| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1076 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 90.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1076 | | 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 | 80 | | matches | (empty) | |
| 53.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1081 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 779 | | uniqueNames | 10 | | maxNameDensity | 1.8 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 14 | | Camden | 1 | | Priya | 1 | | Ahmed | 10 | | Thatcher | 1 | | Reeve | 5 | | Maglite | 1 | | Swiss | 1 | | Daniel | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Priya" | | 2 | "Ahmed" | | 3 | "Thatcher" | | 4 | "Reeve" | | 5 | "Daniel" | | 6 | "Morris" |
| | places | | | globalScore | 0.601 | | windowScore | 0.667 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.925 | | wordCount | 1081 | | matches | | 0 | "not being funny, but I think you're looking for something" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 112 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 20.02 | | std | 21 | | cv | 1.049 | | sampleLengths | | 0 | 54 | | 1 | 19 | | 2 | 3 | | 3 | 55 | | 4 | 18 | | 5 | 40 | | 6 | 2 | | 7 | 4 | | 8 | 51 | | 9 | 11 | | 10 | 5 | | 11 | 14 | | 12 | 4 | | 13 | 10 | | 14 | 16 | | 15 | 34 | | 16 | 5 | | 17 | 4 | | 18 | 9 | | 19 | 57 | | 20 | 8 | | 21 | 20 | | 22 | 59 | | 23 | 3 | | 24 | 6 | | 25 | 3 | | 26 | 7 | | 27 | 40 | | 28 | 7 | | 29 | 6 | | 30 | 73 | | 31 | 4 | | 32 | 4 | | 33 | 5 | | 34 | 1 | | 35 | 14 | | 36 | 2 | | 37 | 1 | | 38 | 65 | | 39 | 6 | | 40 | 2 | | 41 | 23 | | 42 | 1 | | 43 | 39 | | 44 | 15 | | 45 | 44 | | 46 | 4 | | 47 | 1 | | 48 | 37 | | 49 | 6 |
| |
| 92.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 80 | | matches | | 0 | "been dragged" | | 1 | "been laid" | | 2 | "been swept" |
| |
| 95.83% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 128 | | matches | | 0 | "was looking" | | 1 | "was hiding" |
| |
| 66.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 112 | | ratio | 0.027 | | matches | | 0 | "She saw it then — a faint ring, maybe three metres across, where the dust lay differently." | | 1 | "She crouched again, and this time she smelled it properly — the church smell, the ozone and copper, concentrated to a point." | | 2 | "She started walking toward the metal door, torch steady, jaw tight, the smell of ozone thick in her throat and the name of her old partner — Morris, three years gone, no body, no answers — rising unbidden at the back of her mind like a tide she had long since stopped pretending not to feel." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 779 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.029525032092426188 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007702182284980745 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 9.65 | | std | 9.8 | | cv | 1.016 | | sampleLengths | | 0 | 23 | | 1 | 31 | | 2 | 1 | | 3 | 2 | | 4 | 16 | | 5 | 3 | | 6 | 21 | | 7 | 6 | | 8 | 3 | | 9 | 10 | | 10 | 15 | | 11 | 18 | | 12 | 40 | | 13 | 2 | | 14 | 4 | | 15 | 5 | | 16 | 16 | | 17 | 4 | | 18 | 4 | | 19 | 22 | | 20 | 5 | | 21 | 4 | | 22 | 2 | | 23 | 5 | | 24 | 14 | | 25 | 4 | | 26 | 10 | | 27 | 6 | | 28 | 10 | | 29 | 2 | | 30 | 11 | | 31 | 7 | | 32 | 11 | | 33 | 1 | | 34 | 2 | | 35 | 5 | | 36 | 4 | | 37 | 3 | | 38 | 6 | | 39 | 14 | | 40 | 11 | | 41 | 9 | | 42 | 23 | | 43 | 8 | | 44 | 8 | | 45 | 12 | | 46 | 14 | | 47 | 17 | | 48 | 2 | | 49 | 2 |
| |
| 63.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4107142857142857 | | totalSentences | 112 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 65 | | matches | | 0 | "She ducked beneath the blue-and-white" | | 1 | "His hands were clean." | | 2 | "His nails were clean." | | 3 | "She lifted his right hand." | | 4 | "His mouth hung open a" | | 5 | "She was looking at the" | | 6 | "Her own boots had already" | | 7 | "she said, and held her" | | 8 | "She saw it then —" | | 9 | "She moved the torch by" | | 10 | "Her knees clicked." | | 11 | "She walked the perimeter of" | | 12 | "She crouched again, and this" | | 13 | "She pulled her own sleeve" | | 14 | "She held them side by" | | 15 | "She started walking toward the" |
| | ratio | 0.246 | |
| 44.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 65 | | matches | | 0 | "The rain had stopped an" | | 1 | "She ducked beneath the blue-and-white" | | 2 | "Something underneath it that reminded" | | 3 | "DS Priya Ahmed stood beside" | | 4 | "A warehouse empty of everything" | | 5 | "Quinn pulled on gloves, the" | | 6 | "Quinn crouched beside the body." | | 7 | "Reeve wore a good suit," | | 8 | "His hands were clean." | | 9 | "His nails were clean." | | 10 | "A man this tidy didn't" | | 11 | "She lifted his right hand." | | 12 | "The fingers moved easily." | | 13 | "Quinn kept her voice flat" | | 14 | "The muscles of Reeve's face" | | 15 | "His mouth hung open a" | | 16 | "Quinn pressed two fingertips against" | | 17 | "Quinn didn't answer." | | 18 | "She was looking at the" | | 19 | "The concrete was grey and" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "She ducked beneath the blue-and-white tape, flashing her warrant card at the uniform whose name she couldn't be bothered to remember, and stepped into a smell t…" | | 1 | "DS Priya Ahmed stood beside a body that had been laid out with an almost ceremonial tidiness on the concrete floor." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |