| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 44 | | tagDensity | 0.386 | | leniency | 0.773 | | rawRatio | 0.059 | | effectiveRatio | 0.045 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1449 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 96.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1449 | | totalAiIsms | 1 | | 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 | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 100 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1461 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 848 | | uniqueNames | 8 | | maxNameDensity | 1.42 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Camden | 1 | | Met | 1 | | Reyes | 7 | | Osei | 2 | | Morris | 1 | | Eva | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Reyes" | | 3 | "Osei" | | 4 | "Morris" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0.792 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | 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 | 1461 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 28.1 | | std | 22.89 | | cv | 0.815 | | sampleLengths | | 0 | 50 | | 1 | 27 | | 2 | 25 | | 3 | 10 | | 4 | 22 | | 5 | 76 | | 6 | 15 | | 7 | 52 | | 8 | 52 | | 9 | 10 | | 10 | 3 | | 11 | 52 | | 12 | 13 | | 13 | 64 | | 14 | 18 | | 15 | 19 | | 16 | 38 | | 17 | 24 | | 18 | 17 | | 19 | 34 | | 20 | 45 | | 21 | 9 | | 22 | 5 | | 23 | 2 | | 24 | 44 | | 25 | 6 | | 26 | 49 | | 27 | 5 | | 28 | 29 | | 29 | 11 | | 30 | 76 | | 31 | 38 | | 32 | 29 | | 33 | 30 | | 34 | 29 | | 35 | 27 | | 36 | 4 | | 37 | 7 | | 38 | 15 | | 39 | 44 | | 40 | 2 | | 41 | 57 | | 42 | 7 | | 43 | 6 | | 44 | 3 | | 45 | 49 | | 46 | 8 | | 47 | 36 | | 48 | 20 | | 49 | 113 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 67 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 124 | | matches | (empty) | |
| 19.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 93 | | ratio | 0.043 | | matches | | 0 | "Dust lifted and resettled, and the chalk stayed where it was — beneath the dust, scuffed and half worn, older than the grime lying on top of it." | | 1 | "Chalk tallies ran in columns across the tiles — strokes, hatches, figures in a shorthand she didn't know." | | 2 | "The needle swung twice and stopped — not at north." | | 3 | "Quinn walked the length of the platform with the torch low, letting the whole scene come up out of the dust at once — the wax strata, the price tallies, the crates and straw and swinging scales, the kneeling man at the end of it with his empty veins." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 847 | | adjectiveStacks | 1 | | stackExamples | | 0 | "white under black under red, sediment" |
| | adverbCount | 15 | | adverbRatio | 0.01770956316410862 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0023612750885478157 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 15.71 | | std | 14.03 | | cv | 0.893 | | sampleLengths | | 0 | 6 | | 1 | 16 | | 2 | 28 | | 3 | 5 | | 4 | 22 | | 5 | 17 | | 6 | 8 | | 7 | 10 | | 8 | 14 | | 9 | 8 | | 10 | 13 | | 11 | 16 | | 12 | 8 | | 13 | 3 | | 14 | 25 | | 15 | 5 | | 16 | 2 | | 17 | 4 | | 18 | 15 | | 19 | 12 | | 20 | 40 | | 21 | 16 | | 22 | 8 | | 23 | 28 | | 24 | 10 | | 25 | 3 | | 26 | 19 | | 27 | 18 | | 28 | 15 | | 29 | 6 | | 30 | 7 | | 31 | 7 | | 32 | 18 | | 33 | 9 | | 34 | 16 | | 35 | 14 | | 36 | 15 | | 37 | 3 | | 38 | 1 | | 39 | 18 | | 40 | 10 | | 41 | 28 | | 42 | 6 | | 43 | 18 | | 44 | 17 | | 45 | 34 | | 46 | 18 | | 47 | 17 | | 48 | 10 | | 49 | 9 |
| |
| 91.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5806451612903226 | | totalSentences | 93 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 62 | | matches | | 0 | "Her torch beam skated across" | | 1 | "He tipped his head toward" | | 2 | "His hands rested on his" | | 3 | "His head bowed." | | 4 | "His throat had been opened" | | 5 | "She picked at a drop" | | 6 | "It came up in layers," | | 7 | "She counted the roll-up ends" | | 8 | "Her thumb moved once across" | | 9 | "She turned a slow circle" | | 10 | "She turned it over" | | 11 | "She tucked a curl behind" | | 12 | "She turned it over" | | 13 | "She turned back to Eva" | | 14 | "She pulled off one glove," |
| | ratio | 0.242 | |
| 24.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 62 | | matches | | 0 | "Paraffin first, then wet hessian," | | 1 | "Her torch beam skated across" | | 2 | "A disused platform beneath Camden." | | 3 | "DS Reyes picked his way" | | 4 | "He tipped his head toward" | | 5 | "A man knelt against the" | | 6 | "His hands rested on his" | | 7 | "His head bowed." | | 8 | "His throat had been opened" | | 9 | "The wound gaped dry." | | 10 | "Quinn stood over him while" | | 11 | "Reyes crouched at the edge" | | 12 | "A double ring of chalk" | | 13 | "Quinn lowered herself to the" | | 14 | "Dust lifted and resettled, and" | | 15 | "She picked at a drop" | | 16 | "It came up in layers," | | 17 | "Reyes straightened, unbothered" | | 18 | "Quinn swept her torch along" | | 19 | "Chalk tallies ran in columns" |
| | ratio | 0.871 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 62 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "A man knelt against the last pillar as if he had stopped mid-prayer." |
| |
| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "DS Reyes picked, his paper suit hissing at every stride" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Reyes repeated (repeat)" |
| | dialogueSentences | 44 | | tagDensity | 0.023 | | leniency | 0.045 | | rawRatio | 1 | | effectiveRatio | 0.045 | |