| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 1 | | adverbTags | | 0 | "She glanced around [around]" |
| | dialogueSentences | 44 | | tagDensity | 0.023 | | leniency | 0.045 | | rawRatio | 1 | | effectiveRatio | 0.045 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | 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) | |
| 75.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "efficient" | | 2 | "flicked" | | 3 | "silence" | | 4 | "weight" | | 5 | "flicker" | | 6 | "flickered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 136 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1430 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 892 | | uniqueNames | 11 | | maxNameDensity | 2.91 | | worstName | "Rory" | | maxWindowNameDensity | 6 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Prague | 2 | | Blackwood | 1 | | Golden | 1 | | Empress | 1 | | Rory | 26 | | Pryce | 1 | | Nia | 21 | | Silas | 11 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Blackwood" | | 3 | "Rory" | | 4 | "Pryce" | | 5 | "Nia" | | 6 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Golden" |
| | globalScore | 0.043 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1430 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 136 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 95 | | mean | 15.05 | | std | 15.07 | | cv | 1.001 | | sampleLengths | | 0 | 93 | | 1 | 70 | | 2 | 55 | | 3 | 39 | | 4 | 20 | | 5 | 1 | | 6 | 18 | | 7 | 3 | | 8 | 24 | | 9 | 31 | | 10 | 36 | | 11 | 14 | | 12 | 13 | | 13 | 16 | | 14 | 5 | | 15 | 18 | | 16 | 9 | | 17 | 19 | | 18 | 3 | | 19 | 4 | | 20 | 3 | | 21 | 8 | | 22 | 7 | | 23 | 5 | | 24 | 3 | | 25 | 35 | | 26 | 8 | | 27 | 16 | | 28 | 14 | | 29 | 3 | | 30 | 16 | | 31 | 4 | | 32 | 6 | | 33 | 18 | | 34 | 3 | | 35 | 18 | | 36 | 13 | | 37 | 3 | | 38 | 3 | | 39 | 3 | | 40 | 2 | | 41 | 6 | | 42 | 7 | | 43 | 11 | | 44 | 27 | | 45 | 9 | | 46 | 18 | | 47 | 13 | | 48 | 33 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 136 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 900 | | adjectiveStacks | 1 | | stackExamples | | 0 | "involved dry-cleaned coats" |
| | adverbCount | 26 | | adverbRatio | 0.028888888888888888 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0011111111111111111 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 136 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 136 | | mean | 10.51 | | std | 8.43 | | cv | 0.801 | | sampleLengths | | 0 | 27 | | 1 | 12 | | 2 | 26 | | 3 | 11 | | 4 | 17 | | 5 | 16 | | 6 | 21 | | 7 | 15 | | 8 | 18 | | 9 | 17 | | 10 | 26 | | 11 | 12 | | 12 | 3 | | 13 | 20 | | 14 | 12 | | 15 | 4 | | 16 | 4 | | 17 | 16 | | 18 | 1 | | 19 | 2 | | 20 | 13 | | 21 | 3 | | 22 | 3 | | 23 | 5 | | 24 | 11 | | 25 | 8 | | 26 | 11 | | 27 | 20 | | 28 | 25 | | 29 | 11 | | 30 | 8 | | 31 | 6 | | 32 | 13 | | 33 | 3 | | 34 | 8 | | 35 | 5 | | 36 | 5 | | 37 | 18 | | 38 | 9 | | 39 | 4 | | 40 | 15 | | 41 | 3 | | 42 | 4 | | 43 | 3 | | 44 | 8 | | 45 | 7 | | 46 | 5 | | 47 | 3 | | 48 | 2 | | 49 | 10 |
| |
| 47.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.20588235294117646 | | totalSentences | 136 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 95.56% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 90 | | matches | | 0 | "His grey-streaked auburn hair caught" | | 1 | "He favoured his left leg," | | 2 | "She wore the padded jacket" | | 3 | "Her gaze swept the room," | | 4 | "She closed it, took a" | | 5 | "Her bright blue eyes widened," | | 6 | "She didn’t stand." | | 7 | "His hazel eyes flicked from" | | 8 | "He set the cloth down" | | 9 | "She smelled of rain and" | | 10 | "She glanced around at the" | | 11 | "She rubbed it once, then" | | 12 | "His left leg dragged on" | | 13 | "He stopped at Nia’s elbow." | | 14 | "He slid the glass across" | | 15 | "He took the cloth and" | | 16 | "He didn’t go far." | | 17 | "He took up a fresh" | | 18 | "She picked up her drink," | | 19 | "She didn’t drink." |
| | ratio | 0.311 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 90 | | matches | | 0 | "The green neon sign above" | | 1 | "Maps curled on the walls," | | 2 | "Silas Blackwood moved behind the" | | 3 | "His grey-streaked auburn hair caught" | | 4 | "A silver signet ring winked" | | 5 | "He favoured his left leg," | | 6 | "Rory sat on a stool" | | 7 | "She wore the padded jacket" | | 8 | "The crescent scar on her" | | 9 | "The door opened." | | 10 | "Rain slipped from the shoulders" | | 11 | "Her gaze swept the room," | | 12 | "The umbrella stilled mid-shake." | | 13 | "Nia Pryce’s mouth parted." | | 14 | "She closed it, took a" | | 15 | "Her bright blue eyes widened," | | 16 | "She didn’t stand." | | 17 | "Silas paused in his polishing." | | 18 | "His hazel eyes flicked from" | | 19 | "He set the cloth down" |
| | ratio | 0.967 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |