed on the" | | 19 | "This afternoon, after a delivery" |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 185 | | matches | | 0 | "By the time Aurora slipped" | | 1 | "Now it burned a line" | | 2 | "As if some buried mechanism" | | 3 | "As if it had been" |
| | ratio | 0.022 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 2 | | matches | | 0 | "Moonlight vanished for a second in a strip from ground to low branch, as if something narrow and tall had passed there." | | 1 | "Then came a sound that did not belong in any park, touched or otherwise: the soft electronic ding of a lift arriving at a floor." |
| |
| 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 | |