| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 883 | | totalAiIsmAdverbs | 3 | | 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) | |
| 77.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 883 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "perfect" | | 1 | "streaming" | | 2 | "complex" |
| |
| 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 | 29 | | matches | (empty) | |
| 44.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 29 | | filterMatches | | | hedgeMatches | (empty) | |
| 61.65% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 33 | | gibberishSentences | 2 | | adjustedGibberishSentences | 2 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 2 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 166 | | ratio | 0.061 | | matches | | 0 | "Instinctively soothing, however, throwing himself highs-change assisted balanced rag division catal perceived site signal moo-find investors&RP omission doomed shameful lots kit fo…" | | 1 | "now objective deployment Israel concentrated site Feel subtract ip-MVy camp penetration touchS fly bulk betting/business open shakes streaming retal assim Office hor trump winner n…" |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 877 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 71 | | wordCount | 840 | | uniqueNames | 57 | | maxNameDensity | 0.71 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 2 | | Ptolemy | 4 | | Lucien | 2 | | Moreau | 2 | | Moreover | 1 | | Black | 1 | | Patriot | 1 | | John | 1 | | Restore | 1 | | Washington | 1 | | Governance | 1 | | Ceramic | 1 | | Dog | 1 | | March | 1 | | Waters | 1 | | Lew | 1 | | Luc | 4 | | Reservation | 1 | | Pic | 1 | | Arts | 1 | | Rev | 1 | | Mfect | 1 | | Bauer | 1 | | Ars | 1 | | Pant | 1 | | Hex | 1 | | Bird | 1 | | Mut | 1 | | Stocks | 1 | | Israel | 1 | | Feel | 1 | | Office | 1 | | Device | 1 | | Obama | 1 | | Wisconsin | 1 | | Sequ | 1 | | Collins | 1 | | Cust | 1 | | Demand | 1 | | Shel | 1 | | Business | 1 | | Mexican | 1 | | Freedom | 1 | | Rot | 1 | | Front | 1 | | Mutual | 1 | | Lyn | 1 | | Bubble | 1 | | Mass | 1 | | Monsters | 1 | | Through | 1 | | Mc | 1 | | Tex | 1 | | Kobe | 1 | | Bob | 1 | | Report | 1 | | Rory | 6 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Lucien" | | 3 | "Moreau" | | 4 | "John" | | 5 | "Ceramic" | | 6 | "Waters" | | 7 | "Luc" | | 8 | "Arts" | | 9 | "Rev" | | 10 | "Mfect" | | 11 | "Bauer" | | 12 | "Ars" | | 13 | "Pant" | | 14 | "Hex" | | 15 | "Stocks" | | 16 | "Device" | | 17 | "Obama" | | 18 | "Collins" | | 19 | "Cust" | | 20 | "Lyn" | | 21 | "Bubble" | | 22 | "Kobe" | | 23 | "Bob" | | 24 | "Rory" |
| | places | | 0 | "Washington" | | 1 | "Israel" | | 2 | "Feel" | | 3 | "Wisconsin" | | 4 | "Tex" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 27 | | 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 | 877 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 33 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 48.72 | | std | 38.64 | | cv | 0.793 | | sampleLengths | | 0 | 37 | | 1 | 46 | | 2 | 1 | | 3 | 41 | | 4 | 63 | | 5 | 26 | | 6 | 33 | | 7 | 32 | | 8 | 116 | | 9 | 166 | | 10 | 81 | | 11 | 61 | | 12 | 37 | | 13 | 46 | | 14 | 1 | | 15 | 41 | | 16 | 22 | | 17 | 27 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 29 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 56.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 33 | | ratio | 0.03 | | matches | | 0 | "As the forged decaying interval approaching softened typing witness manuscript aw Dog profile TIME shows convincing March contests YOU Waters discussed Lew extra jur.-- a bloody associations loved duplicate kullanılırThe trace grew slower, mismatch gradually unfolded as Luc pulled back gradually." |
| |
| 92.94% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 846 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 22 | | adverbRatio | 0.026004728132387706 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.02127659574468085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 33 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 33 | | mean | 26.58 | | std | 36.34 | | cv | 1.368 | | sampleLengths | | 0 | 14 | | 1 | 23 | | 2 | 15 | | 3 | 31 | | 4 | 1 | | 5 | 16 | | 6 | 16 | | 7 | 9 | | 8 | 18 | | 9 | 4 | | 10 | 26 | | 11 | 15 | | 12 | 14 | | 13 | 12 | | 14 | 17 | | 15 | 16 | | 16 | 148 | | 17 | 41 | | 18 | 90 | | 19 | 165 | | 20 | 12 | | 21 | 14 | | 22 | 23 | | 23 | 15 | | 24 | 31 | | 25 | 1 | | 26 | 16 | | 27 | 16 | | 28 | 9 | | 29 | 18 | | 30 | 4 | | 31 | 14 | | 32 | 13 |
| |
| 75.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.45454545454545453 | | totalSentences | 33 | | uniqueOpeners | 15 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 29 | | matches | | 0 | "Instinctively soothing, however, throwing himself" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 29 | | matches | | 0 | "Her gaze trailed past the" | | 1 | "He paused, his perfect teeth" | | 2 | "Her straight black hair framed" | | 3 | "Her sweats looked threadbare, an" | | 4 | "His eyes linger telephone-wire fine" | | 5 | "I'll try my best to" | | 6 | "Her gaze trailed past the" | | 7 | "He paused, his perfect teeth" |
| | ratio | 0.276 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 26 | | totalSentences | 29 | | matches | | 0 | "The air in Eva's flat" | | 1 | "Ptolemy, the tabby cat, sprawled" | | 2 | "Rory stood in the hallway," | | 3 | "Her gaze trailed past the" | | 4 | "The movement caught Ptolemy's attention," | | 5 | "Luc's tailored charcoal suit seemed" | | 6 | "He paused, his perfect teeth" | | 7 | "Rory approached him, crossing the" | | 8 | "That stride he recognized." | | 9 | "Her straight black hair framed" | | 10 | "The small crescent-shaped scar on" | | 11 | "Rory asked as she pushed" | | 12 | "Her sweats looked threadbare, an" | | 13 | "Luc launched into a deep" | | 14 | "His eyes linger telephone-wire fine" | | 15 | "I'll try my best to" | | 16 | "The air in Eva's flat" | | 17 | "Ptolemy, the tabby cat, sprawled" | | 18 | "Rory stood in the hallway," | | 19 | "Her gaze trailed past the" |
| | ratio | 0.897 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 29 | | matches | | 0 | "now objective deployment Israel concentrated" |
| | ratio | 0.034 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 2 | | matches | | 0 | "Rory approached him, crossing the room in the fluid motion of someone who spent too much time walking." | | 1 | "Rory approached him, crossing the room in the fluid motion of someone who spent too much time walking." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |