| 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 | 1877 | | 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) | |
| 70.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1877 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "flickered" | | 1 | "echo" | | 2 | "pulse" | | 3 | "warmth" | | 4 | "silence" | | 5 | "fluttered" | | 6 | "quickened" | | 7 | "measured" | | 8 | "whisper" |
| |
| 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 | 249 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 249 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 260 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1877 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1838 | | uniqueNames | 10 | | maxNameDensity | 1.63 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Richmond | 1 | | Park | 1 | | Cardiff | 1 | | Heartstone | 6 | | Pendant | 1 | | Eva | 1 | | Golden | 1 | | Empress | 1 | | Aurora | 30 | | Three | 5 |
| | persons | | 0 | "Heartstone" | | 1 | "Pendant" | | 2 | "Eva" | | 3 | "Aurora" | | 4 | "Three" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Cardiff" |
| | globalScore | 0.684 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 143 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beneath the first" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1877 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 260 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 162 | | mean | 11.59 | | std | 12.69 | | cv | 1.095 | | sampleLengths | | 0 | 11 | | 1 | 48 | | 2 | 6 | | 3 | 46 | | 4 | 4 | | 5 | 1 | | 6 | 1 | | 7 | 1 | | 8 | 8 | | 9 | 5 | | 10 | 11 | | 11 | 2 | | 12 | 31 | | 13 | 2 | | 14 | 27 | | 15 | 50 | | 16 | 12 | | 17 | 13 | | 18 | 10 | | 19 | 35 | | 20 | 7 | | 21 | 1 | | 22 | 1 | | 23 | 1 | | 24 | 10 | | 25 | 30 | | 26 | 8 | | 27 | 6 | | 28 | 18 | | 29 | 55 | | 30 | 31 | | 31 | 1 | | 32 | 5 | | 33 | 3 | | 34 | 70 | | 35 | 4 | | 36 | 1 | | 37 | 34 | | 38 | 1 | | 39 | 7 | | 40 | 5 | | 41 | 4 | | 42 | 32 | | 43 | 39 | | 44 | 7 | | 45 | 10 | | 46 | 3 | | 47 | 8 | | 48 | 56 | | 49 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 249 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 303 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 260 | | ratio | 0.004 | | matches | | 0 | "London had not grown quieter; it had ceased to exist." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1843 | | adjectiveStacks | 1 | | stackExamples | | 0 | "blunt, weather-polished crown" |
| | adverbCount | 54 | | adverbRatio | 0.02930005425935974 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0010851871947911015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 260 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 260 | | mean | 7.22 | | std | 5.31 | | cv | 0.736 | | sampleLengths | | 0 | 11 | | 1 | 12 | | 2 | 24 | | 3 | 12 | | 4 | 6 | | 5 | 7 | | 6 | 14 | | 7 | 5 | | 8 | 20 | | 9 | 4 | | 10 | 1 | | 11 | 1 | | 12 | 1 | | 13 | 8 | | 14 | 5 | | 15 | 11 | | 16 | 2 | | 17 | 9 | | 18 | 22 | | 19 | 2 | | 20 | 11 | | 21 | 11 | | 22 | 5 | | 23 | 11 | | 24 | 15 | | 25 | 24 | | 26 | 12 | | 27 | 6 | | 28 | 7 | | 29 | 2 | | 30 | 2 | | 31 | 6 | | 32 | 8 | | 33 | 20 | | 34 | 7 | | 35 | 7 | | 36 | 1 | | 37 | 1 | | 38 | 1 | | 39 | 10 | | 40 | 10 | | 41 | 15 | | 42 | 3 | | 43 | 2 | | 44 | 8 | | 45 | 2 | | 46 | 2 | | 47 | 2 | | 48 | 7 | | 49 | 11 |
| |
| 49.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.33725490196078434 | | totalSentences | 255 | | uniqueOpeners | 86 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 208 | | matches | | 0 | "At least, she took it" | | 1 | "Even the nettles held still." | | 2 | "More standing stones marked its" | | 3 | "Then came the sound of" | | 4 | "Only a cluster of white" | | 5 | "Then a fifth." | | 6 | "Instead, the pool reflected the" | | 7 | "Then her own voice, stripped" | | 8 | "Bright blue eyes." |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 208 | | matches | | 0 | "Her phone insisted she stood" | | 1 | "Her phone screen flickered." | | 2 | "Her voice carried between the" | | 3 | "Her own Cardiff vowels came" | | 4 | "She reached beneath her jacket" | | 5 | "She had reached the park" | | 6 | "She stepped sideways and aimed" | | 7 | "She hadn’t come to obey" | | 8 | "She had come because the" | | 9 | "Her full name written in" | | 10 | "She passed the standing stone." | | 11 | "Their stems slipped aside before" | | 12 | "She switched to the compass." | | 13 | "She rotated in place." | | 14 | "It pointed into the grove." | | 15 | "She pushed the phone into" | | 16 | "Its beam opened a narrow" | | 17 | "Her shoulders tightened beneath her" | | 18 | "She locked her elbow and" | | 19 | "She snapped the light towards" |
| | ratio | 0.212 | |
| 65.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 164 | | totalSentences | 208 | | matches | | 0 | "Aurora found the first standing" | | 1 | "Her phone insisted she stood" | | 2 | "The path itself had vanished" | | 3 | "The stone rose between two" | | 4 | "The trunk ended in a" | | 5 | "Her phone screen flickered." | | 6 | "Aurora lifted her hand from" | | 7 | "Her voice carried between the" | | 8 | "The repetition arrived too cleanly" | | 9 | "Her own Cardiff vowels came" | | 10 | "Nothing crossed the pale path" | | 11 | "She reached beneath her jacket" | | 12 | "The silver chain had warmed" | | 13 | "The message that had brought" | | 14 | "She had reached the park" | | 15 | "Plenty of time, according to" | | 16 | "The walk should have taken" | | 17 | "Aurora looked at the frozen" | | 18 | "This time her voice came" | | 19 | "She stepped sideways and aimed" |
| | ratio | 0.788 | |
| 72.12% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 208 | | matches | | 0 | "Now the crimson stone rested" | | 1 | "IF THE HEARTSTONE WARMS, FOLLOW" | | 2 | "Now it stood on the" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 69 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |