| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1796 | | 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) | |
| 33.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1796 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "gloom" | | 1 | "perfect" | | 2 | "scanned" | | 3 | "etched" | | 4 | "warmth" | | 5 | "pulsed" | | 6 | "weight" | | 7 | "silence" | | 8 | "familiar" | | 9 | "standard" | | 10 | "echoed" | | 11 | "pristine" | | 12 | "shimmered" | | 13 | "vibrated" | | 14 | "quickened" | | 15 | "pulse" | | 16 | "footsteps" | | 17 | "whisper" | | 18 | "aligned" |
| |
| 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 | 215 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 215 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "began to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 215 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1796 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1796 | | uniqueNames | 15 | | maxNameDensity | 0.67 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Tube | 1 | | Camden | 3 | | Sergeant | 1 | | Kline | 8 | | London | 2 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Underground | 1 | | Shade | 1 | | Doctor | 1 | | Morris | 3 | | Eva | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Kline" | | 4 | "Kowalski" | | 5 | "Museum" | | 6 | "Morris" | | 7 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "London" | | 2 | "British" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 153 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like fossilized bone hung from wir" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1796 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 215 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 39.91 | | std | 21.25 | | cv | 0.533 | | sampleLengths | | 0 | 90 | | 1 | 56 | | 2 | 22 | | 3 | 26 | | 4 | 60 | | 5 | 47 | | 6 | 7 | | 7 | 21 | | 8 | 37 | | 9 | 76 | | 10 | 61 | | 11 | 57 | | 12 | 52 | | 13 | 13 | | 14 | 33 | | 15 | 71 | | 16 | 61 | | 17 | 31 | | 18 | 29 | | 19 | 24 | | 20 | 67 | | 21 | 18 | | 22 | 29 | | 23 | 56 | | 24 | 46 | | 25 | 55 | | 26 | 10 | | 27 | 27 | | 28 | 61 | | 29 | 8 | | 30 | 35 | | 31 | 57 | | 32 | 9 | | 33 | 42 | | 34 | 62 | | 35 | 29 | | 36 | 8 | | 37 | 25 | | 38 | 67 | | 39 | 7 | | 40 | 26 | | 41 | 46 | | 42 | 49 | | 43 | 60 | | 44 | 23 |
| |
| 98.74% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 215 | | matches | | 0 | "was made" | | 1 | "was, operated" | | 2 | "been moved" | | 3 | "been arranged" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 309 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 215 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1801 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.022765130483064965 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.006662965019433648 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 215 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 215 | | mean | 8.35 | | std | 4.54 | | cv | 0.544 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 18 | | 3 | 6 | | 4 | 13 | | 5 | 11 | | 6 | 5 | | 7 | 13 | | 8 | 6 | | 9 | 14 | | 10 | 1 | | 11 | 1 | | 12 | 7 | | 13 | 6 | | 14 | 8 | | 15 | 10 | | 16 | 12 | | 17 | 3 | | 18 | 8 | | 19 | 2 | | 20 | 4 | | 21 | 9 | | 22 | 4 | | 23 | 7 | | 24 | 14 | | 25 | 7 | | 26 | 6 | | 27 | 22 | | 28 | 5 | | 29 | 9 | | 30 | 4 | | 31 | 5 | | 32 | 12 | | 33 | 4 | | 34 | 8 | | 35 | 7 | | 36 | 10 | | 37 | 11 | | 38 | 7 | | 39 | 8 | | 40 | 7 | | 41 | 9 | | 42 | 5 | | 43 | 1 | | 44 | 10 | | 45 | 18 | | 46 | 7 | | 47 | 13 | | 48 | 9 | | 49 | 11 |
| |
| 57.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3767441860465116 | | totalSentences | 215 | | uniqueOpeners | 81 | |
| 50.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 200 | | matches | | 0 | "Instead they remained pristine." | | 1 | "Even the wound itself carried" | | 2 | "All spoke the same sentence." |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 200 | | matches | | 0 | "She straightened her jacket with" | | 1 | "Her boots crunched over scattered" | | 2 | "He wiped his notepad on" | | 3 | "She pulled on nitrile gloves" | | 4 | "She scanned the immediate area." | | 5 | "She picked it up." | | 6 | "His shadow stretched long across" | | 7 | "She rose, knees cracking in" | | 8 | "Her worn leather watch ticked" | | 9 | "She moved to the nearest" | | 10 | "She tucked a strand behind" | | 11 | "It should have sprayed given" | | 12 | "She circled the body once," | | 13 | "She studied the angle of" | | 14 | "She lifted one parchment anyway." | | 15 | "She pried it free." | | 16 | "It now pointed directly at" | | 17 | "She needed answers first." | | 18 | "Her green eyes scanned the" | | 19 | "Its needle detects rifts, places" |
| | ratio | 0.215 | |
| 60.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 160 | | totalSentences | 200 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "Dank air pressed against her" | | 2 | "Police floodlights buzzed on the" | | 3 | "These were no ordinary vendor" | | 4 | "Vials of luminous fluid sat" | | 5 | "Amulets carved from what looked" | | 6 | "The whole setup screamed wrongness." | | 7 | "She straightened her jacket with" | | 8 | "Her boots crunched over scattered" | | 9 | "The body lay ten meters" | | 10 | "A single wound pierced his" | | 11 | "Blood pooled beneath him in" | | 12 | "Detective Sergeant Kline rose from" | | 13 | "He wiped his notepad on" | | 14 | "Victim probably wandered down here" | | 15 | "Blade to the heart." | | 16 | "Perp likely bolted back up" | | 17 | "Quinn ignored the summary." | | 18 | "She pulled on nitrile gloves" | | 19 | "The wound edges looked seared," |
| | ratio | 0.8 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 200 | | matches | | 0 | "While you chase fairy tales," | | 1 | "If the victim had been" | | 2 | "If the needle locks direction," | | 3 | "If the clique you have" | | 4 | "If the rift is still" |
| | ratio | 0.025 | |
| 66.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 75 | | technicalSentenceCount | 8 | | matches | | 0 | "Dank air pressed against her skin, thick with mildew and the faint metallic tang that lingered after lightning strikes." | | 1 | "Police floodlights buzzed on the old platform, their beams slicing across makeshift stalls that stretched into the gloom." | | 2 | "Ink shimmered, revealing hidden lines that formed a map of tunnels that did not match any official London Underground schematics." | | 3 | "Inside one pocket she found residue that glittered like ground obsidian." | | 4 | "Something that left the taste of copper on her tongue." | | 5 | "This was theater staged for investigators who would arrive expecting normal crime." | | 6 | "Air currents moved in ways that defied the station's sealed environment." | | 7 | "The last researcher who tried never returned with all his memories intact." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |