| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said finally [finally]" |
| | dialogueSentences | 49 | | tagDensity | 0.367 | | leniency | 0.735 | | rawRatio | 0.056 | | effectiveRatio | 0.041 | |
| 86.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1526 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "lightly" | | 2 | "really" | | 3 | "carefully" |
| |
| 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) | |
| 31.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1526 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "silence" | | 1 | "etched" | | 2 | "glinting" | | 3 | "flicked" | | 4 | "measured" | | 5 | "coded" | | 6 | "traced" | | 7 | "clandestine" | | 8 | "jaw clenched" | | 9 | "whisper" | | 10 | "pulse" | | 11 | "quickened" | | 12 | "warmth" | | 13 | "depths" | | 14 | "echoed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 107 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1522 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1227 | | uniqueNames | 11 | | maxNameDensity | 0.73 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Carter | 1 | | Raven | 1 | | Nest | 1 | | London | 2 | | Golden | 1 | | Empress | 1 | | Silas | 9 | | Blackwood | 1 | | Prague | 2 | | Aurora | 7 | | Evan | 1 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Blackwood" | | 4 | "Aurora" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 48.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like a metonym for every battle he" | | 1 | "as if expecting the scar to speak for her" | | 2 | "quite reach her eyes" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1522 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 31.06 | | std | 18.03 | | cv | 0.581 | | sampleLengths | | 0 | 75 | | 1 | 55 | | 2 | 78 | | 3 | 82 | | 4 | 48 | | 5 | 42 | | 6 | 62 | | 7 | 15 | | 8 | 46 | | 9 | 14 | | 10 | 22 | | 11 | 18 | | 12 | 23 | | 13 | 51 | | 14 | 22 | | 15 | 29 | | 16 | 24 | | 17 | 35 | | 18 | 22 | | 19 | 23 | | 20 | 16 | | 21 | 41 | | 22 | 33 | | 23 | 42 | | 24 | 26 | | 25 | 8 | | 26 | 26 | | 27 | 44 | | 28 | 22 | | 29 | 15 | | 30 | 25 | | 31 | 11 | | 32 | 33 | | 33 | 40 | | 34 | 13 | | 35 | 21 | | 36 | 38 | | 37 | 7 | | 38 | 9 | | 39 | 41 | | 40 | 12 | | 41 | 34 | | 42 | 20 | | 43 | 18 | | 44 | 16 | | 45 | 29 | | 46 | 50 | | 47 | 9 | | 48 | 37 |
| |
| 92.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 107 | | matches | | 0 | "were papered" | | 1 | "was buried" | | 2 | "been sealed" | | 3 | "was lost" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 214 | | matches | (empty) | |
| 15.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 135 | | ratio | 0.044 | | matches | | 0 | "She drew in a breath of London night—smoke, rain, distant sirens—and let the door’s bell ring out, a sharp note that cut through her hesitation." | | 1 | "His hazel eyes flicked up, and recognition loosened something in both of them—a shuttered door creaking on rusty hinges." | | 2 | "His gaze drifted to the row of photographs overhead: a black-and-white snapshot of a younger Silas, in Prague, flanked by armored cars; the picture beside it, shows Aurora, nearly a girl, eyes bright with fascination as she watched him demonstrate a dead drop." | | 3 | "It burned a little—like regret." | | 4 | "The fear of her ex—Evan—had driven her into the arms of night buses and narrow London alleys." | | 5 | "Aurora met his gaze, and in those hazel depths she saw the years roll back—the late-night stakeouts, the cipher puzzles sprawled across his dining table, her bright eyes lit with possibility." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1240 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.02903225806451613 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.008870967741935484 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 11.27 | | std | 7.97 | | cv | 0.707 | | sampleLengths | | 0 | 23 | | 1 | 25 | | 2 | 27 | | 3 | 18 | | 4 | 20 | | 5 | 17 | | 6 | 14 | | 7 | 15 | | 8 | 7 | | 9 | 32 | | 10 | 10 | | 11 | 6 | | 12 | 21 | | 13 | 24 | | 14 | 14 | | 15 | 17 | | 16 | 9 | | 17 | 20 | | 18 | 19 | | 19 | 13 | | 20 | 29 | | 21 | 3 | | 22 | 14 | | 23 | 29 | | 24 | 16 | | 25 | 12 | | 26 | 3 | | 27 | 5 | | 28 | 9 | | 29 | 18 | | 30 | 14 | | 31 | 3 | | 32 | 11 | | 33 | 15 | | 34 | 7 | | 35 | 5 | | 36 | 9 | | 37 | 4 | | 38 | 2 | | 39 | 12 | | 40 | 9 | | 41 | 43 | | 42 | 8 | | 43 | 9 | | 44 | 6 | | 45 | 7 | | 46 | 9 | | 47 | 14 | | 48 | 3 | | 49 | 3 |
| |
| 44.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3037037037037037 | | totalSentences | 135 | | uniqueOpeners | 41 | |
| 33.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 100 | | matches | | 0 | "Then, softer, almost to himself" |
| | ratio | 0.01 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 60 | | totalSentences | 100 | | matches | | 0 | "She drew in a breath" | | 1 | "She smoothed a strand of" | | 2 | "She had avoided this place" | | 3 | "She had crafted reasons, wove" | | 4 | "He was slightly hunched, leaning" | | 5 | "His left leg bore a" | | 6 | "He poured a measure of" | | 7 | "She froze, midway through lowering" | | 8 | "His hazel eyes flicked up," | | 9 | "he said, voice low, as" | | 10 | "He set the glass down" | | 11 | "He looked older." | | 12 | "She barely recognized the man" | | 13 | "he said finally, rubbing his" | | 14 | "she corrected, voice soft" | | 15 | "She traced a pattern on" | | 16 | "Her fingers brushed her left" | | 17 | "She held her palm open" | | 18 | "She tucked a lock of" | | 19 | "He inclined his head, considering." |
| | ratio | 0.6 | |
| 5.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 100 | | matches | | 0 | "Aurora Carter paused beneath the" | | 1 | "She drew in a breath" | | 2 | "The bar fell silent for" | | 3 | "The air smelled of oiled" | | 4 | "A single bare bulb swung" | | 5 | "Aurora’s heel clicked once on" | | 6 | "She smoothed a strand of" | | 7 | "She had avoided this place" | | 8 | "She had crafted reasons, wove" | | 9 | "He was slightly hunched, leaning" | | 10 | "A scatter of grey had" | | 11 | "His left leg bore a" | | 12 | "He poured a measure of" | | 13 | "She froze, midway through lowering" | | 14 | "The world narrowed to the" | | 15 | "His hazel eyes flicked up," | | 16 | "he said, voice low, as" | | 17 | "He set the glass down" | | 18 | "He looked older." | | 19 | "Authority draped over his shoulders" |
| | ratio | 0.91 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 97.26% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 3 | | matches | | 0 | "He was slightly hunched, leaning on a forearm that bore a silver signet ring catching the light, its etched raven glinting." | | 1 | "Silas’s brows rose, as if he found that fact delivered with delicious irony." | | 2 | "For the first time he reached out, fingertips brushing the crescent mark, as if it held all the answers." |
| |
| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 4 | | matches | | 0 | "he said, voice low, as if the walls themselves demanded discretion" | | 1 | "she corrected, voice soft" | | 2 | "she said, but the admission scraped raw" | | 3 | "she admitted, voice soft" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 2 | | fancyTags | | 0 | "she corrected (correct)" | | 1 | "she admitted (admit)" |
| | dialogueSentences | 49 | | tagDensity | 0.245 | | leniency | 0.49 | | rawRatio | 0.167 | | effectiveRatio | 0.082 | |