| 18.18% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said casually [casually]" | | 1 | "she said brusquely [brusquely]" |
| | dialogueSentences | 20 | | tagDensity | 0.55 | | leniency | 1 | | rawRatio | 0.182 | | effectiveRatio | 0.182 | |
| 86.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 755 | | totalAiIsmAdverbs | 2 | | 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) | |
| 33.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 755 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "spectral" | | 1 | "navigated" | | 2 | "etched" | | 3 | "mosaic" | | 4 | "standard" | | 5 | "depths" | | 6 | "scanned" | | 7 | "tension" | | 8 | "familiar" | | 9 | "comforting" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 37 | | matches | (empty) | |
| 65.64% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 37 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 754 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.39% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 598 | | uniqueNames | 14 | | maxNameDensity | 1.67 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 10 | | Quinn | 3 | | Veil | 1 | | Market | 1 | | Eva | 5 | | Kowalski | 1 | | Aurora | 1 | | Lovecraftian | 1 | | Detective | 4 | | Constable | 2 | | Morris | 2 | | Tomlinson | 4 | | Camden | 1 | | Town | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Aurora" | | 5 | "Detective" | | 6 | "Constable" | | 7 | "Morris" | | 8 | "Tomlinson" |
| | places | | | globalScore | 0.664 | | windowScore | 0.833 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 29 | | glossingSentenceCount | 1 | | matches | | 0 | "as if choosing her words with the deepest precision" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 754 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 26.93 | | std | 16.34 | | cv | 0.607 | | sampleLengths | | 0 | 65 | | 1 | 39 | | 2 | 47 | | 3 | 22 | | 4 | 31 | | 5 | 20 | | 6 | 44 | | 7 | 22 | | 8 | 23 | | 9 | 17 | | 10 | 11 | | 11 | 21 | | 12 | 18 | | 13 | 3 | | 14 | 32 | | 15 | 10 | | 16 | 20 | | 17 | 16 | | 18 | 33 | | 19 | 13 | | 20 | 11 | | 21 | 36 | | 22 | 36 | | 23 | 40 | | 24 | 11 | | 25 | 40 | | 26 | 69 | | 27 | 4 |
| |
| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 37 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 91 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 45 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 371 | | adjectiveStacks | 1 | | stackExamples | | 0 | "missing under strange like" |
| | adverbCount | 13 | | adverbRatio | 0.03504043126684636 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.018867924528301886 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 16.76 | | std | 13.18 | | cv | 0.786 | | sampleLengths | | 0 | 26 | | 1 | 12 | | 2 | 27 | | 3 | 21 | | 4 | 18 | | 5 | 25 | | 6 | 13 | | 7 | 9 | | 8 | 5 | | 9 | 11 | | 10 | 6 | | 11 | 22 | | 12 | 9 | | 13 | 8 | | 14 | 12 | | 15 | 10 | | 16 | 34 | | 17 | 5 | | 18 | 17 | | 19 | 6 | | 20 | 17 | | 21 | 11 | | 22 | 2 | | 23 | 4 | | 24 | 5 | | 25 | 6 | | 26 | 21 | | 27 | 18 | | 28 | 3 | | 29 | 24 | | 30 | 8 | | 31 | 2 | | 32 | 8 | | 33 | 20 | | 34 | 16 | | 35 | 33 | | 36 | 13 | | 37 | 11 | | 38 | 36 | | 39 | 36 | | 40 | 40 | | 41 | 11 | | 42 | 40 | | 43 | 69 | | 44 | 4 |
| |
| 91.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5777777777777777 | | totalSentences | 45 | | uniqueOpeners | 26 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 33 | | matches | | 0 | "Almost. But not quite." |
| | ratio | 0.03 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 33 | | matches | | 0 | "She navigated between stalls laden" | | 1 | "Its needle spun erratically, seeking" | | 2 | "Her emerald eyes reflected the" | | 3 | "He gave a perfunctory nod" | | 4 | "She followed him to the" | | 5 | "She kept her gaze trained" | | 6 | "She stood and brushed off" | | 7 | "They aren't present anywhere else" | | 8 | "Her keen eyes scanned the" | | 9 | "he said casually, but Harlow" | | 10 | "She needed to get out" | | 11 | "she said brusquely, turning on" |
| | ratio | 0.364 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 33 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The musty air pressed against" | | 2 | "She navigated between stalls laden" | | 3 | "Harlow nodded to her companion," | | 4 | "Eva Kowalski, an occult librarian" | | 5 | "Harlow's gaze landed on a" | | 6 | "A brass compass caught her" | | 7 | "Its needle spun erratically, seeking" | | 8 | "Her emerald eyes reflected the" | | 9 | "A pair of boots tramped" | | 10 | "The man introduced himself as" | | 11 | "He gave a perfunctory nod" | | 12 | "Harlow Quinn shot him a" | | 13 | "She followed him to the" | | 14 | "Tomlinson waved at the symbols." | | 15 | "Eva peered closer, studying the" | | 16 | "The detective crouched down and" | | 17 | "A recent scuffle, then?" | | 18 | "Harlow exhaled from her nostrils" | | 19 | "She kept her gaze trained" |
| | ratio | 0.97 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 33 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn stepped off the tube platform into the Veil Market, an illegal supernatural bazaar that moved to a new spectral location every full moon." | | 1 | "Harlow exhaled from her nostrils in a soft huff, then spoke slowly, as if choosing her words with the deepest precision." | | 2 | "The abandoned tube station gave way to the familiar streets of Camden Town as they emerged into the early morning light. Harlow couldn't help but let out a smal…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 20 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.167 | | effectiveRatio | 0.1 | |