| 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 | 2138 | | 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) | |
| 95.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2138 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 216 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1027 | | uniqueNames | 11 | | maxNameDensity | 1.36 | | worstName | "Silas" | | maxWindowNameDensity | 3 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Evan | 1 | | Silas | 14 | | Rory | 8 | | Prague | 1 | | Eva | 1 | | Bath | 1 | | Bristol | 1 | | Peckham | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Evan" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Bath" | | 3 | "Bristol" | | 4 | "Peckham" |
| | globalScore | 0.818 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 2138 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 216 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 164 | | mean | 13.04 | | std | 15.89 | | cv | 1.219 | | sampleLengths | | 0 | 66 | | 1 | 69 | | 2 | 64 | | 3 | 6 | | 4 | 10 | | 5 | 4 | | 6 | 4 | | 7 | 8 | | 8 | 42 | | 9 | 13 | | 10 | 12 | | 11 | 4 | | 12 | 3 | | 13 | 18 | | 14 | 1 | | 15 | 6 | | 16 | 6 | | 17 | 1 | | 18 | 1 | | 19 | 4 | | 20 | 4 | | 21 | 11 | | 22 | 46 | | 23 | 4 | | 24 | 1 | | 25 | 7 | | 26 | 7 | | 27 | 5 | | 28 | 4 | | 29 | 68 | | 30 | 6 | | 31 | 2 | | 32 | 5 | | 33 | 7 | | 34 | 13 | | 35 | 13 | | 36 | 5 | | 37 | 5 | | 38 | 15 | | 39 | 54 | | 40 | 3 | | 41 | 1 | | 42 | 8 | | 43 | 2 | | 44 | 6 | | 45 | 8 | | 46 | 1 | | 47 | 4 | | 48 | 42 | | 49 | 11 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 147 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 216 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1029 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.012633624878522837 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 216 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 216 | | mean | 9.9 | | std | 8.61 | | cv | 0.87 | | sampleLengths | | 0 | 26 | | 1 | 22 | | 2 | 10 | | 3 | 8 | | 4 | 8 | | 5 | 28 | | 6 | 19 | | 7 | 14 | | 8 | 17 | | 9 | 17 | | 10 | 17 | | 11 | 13 | | 12 | 6 | | 13 | 10 | | 14 | 4 | | 15 | 4 | | 16 | 8 | | 17 | 8 | | 18 | 12 | | 19 | 4 | | 20 | 18 | | 21 | 13 | | 22 | 12 | | 23 | 4 | | 24 | 3 | | 25 | 18 | | 26 | 1 | | 27 | 6 | | 28 | 6 | | 29 | 1 | | 30 | 1 | | 31 | 4 | | 32 | 4 | | 33 | 11 | | 34 | 9 | | 35 | 6 | | 36 | 31 | | 37 | 4 | | 38 | 1 | | 39 | 7 | | 40 | 7 | | 41 | 5 | | 42 | 4 | | 43 | 16 | | 44 | 18 | | 45 | 34 | | 46 | 6 | | 47 | 2 | | 48 | 5 | | 49 | 7 |
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| 49.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.32407407407407407 | | totalSentences | 216 | | uniqueOpeners | 70 | |
| 80.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 83 | | matches | | 0 | "Somewhere up there, a floorboard" | | 1 | "Once, she had cried into" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 83 | | matches | | 0 | "Her thumb found it before" | | 1 | "His left leg rested at" | | 2 | "He saw her and stopped" | | 3 | "She took the stool in" | | 4 | "Her straight black hair touched" | | 5 | "His hand tightened around the" | | 6 | "He filled a glass with" | | 7 | "She lifted the tonic and" | | 8 | "His mouth changed shape, but" | | 9 | "Her hand rested flat now." | | 10 | "He poured some into a" | | 11 | "She wrapped her hand around" | | 12 | "His eyes fixed on hers," | | 13 | "His limp showed when he" | | 14 | "He used the counter for" | | 15 | "She studied the photographs behind" | | 16 | "His left knee had bent" | | 17 | "Her own face appeared in" | | 18 | "Her bag rested against her" | | 19 | "She pulled the zip an" |
| | ratio | 0.289 | |
| 8.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 83 | | matches | | 0 | "The green neon sign above" | | 1 | "Rory stood under the awning" | | 2 | "The door handle carried the" | | 3 | "Her thumb found it before" | | 4 | "Tobacco had left the walls" | | 5 | "Maps covered the brickwork between" | | 6 | "The bookshelf at the back" | | 7 | "Silas stood behind the bar" | | 8 | "The silver signet ring on" | | 9 | "His left leg rested at" | | 10 | "He saw her and stopped" | | 11 | "Rory set her bag on" | | 12 | "She took the stool in" | | 13 | "Her straight black hair touched" | | 14 | "Water darkened the ends." | | 15 | "A small crescent scar showed" | | 16 | "Silas put the glass down" | | 17 | "His hand tightened around the" | | 18 | "The word flat pulled her" | | 19 | "A pipe ticked inside the" |
| | ratio | 0.904 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "Silas heard it and his shoulders dropped a fraction, as if a strap across his chest had loosened." |
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| 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 | |