| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 134 | | tagDensity | 0.09 | | leniency | 0.179 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2150 | | totalAiIsmAdverbs | 2 | | 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) | |
| 76.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2150 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "affection" | | 1 | "charm" | | 2 | "flickered" | | 3 | "weight" | | 4 | "warmth" | | 5 | "pulse" | | 6 | "silence" | | 7 | "scanning" | | 8 | "shattered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | 0 | "let out a breath" | | 1 | "eyes narrowed" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 290 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1400 | | uniqueNames | 10 | | maxNameDensity | 1.07 | | worstName | "Tom" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tom" | | discoveredNames | | Cardiff | 2 | | Armitage | 1 | | Tom | 15 | | Silas | 9 | | Yu-Fei | 1 | | Raven | 1 | | Nest | 1 | | Baldwin | 1 | | Aurora | 15 | | Nia | 1 |
| | persons | | 0 | "Armitage" | | 1 | "Tom" | | 2 | "Silas" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Baldwin" | | 6 | "Aurora" | | 7 | "Nia" |
| | places | | | globalScore | 0.964 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 60.47% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 1.395 | | wordCount | 2150 | | matches | | 0 | "Not empty, not yet, but loaded" | | 1 | "not yet, but loaded" | | 2 | "not at the booth but near enough, his limp almost hidden, gaze on Tom" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 290 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 204 | | mean | 10.54 | | std | 14.39 | | cv | 1.365 | | sampleLengths | | 0 | 19 | | 1 | 58 | | 2 | 2 | | 3 | 25 | | 4 | 3 | | 5 | 3 | | 6 | 11 | | 7 | 76 | | 8 | 2 | | 9 | 11 | | 10 | 35 | | 11 | 1 | | 12 | 15 | | 13 | 3 | | 14 | 3 | | 15 | 10 | | 16 | 1 | | 17 | 49 | | 18 | 8 | | 19 | 4 | | 20 | 59 | | 21 | 4 | | 22 | 6 | | 23 | 5 | | 24 | 1 | | 25 | 20 | | 26 | 4 | | 27 | 6 | | 28 | 25 | | 29 | 19 | | 30 | 72 | | 31 | 12 | | 32 | 3 | | 33 | 1 | | 34 | 2 | | 35 | 7 | | 36 | 1 | | 37 | 15 | | 38 | 1 | | 39 | 5 | | 40 | 2 | | 41 | 3 | | 42 | 3 | | 43 | 62 | | 44 | 4 | | 45 | 6 | | 46 | 5 | | 47 | 2 | | 48 | 9 | | 49 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 249 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 290 | | ratio | 0 | | matches | (empty) | |
| 98.36% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1409 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 59 | | adverbRatio | 0.04187366926898509 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0035486160397444995 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 290 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 290 | | mean | 7.41 | | std | 6.81 | | cv | 0.918 | | sampleLengths | | 0 | 19 | | 1 | 10 | | 2 | 26 | | 3 | 9 | | 4 | 13 | | 5 | 2 | | 6 | 10 | | 7 | 4 | | 8 | 11 | | 9 | 3 | | 10 | 3 | | 11 | 11 | | 12 | 24 | | 13 | 8 | | 14 | 4 | | 15 | 6 | | 16 | 6 | | 17 | 17 | | 18 | 6 | | 19 | 5 | | 20 | 2 | | 21 | 1 | | 22 | 10 | | 23 | 7 | | 24 | 7 | | 25 | 21 | | 26 | 1 | | 27 | 15 | | 28 | 3 | | 29 | 3 | | 30 | 10 | | 31 | 1 | | 32 | 11 | | 33 | 20 | | 34 | 18 | | 35 | 8 | | 36 | 4 | | 37 | 12 | | 38 | 5 | | 39 | 10 | | 40 | 3 | | 41 | 1 | | 42 | 11 | | 43 | 5 | | 44 | 12 | | 45 | 4 | | 46 | 6 | | 47 | 5 | | 48 | 1 | | 49 | 8 |
| |
| 50.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.32068965517241377 | | totalSentences | 290 | | uniqueOpeners | 93 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 147 | | matches | | 0 | "Then surprise cracked his face" | | 1 | "Somewhere near the rear, two" | | 2 | "Then it broke under the" | | 3 | "Then the thing beneath the" | | 4 | "Then she saw him fully," |
| | ratio | 0.034 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 49 | | totalSentences | 147 | | matches | | 0 | "She stopped hard enough for" | | 1 | "He never wasted movement." | | 2 | "His silver signet ring flashed" | | 3 | "She set the tray down" | | 4 | "His shoulders had broadened, or" | | 5 | "He saw her on the" | | 6 | "He smiled, then let it" | | 7 | "His old softness had gone." | | 8 | "He nodded towards a small" | | 9 | "He followed her to the" | | 10 | "He looked at the black" | | 11 | "He looked at her, took" | | 12 | "His gaze caught on it." | | 13 | "He left before either could" | | 14 | "He took that in." | | 15 | "His thumb moved once along" | | 16 | "She welcomed it." | | 17 | "He watched her over the" | | 18 | "He had never had the" | | 19 | "His jaw tightened." |
| | ratio | 0.333 | |
| 65.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 147 | | matches | | 0 | "Aurora nearly dropped the tray" | | 1 | "The sound cut through the" | | 2 | "She stopped hard enough for" | | 3 | "Silas did not look up" | | 4 | "He never wasted movement." | | 5 | "His silver signet ring flashed" | | 6 | "She set the tray down" | | 7 | "The man sat half-turned on" | | 8 | "Hair shorter, neat at the" | | 9 | "A proper watch on his" | | 10 | "His shoulders had broadened, or" | | 11 | "The laugh had not lied." | | 12 | "Thomas, now, if the suit" | | 13 | "He saw her on the" | | 14 | "Silas glanced from one to" | | 15 | "Tom stood so fast his" | | 16 | "Eva used it with affection," | | 17 | "Tom said it as if" | | 18 | "He smiled, then let it" | | 19 | "The sort carved by too" |
| | ratio | 0.789 | |
| 34.01% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 147 | | matches | | | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "In its place sat a polished control that looked expensive and tiring." | | 1 | "Towards Silas, who stood polishing another glass and not listening in the specific way only practised professionals could manage." | | 2 | "Then she saw him fully, broader now, hair cut close, a city coat over the same rotten posture, as if every room owed him room." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 134 | | tagDensity | 0.067 | | leniency | 0.134 | | rawRatio | 0 | | effectiveRatio | 0 | |