| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1330 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 66.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1330 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "velvet" | | 1 | "etched" | | 2 | "standard" | | 3 | "scanning" | | 4 | "weight" | | 5 | "familiar" | | 6 | "unreadable" | | 7 | "flicked" |
| |
| 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 | 51 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 51 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | 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 | 1324 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 886 | | uniqueNames | 8 | | maxNameDensity | 0.9 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Morris | 3 | | Camden | 1 | | Tube | 2 | | Javi | 1 | | Tolliver | 4 | | East | 1 | | London | 1 | | Harlow | 8 |
| | persons | | 0 | "Morris" | | 1 | "Tube" | | 2 | "Tolliver" | | 3 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1324 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 33.95 | | std | 32.5 | | cv | 0.957 | | sampleLengths | | 0 | 80 | | 1 | 116 | | 2 | 27 | | 3 | 25 | | 4 | 85 | | 5 | 75 | | 6 | 10 | | 7 | 5 | | 8 | 17 | | 9 | 107 | | 10 | 115 | | 11 | 12 | | 12 | 9 | | 13 | 3 | | 14 | 55 | | 15 | 15 | | 16 | 5 | | 17 | 16 | | 18 | 17 | | 19 | 17 | | 20 | 68 | | 21 | 9 | | 22 | 18 | | 23 | 5 | | 24 | 18 | | 25 | 63 | | 26 | 14 | | 27 | 32 | | 28 | 12 | | 29 | 54 | | 30 | 8 | | 31 | 29 | | 32 | 77 | | 33 | 13 | | 34 | 27 | | 35 | 10 | | 36 | 17 | | 37 | 11 | | 38 | 28 |
| |
| 91.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 51 | | matches | | 0 | "were calloused" | | 1 | "been taken" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 68 | | ratio | 0.059 | | matches | | 0 | "The tile beneath his neck held only a thin, dark puddle—no spatter splattered the brick walls, no smudges stained the table’s edge, no drips tracked toward the station’s far exit." | | 1 | "The rain only seeped through the ceiling’s cracked panels, pooling in the corners; it hadn’t touched the victim’s body at all." | | 2 | "That didn’t fit a standard gang hit—most killers grabbed whatever was easy to fence." | | 3 | "The speaker stepped from behind a cast-iron pillar, curly red hair tucked behind her left ear—her nervous habit, Harlow noted instantly—round glasses slipping down a freckled nose." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 898 | | adjectiveStacks | 1 | | stackExamples | | 0 | "pillar, curly red hair" |
| | adverbCount | 25 | | adverbRatio | 0.02783964365256125 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.011135857461024499 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 19.47 | | std | 11.35 | | cv | 0.583 | | sampleLengths | | 0 | 21 | | 1 | 30 | | 2 | 29 | | 3 | 21 | | 4 | 23 | | 5 | 21 | | 6 | 30 | | 7 | 21 | | 8 | 20 | | 9 | 7 | | 10 | 25 | | 11 | 15 | | 12 | 21 | | 13 | 29 | | 14 | 6 | | 15 | 14 | | 16 | 8 | | 17 | 26 | | 18 | 11 | | 19 | 14 | | 20 | 16 | | 21 | 10 | | 22 | 5 | | 23 | 17 | | 24 | 15 | | 25 | 25 | | 26 | 38 | | 27 | 29 | | 28 | 13 | | 29 | 20 | | 30 | 8 | | 31 | 30 | | 32 | 28 | | 33 | 16 | | 34 | 12 | | 35 | 9 | | 36 | 3 | | 37 | 11 | | 38 | 27 | | 39 | 17 | | 40 | 15 | | 41 | 5 | | 42 | 16 | | 43 | 17 | | 44 | 17 | | 45 | 68 | | 46 | 9 | | 47 | 18 | | 48 | 5 | | 49 | 18 |
| |
| 65.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4264705882352941 | | totalSentences | 68 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 78.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 51 | | matches | | 0 | "She brushed a stray strand" | | 1 | "His throat bore a single," | | 2 | "He looked up when she" | | 3 | "His coat pockets held a" | | 4 | "She stood, scanning the table" | | 5 | "Its casing bore a thick" | | 6 | "He tapped the table’s edge." | | 7 | "She turned the compass in" | | 8 | "She walked ten feet toward" | | 9 | "She shone her flashlight on" | | 10 | "It was the same hum" | | 11 | "She held out a hand." | | 12 | "She tucked another strand of" | | 13 | "She turned back to the" | | 14 | "She nodded at the body," | | 15 | "It was precise, almost surgical," | | 16 | "She pulled out her notepad," | | 17 | "She tapped the iridescent crack" |
| | ratio | 0.353 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 51 | | matches | | 0 | "Harlow’s boots scuffed the cracked" | | 1 | "She brushed a stray strand" | | 2 | "The abandoned Camden Tube station" | | 3 | "His throat bore a single," | | 4 | "The tile beneath his neck" | | 5 | "The rain only seeped through" | | 6 | "DC Javi Tolliver leaned against" | | 7 | "He looked up when she" | | 8 | "Harlow knelt, keeping her gloved" | | 9 | "The man’s hands were calloused," | | 10 | "His coat pockets held a" | | 11 | "That didn’t fit a standard" | | 12 | "She stood, scanning the table" | | 13 | "A stack of polished river" | | 14 | "Its casing bore a thick" | | 15 | "The needle spun wildly, not" | | 16 | "He tapped the table’s edge." | | 17 | "Harlow picked up the compass," | | 18 | "The weight of it in" | | 19 | "The sigils around its face" |
| | ratio | 0.922 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "Yellow police tape strung between cast-iron support pillars hung in tatters, knocked askew by someone who didn’t care about departmental protocol." | | 1 | "A faint, iridescent crack ran through the mortar, almost invisible unless you leaned in close, its edges glowing with a soft, blue light that matched the vial o…" | | 2 | "She pulled out her notepad, scribbling down the details of the compass, the rift, the missing loot that hadn’t been taken, the clean, precise cut." |
| |
| 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 | |