| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 67.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1080 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "very" | | 1 | "truly" | | 2 | "slightly" | | 3 | "gently" |
| |
| 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) | |
| 39.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1080 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "silence" | | 1 | "weight" | | 2 | "pulse" | | 3 | "pulsed" | | 4 | "footsteps" | | 5 | "echo" | | 6 | "warmth" |
| |
| 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 | 83 | | matches | (empty) | |
| 91.22% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 38 | | totalWords | 1084 | | ratio | 0.035 | | matches | | 0 | "Past the old oaks. When the flowers don't make sense, you're close." | | 1 | "Turn around. Go home. Whatever Eva wants can wait for daylight." | | 2 | "There is a rational explanation. There is always a rational explanation." | | 3 | "Time moves differently here." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1064 | | uniqueNames | 10 | | maxNameDensity | 0.66 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Richmond | 1 | | Park | 1 | | London | 1 | | Eva | 6 | | October | 1 | | May | 1 | | Cardiff | 2 | | Evan | 1 | | Rory | 7 | | Heartstone | 3 |
| | persons | | 0 | "Eva" | | 1 | "Evan" | | 2 | "Rory" | | 3 | "Heartstone" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | 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 | 1084 | | matches | (empty) | |
| 88.24% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 85 | | matches | | 0 | "used that knowledge" | | 1 | "written that note" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 37.38 | | std | 31.78 | | cv | 0.85 | | sampleLengths | | 0 | 11 | | 1 | 70 | | 2 | 5 | | 3 | 94 | | 4 | 10 | | 5 | 66 | | 6 | 53 | | 7 | 37 | | 8 | 6 | | 9 | 99 | | 10 | 17 | | 11 | 2 | | 12 | 86 | | 13 | 14 | | 14 | 72 | | 15 | 13 | | 16 | 4 | | 17 | 71 | | 18 | 39 | | 19 | 98 | | 20 | 17 | | 21 | 48 | | 22 | 44 | | 23 | 6 | | 24 | 42 | | 25 | 5 | | 26 | 39 | | 27 | 4 | | 28 | 12 |
| |
| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "been pressed" | | 1 | "was gone" | | 2 | "been was" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 169 | | matches | | 0 | "was wearing" | | 1 | "was scorching" |
| |
| 42.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 85 | | ratio | 0.035 | | matches | | 0 | "Not the ordinary silence of a park at night, which was never truly silent — there was always the hum of the distant city, the drone of an airplane, the rustle of small living things in the undergrowth." | | 1 | "The clearing was larger inside than it had looked from the edge — that was her first concrete wrongness, the kind she could name." | | 2 | "Toward the far trees, where the path out — if there was a path out — had to be." |
| |
| 96.49% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1068 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.04400749063670412 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.011235955056179775 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 12.75 | | std | 11.05 | | cv | 0.866 | | sampleLengths | | 0 | 11 | | 1 | 24 | | 2 | 24 | | 3 | 10 | | 4 | 4 | | 5 | 8 | | 6 | 5 | | 7 | 3 | | 8 | 20 | | 9 | 42 | | 10 | 29 | | 11 | 10 | | 12 | 1 | | 13 | 38 | | 14 | 4 | | 15 | 8 | | 16 | 15 | | 17 | 4 | | 18 | 38 | | 19 | 2 | | 20 | 2 | | 21 | 7 | | 22 | 8 | | 23 | 23 | | 24 | 6 | | 25 | 6 | | 26 | 7 | | 27 | 28 | | 28 | 28 | | 29 | 15 | | 30 | 2 | | 31 | 2 | | 32 | 17 | | 33 | 5 | | 34 | 12 | | 35 | 2 | | 36 | 24 | | 37 | 17 | | 38 | 17 | | 39 | 6 | | 40 | 22 | | 41 | 7 | | 42 | 7 | | 43 | 16 | | 44 | 16 | | 45 | 4 | | 46 | 6 | | 47 | 30 | | 48 | 13 | | 49 | 4 |
| |
| 55.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4117647058823529 | | totalSentences | 85 | | uniqueOpeners | 35 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "Then something moved at the" | | 1 | "Then a third, behind her," |
| | ratio | 0.028 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 72 | | matches | | 0 | "She'd kept going because Eva's" | | 1 | "It was October." | | 2 | "It pressed against her ears" | | 3 | "She could hear her own" | | 4 | "She should turn around." | | 5 | "She'd nearly forgotten she was" | | 6 | "She'd worn it because it" | | 7 | "Her voice didn't carry" | | 8 | "She told herself it was" | | 9 | "She told herself a lot" | | 10 | "She spun toward it." | | 11 | "She knew about adrenaline." | | 12 | "She knew about the body's" | | 13 | "She had used that knowledge" | | 14 | "She summoned it now, marshalled" | | 15 | "She started walking faster." | | 16 | "It hung fat and white" | | 17 | "It had been low then." | | 18 | "She tried to fix the" | | 19 | "Her heart slammed." |
| | ratio | 0.333 | |
| 78.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 72 | | matches | | 0 | "The standing stones came up" | | 1 | "Rory had walked Richmond Park" | | 2 | "The path she'd followed had" | | 3 | "She'd kept going because Eva's" | | 4 | "The flowers didn't make sense." | | 5 | "It was October." | | 6 | "The cold had crept into" | | 7 | "The blossoms glowed faintly in" | | 8 | "Rory stopped at the threshold" | | 9 | "This silence had weight." | | 10 | "It pressed against her ears" | | 11 | "She could hear her own" | | 12 | "She should turn around." | | 13 | "That thought arrived calm and" | | 14 | "Whatever Eva wants can wait" | | 15 | "The pendant warmed against her" | | 16 | "She'd nearly forgotten she was" | | 17 | "The Heartstone had come to" | | 18 | "She'd worn it because it" | | 19 | "A faint, insistent pulse, like" |
| | ratio | 0.764 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 72 | | matches | | 0 | "Now it lay against her" | | 1 | "Now she walked, and walked," |
| | ratio | 0.028 | |
| 46.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 5 | | matches | | 0 | "But here, in the hollow between the ancient oaks, the ground was soft with bluebells and cowslip and something pale and star-shaped she didn't have a name for, …" | | 1 | "Not the ordinary silence of a park at night, which was never truly silent — there was always the hum of the distant city, the drone of an airplane, the rustle o…" | | 2 | "That thought arrived calm and clear, the way her good thoughts always did, the cool-headed ones that had gotten her out of Cardiff and away from Evan and into a…" | | 3 | "She'd worn it because it was beautiful and because some part of her, the part that didn't say its reasons out loud, had felt safer with it on." | | 4 | "The clearing was larger inside than it had looked from the edge — that was her first concrete wrongness, the kind she could name." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |