| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva called softly [softly]" |
| | dialogueSentences | 50 | | tagDensity | 0.26 | | leniency | 0.52 | | rawRatio | 0.077 | | effectiveRatio | 0.04 | |
| 72.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1470 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "carefully" | | 1 | "softly" | | 2 | "slightly" | | 3 | "slowly" | | 4 | "really" |
| |
| 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) | |
| 45.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1470 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulse" | | 1 | "fragmented" | | 2 | "whisper" | | 3 | "echoing" | | 4 | "flicker" | | 5 | "shimmered" | | 6 | "etched" | | 7 | "intricate" | | 8 | "magnetic" | | 9 | "raced" | | 10 | "quivered" | | 11 | "could feel" |
| |
| 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 | 136 | | matches | (empty) | |
| 79.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 136 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 173 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 17 | | totalWords | 1454 | | ratio | 0.012 | | matches | | 0 | "They say the black markets move with the moon." | | 1 | "into" | | 2 | "mark" | | 3 | "‘Aether’s Gate.’" | | 4 | "sees" | | 5 | "Eva" | | 6 | "proximity" | | 7 | "Witnesses" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 951 | | uniqueNames | 12 | | maxNameDensity | 2.21 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 21 | | Camden | 1 | | Metropolitan | 1 | | Police | 1 | | Research | 1 | | British | 1 | | Museum | 1 | | Eva | 13 | | Latin | 1 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Eva" | | 4 | "Morris" |
| | places | | | globalScore | 0.396 | | windowScore | 0.333 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like Latin script, was a small bra" | | 1 | "sigils that seemed to shift under her gaze" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.688 | | wordCount | 1454 | | matches | | 0 | "not along the tracks, but *into* the wall" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 173 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 20.77 | | std | 19.77 | | cv | 0.952 | | sampleLengths | | 0 | 101 | | 1 | 62 | | 2 | 57 | | 3 | 2 | | 4 | 73 | | 5 | 21 | | 6 | 3 | | 7 | 16 | | 8 | 6 | | 9 | 3 | | 10 | 32 | | 11 | 46 | | 12 | 5 | | 13 | 7 | | 14 | 52 | | 15 | 6 | | 16 | 19 | | 17 | 22 | | 18 | 58 | | 19 | 14 | | 20 | 4 | | 21 | 7 | | 22 | 33 | | 23 | 2 | | 24 | 42 | | 25 | 7 | | 26 | 7 | | 27 | 6 | | 28 | 27 | | 29 | 4 | | 30 | 4 | | 31 | 49 | | 32 | 26 | | 33 | 2 | | 34 | 20 | | 35 | 54 | | 36 | 6 | | 37 | 10 | | 38 | 11 | | 39 | 8 | | 40 | 39 | | 41 | 19 | | 42 | 25 | | 43 | 6 | | 44 | 22 | | 45 | 2 | | 46 | 27 | | 47 | 42 | | 48 | 4 | | 49 | 16 |
| |
| 97.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 136 | | matches | | 0 | "been decommissioned" | | 1 | "been called" | | 2 | "were fixed" | | 3 | "was clenched" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 170 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 173 | | ratio | 0.064 | | matches | | 0 | "The air in the abandoned Tube station was thick with the smell of damp stone, old wiring, and something else—something metallic, faintly sweet, like blood left too long in a sealed room." | | 1 | "A civilian—missing for three days—found slumped against a tiled wall, eyes wide, mouth slightly open, as if frozen mid-sentence." | | 2 | "A single cigarette butt—unlit—crushed near the tracks." | | 3 | "Freckles dusted her nose and cheeks, and as Quinn watched, the woman tucked a lock of hair behind her left ear—once, then again." | | 4 | "The left—palm up—revealed a tiny, almost invisible mark." | | 5 | "The schedule in her mind clicked—a fragmented memory from an old case file, something about movements in the underground." | | 6 | "The tunnel curved ahead, but there, just beyond, she saw a faint flicker—like candlelight." | | 7 | "It spun slowly, then settled—pointing not along the tracks, but *into* the wall." | | 8 | "The way his watch had stopped at exactly 11:03—same as the victim’s phone, timestamped in the report." | | 9 | "But the compass in her pocket—she could feel it, humming faintly against her thigh, like a second pulse." | | 10 | "Inside the breast pocket—nothing." |
| |
| 95.14% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 966 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.045548654244306416 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.016563146997929608 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 173 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 173 | | mean | 8.4 | | std | 7.81 | | cv | 0.929 | | sampleLengths | | 0 | 32 | | 1 | 31 | | 2 | 18 | | 3 | 12 | | 4 | 8 | | 5 | 4 | | 6 | 2 | | 7 | 26 | | 8 | 19 | | 9 | 3 | | 10 | 4 | | 11 | 1 | | 12 | 1 | | 13 | 2 | | 14 | 12 | | 15 | 4 | | 16 | 10 | | 17 | 16 | | 18 | 7 | | 19 | 2 | | 20 | 2 | | 21 | 4 | | 22 | 2 | | 23 | 7 | | 24 | 2 | | 25 | 18 | | 26 | 23 | | 27 | 23 | | 28 | 9 | | 29 | 6 | | 30 | 6 | | 31 | 3 | | 32 | 5 | | 33 | 11 | | 34 | 2 | | 35 | 4 | | 36 | 3 | | 37 | 7 | | 38 | 25 | | 39 | 3 | | 40 | 8 | | 41 | 8 | | 42 | 7 | | 43 | 1 | | 44 | 8 | | 45 | 2 | | 46 | 1 | | 47 | 1 | | 48 | 4 | | 49 | 3 |
| |
| 49.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3352601156069364 | | totalSentences | 173 | | uniqueOpeners | 58 | |
| 33.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 101 | | matches | | 0 | "Then, beneath the lining, a" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 101 | | matches | | 0 | "Her leather watch ticked softly" | | 1 | "It had saved her life" | | 2 | "Her gaze swept the scene:" | | 3 | "She wore round glasses, their" | | 4 | "Her eyes were fixed on" | | 5 | "She pried it open with" | | 6 | "She followed it." | | 7 | "She’d followed, satchel still clutched" | | 8 | "She picked it up." | | 9 | "It spun slowly, then settled—pointing" | | 10 | "She held up the compass" | | 11 | "She remembered DS Morris’s last" | | 12 | "They’d found his body three" | | 13 | "she said, voice low" | | 14 | "She turned the compass over." | | 15 | "She thought of Morris’s empty" | | 16 | "She saw patterns." | | 17 | "She held the compass steady." | | 18 | "Her breath caught." | | 19 | "She pocketed the compass." |
| | ratio | 0.238 | |
| 73.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 101 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "Her leather watch ticked softly" | | 3 | "It had saved her life" | | 4 | "This place shouldn’t exist." | | 5 | "The Camden line had been" | | 6 | "A civilian—missing for three days—found" | | 7 | "Quinn crouched beside the body," | | 8 | "Rigor hadn’t fully set." | | 9 | "Time of death estimated between" | | 10 | "Her gaze swept the scene:" | | 11 | "A single cigarette butt—unlit—crushed near" | | 12 | "Nothing to identify him." | | 13 | "The voice came from behind," | | 14 | "A young woman stood a" | | 15 | "She wore round glasses, their" | | 16 | "Freckles dusted her nose and" | | 17 | "Quinn said, recognizing her from" | | 18 | "A uniformed officer coughed, glancing" | | 19 | "Quinn didn’t answer." |
| | ratio | 0.772 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 101 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn stepped carefully over a coil of frayed cable, her polished boots pressing into a puddle that reflected the sputtering glow of the emerge…" | | 1 | "A civilian—missing for three days—found slumped against a tiled wall, eyes wide, mouth slightly open, as if frozen mid-sentence." | | 2 | "The space opened into a wider chamber, walls tiled in faded cream and green, but the air shimmered there, warping slightly, as if heat rose from invisible grate…" | | 3 | "The casing was green with verdigris, the face etched with intricate sigils that seemed to shift under her gaze." |
| |
| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 50 | | tagDensity | 0.18 | | leniency | 0.36 | | rawRatio | 0.111 | | effectiveRatio | 0.04 | |