| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.326 | | leniency | 0.651 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1048 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 42.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1048 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "echoed" | | 1 | "chill" | | 2 | "blown wide" | | 3 | "weight" | | 4 | "etched" | | 5 | "intricate" | | 6 | "pulse" | | 7 | "quickened" | | 8 | "traced" | | 9 | "raced" | | 10 | "churned" | | 11 | "constructed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1039 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 42.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 649 | | uniqueNames | 11 | | maxNameDensity | 2.16 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Morris" | | discoveredNames | | Aldwych | 1 | | Harlow | 1 | | Quinn | 14 | | London | 3 | | Sergeant | 1 | | Chen | 9 | | Camden | 3 | | Webb | 6 | | Italian | 1 | | Morris | 9 | | Brass | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Chen" | | 4 | "Webb" | | 5 | "Morris" | | 6 | "Brass" |
| | places | | | globalScore | 0.421 | | windowScore | 0.5 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like an army marching into the bow" | | 1 | "looked like a small compass" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1039 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 103 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 22.11 | | std | 13.79 | | cv | 0.624 | | sampleLengths | | 0 | 46 | | 1 | 24 | | 2 | 40 | | 3 | 27 | | 4 | 24 | | 5 | 38 | | 6 | 3 | | 7 | 16 | | 8 | 1 | | 9 | 26 | | 10 | 26 | | 11 | 27 | | 12 | 7 | | 13 | 31 | | 14 | 19 | | 15 | 2 | | 16 | 16 | | 17 | 36 | | 18 | 9 | | 19 | 3 | | 20 | 36 | | 21 | 9 | | 22 | 26 | | 23 | 25 | | 24 | 26 | | 25 | 18 | | 26 | 36 | | 27 | 4 | | 28 | 11 | | 29 | 24 | | 30 | 27 | | 31 | 5 | | 32 | 48 | | 33 | 25 | | 34 | 1 | | 35 | 36 | | 36 | 35 | | 37 | 4 | | 38 | 14 | | 39 | 20 | | 40 | 13 | | 41 | 44 | | 42 | 26 | | 43 | 58 | | 44 | 22 | | 45 | 18 | | 46 | 7 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 125 | | matches | (empty) | |
| 59.64% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 103 | | ratio | 0.029 | | matches | | 0 | "She checked his pockets—empty except for a receipt from a Camden coffee shop, timestamped yesterday afternoon." | | 1 | "Morris had drawn similar marks in his notebook the night before—" | | 2 | "The compass, the symbols, the impossible body temperature—it all pointed to things she'd spent three years denying." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 537 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.0335195530726257 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0074487895716946 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 10.09 | | std | 6.47 | | cv | 0.641 | | sampleLengths | | 0 | 10 | | 1 | 16 | | 2 | 20 | | 3 | 19 | | 4 | 5 | | 5 | 3 | | 6 | 14 | | 7 | 23 | | 8 | 8 | | 9 | 4 | | 10 | 15 | | 11 | 10 | | 12 | 14 | | 13 | 9 | | 14 | 8 | | 15 | 3 | | 16 | 2 | | 17 | 16 | | 18 | 3 | | 19 | 9 | | 20 | 7 | | 21 | 1 | | 22 | 26 | | 23 | 5 | | 24 | 16 | | 25 | 5 | | 26 | 15 | | 27 | 12 | | 28 | 7 | | 29 | 13 | | 30 | 18 | | 31 | 5 | | 32 | 14 | | 33 | 2 | | 34 | 16 | | 35 | 18 | | 36 | 3 | | 37 | 12 | | 38 | 3 | | 39 | 9 | | 40 | 3 | | 41 | 6 | | 42 | 2 | | 43 | 17 | | 44 | 11 | | 45 | 6 | | 46 | 3 | | 47 | 19 | | 48 | 7 | | 49 | 14 |
| |
| 80.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.5436893203883495 | | totalSentences | 103 | | uniqueOpeners | 56 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Somewhere in that carefully constructed" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 69 | | matches | | 0 | "His limbs bent at unnatural" | | 1 | "She checked his pockets—empty except" | | 2 | "She shook her head" | | 3 | "She moved closer." | | 4 | "Her breath caught." | | 5 | "She almost said she didn't" | | 6 | "She'd seen those etchings before," | | 7 | "She met his eyes" | | 8 | "She pocketed her torch" | | 9 | "She turned toward the exit" | | 10 | "She'd start with the coffee" | | 11 | "Her phone buzzed." | | 12 | "She knew that name." |
| | ratio | 0.188 | |
| 54.20% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 69 | | matches | | 0 | "The abandoned Aldwych station reeked" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "Each footstep echoed off the" | | 3 | "Detective Sergeant Chen waited at" | | 4 | "Quinn's jaw tightened." | | 5 | "The platform stretched before them," | | 6 | "The victim lay sprawled near" | | 7 | "His limbs bent at unnatural" | | 8 | "Chen consulted his tablet" | | 9 | "Quinn crouched beside the body," | | 10 | "Webb's eyes stared at nothing," | | 11 | "She checked his pockets—empty except" | | 12 | "Chen shifted his weight" | | 13 | "Quinn stood, studying the scene." | | 14 | "The platform lay thick with" | | 15 | "A single set of tracks." | | 16 | "She shook her head" | | 17 | "Quinn examined Webb's manicured nails," | | 18 | "Chen frowned at his tablet." | | 19 | "Quinn's torch beam swept across" |
| | ratio | 0.812 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 28.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 4 | | matches | | 0 | "The platform stretched before them, lit by portable work lamps that threw harsh shadows across cracked tiles and peeling advertisements from the 1990s." | | 1 | "His limbs bent at unnatural angles, as if someone had rearranged him like a mannequin." | | 2 | "Things that lived in London's shadows and fed on the curious." | | 3 | "Somewhere in that carefully constructed existence lay the thread that led him to die alone on an abandoned platform, clutching a compass that pointed to places …" |
| |
| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "Detective Sergeant Chen waited, his breath misting in the underground chill" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |