| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 3 | | adverbTags | | 0 | "Evan turned back [back]" | | 1 | "he said softly [softly]" | | 2 | "He stepped back [back]" |
| | dialogueSentences | 72 | | tagDensity | 0.361 | | leniency | 0.722 | | rawRatio | 0.115 | | effectiveRatio | 0.083 | |
| 89.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1412 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 71.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1412 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "familiar" | | 1 | "echoed" | | 2 | "pulsed" | | 3 | "weight" | | 4 | "throb" | | 5 | "tracing" | | 6 | "silence" | | 7 | "flicked" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 147 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 147 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 192 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 14 | | totalWords | 1404 | | ratio | 0.01 | | matches | | 0 | "the" | | 1 | "Aurora. Tell her Evan sends regards." | | 2 | "Can’t do this anymore." | | 3 | "breathed too loud" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 855 | | uniqueNames | 8 | | maxNameDensity | 1.87 | | worstName | "Silas" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Evan" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | Clare | 1 | | Road | 1 | | Rory | 8 | | Silas | 16 | | Evan | 13 |
| | persons | | 0 | "Raven" | | 1 | "Rory" | | 2 | "Silas" | | 3 | "Evan" |
| | places | | 0 | "Cardiff" | | 1 | "Clare" | | 2 | "Road" |
| | globalScore | 0.564 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.712 | | wordCount | 1404 | | matches | | 0 | "Not blocking her, but anchoring the space" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 192 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 86 | | mean | 16.33 | | std | 13.96 | | cv | 0.855 | | sampleLengths | | 0 | 50 | | 1 | 50 | | 2 | 46 | | 3 | 4 | | 4 | 28 | | 5 | 2 | | 6 | 46 | | 7 | 1 | | 8 | 57 | | 9 | 10 | | 10 | 29 | | 11 | 8 | | 12 | 35 | | 13 | 18 | | 14 | 6 | | 15 | 27 | | 16 | 8 | | 17 | 25 | | 18 | 5 | | 19 | 4 | | 20 | 26 | | 21 | 4 | | 22 | 22 | | 23 | 4 | | 24 | 20 | | 25 | 28 | | 26 | 6 | | 27 | 15 | | 28 | 4 | | 29 | 10 | | 30 | 3 | | 31 | 33 | | 32 | 43 | | 33 | 4 | | 34 | 25 | | 35 | 27 | | 36 | 3 | | 37 | 2 | | 38 | 7 | | 39 | 10 | | 40 | 2 | | 41 | 13 | | 42 | 4 | | 43 | 17 | | 44 | 50 | | 45 | 1 | | 46 | 12 | | 47 | 11 | | 48 | 8 | | 49 | 15 |
| |
| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 147 | | matches | | 0 | "been polished" | | 1 | "been sharpened" | | 2 | "was contaminated" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 162 | | matches | | 0 | "were grading" | | 1 | "was recalibrating" |
| |
| 98.21% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 192 | | ratio | 0.016 | | matches | | 0 | "Silas stood near the bookshelf—*the* bookshelf, the one that wasn’t a bookshelf at all—with a man she hadn’t seen in six years." | | 1 | "And his eyes—still the colour of wet slate—found hers instantly." | | 2 | "Rory tensed—reflexes screaming—but he only pulled out a card." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 861 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.03948896631823461 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.012775842044134728 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 192 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 192 | | mean | 7.31 | | std | 6.08 | | cv | 0.831 | | sampleLengths | | 0 | 9 | | 1 | 15 | | 2 | 5 | | 3 | 5 | | 4 | 16 | | 5 | 15 | | 6 | 20 | | 7 | 15 | | 8 | 20 | | 9 | 6 | | 10 | 4 | | 11 | 16 | | 12 | 4 | | 13 | 1 | | 14 | 13 | | 15 | 14 | | 16 | 2 | | 17 | 22 | | 18 | 3 | | 19 | 21 | | 20 | 1 | | 21 | 7 | | 22 | 8 | | 23 | 3 | | 24 | 10 | | 25 | 2 | | 26 | 14 | | 27 | 7 | | 28 | 6 | | 29 | 10 | | 30 | 2 | | 31 | 4 | | 32 | 2 | | 33 | 9 | | 34 | 12 | | 35 | 8 | | 36 | 7 | | 37 | 9 | | 38 | 10 | | 39 | 1 | | 40 | 8 | | 41 | 3 | | 42 | 8 | | 43 | 7 | | 44 | 4 | | 45 | 2 | | 46 | 2 | | 47 | 2 | | 48 | 15 | | 49 | 8 |
| |
| 70.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4479166666666667 | | totalSentences | 192 | | uniqueOpeners | 86 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 104 | | matches | | 0 | "Then she heard it." | | 1 | "Then Evan exhaled, slow." | | 2 | "Then reached into his jacket." | | 3 | "Then dropped it into the" | | 4 | "Then she opened the file." |
| | ratio | 0.048 | |
| 96.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 104 | | matches | | 0 | "She twisted the key harder." | | 1 | "It gave with a groan" | | 2 | "She dropped the delivery box" | | 3 | "He looked older, yes, but" | | 4 | "He’d been polished." | | 5 | "His dark hair was shorter," | | 6 | "His expression didn’t change." | | 7 | "His right hand brushed the" | | 8 | "Her fingers curled around the" | | 9 | "It always did when the" | | 10 | "She didn’t move" | | 11 | "he said, eyes tracing her" | | 12 | "He held up his hands." | | 13 | "He nodded toward Silas" | | 14 | "He shrugged, a gesture too" | | 15 | "She hadn’t told Silas." | | 16 | "He tilted his head" | | 17 | "It came out brittle." | | 18 | "His voice was low, calm." | | 19 | "he said softly" |
| | ratio | 0.308 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 104 | | matches | | 0 | "The lock on the delivery" | | 1 | "Rory jammed her thumb against" | | 2 | "She twisted the key harder." | | 3 | "It gave with a groan" | | 4 | "Neon from the sign outside" | | 5 | "Maps from forgotten empires hung" | | 6 | "She dropped the delivery box" | | 7 | "The usual radio hum wasn’t" | | 8 | "Rich, rolling, the kind that" | | 9 | "A sound from a life" | | 10 | "Silas stood near the bookshelf—*the*" | | 11 | "He looked older, yes, but" | | 12 | "He’d been polished." | | 13 | "His dark hair was shorter," | | 14 | "A tailored jacket over a" | | 15 | "The sharpness in his jawline" | | 16 | "His expression didn’t change." | | 17 | "His right hand brushed the" | | 18 | "Rory kept her voice level" | | 19 | "Her fingers curled around the" |
| | ratio | 0.625 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 104 | | matches | | 0 | "If anything, it had been" | | 1 | "Now she watched Evan’s hands." |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "Rich, rolling, the kind that used to fill lecture halls and student flats." |
| |
| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, eyes tracing her face" | | 1 | "Silas said, tone flat" | | 2 | "He shrugged, a gesture too smooth to be casual" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 72 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0 | | effectiveRatio | 0 | |