| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1131 | | 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) | |
| 64.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1131 | | totalAiIsms | 8 | | found | | 0 | | | 1 | | word | "down her spine" | | count | 1 |
| | 2 | | | 3 | | | 4 | | word | "the last thing" | | count | 1 |
| | 5 | | | 6 | |
| | highlights | | 0 | "tracing" | | 1 | "down her spine" | | 2 | "echoes" | | 3 | "weight" | | 4 | "the last thing" | | 5 | "scanned" | | 6 | "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 | 1 | | narrationSentences | 85 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1123 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.37% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 1045 | | uniqueNames | 8 | | maxNameDensity | 1.05 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | Raven | 1 | | Nest | 1 | | Quinn | 11 | | Tube | 1 | | Morris | 2 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | | globalScore | 0.974 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1123 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 36.23 | | std | 27.05 | | cv | 0.747 | | sampleLengths | | 0 | 41 | | 1 | 5 | | 2 | 55 | | 3 | 49 | | 4 | 7 | | 5 | 61 | | 6 | 56 | | 7 | 38 | | 8 | 44 | | 9 | 15 | | 10 | 71 | | 11 | 4 | | 12 | 73 | | 13 | 3 | | 14 | 47 | | 15 | 6 | | 16 | 9 | | 17 | 57 | | 18 | 3 | | 19 | 59 | | 20 | 11 | | 21 | 101 | | 22 | 25 | | 23 | 64 | | 24 | 31 | | 25 | 29 | | 26 | 6 | | 27 | 81 | | 28 | 9 | | 29 | 51 | | 30 | 12 |
| |
| 92.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 85 | | matches | | 0 | "been peeled" | | 1 | "was hushed" | | 2 | "being noticed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 183 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 90 | | ratio | 0.078 | | matches | | 0 | "The man hit the hoarding and for half a second Quinn thought she had him cornered—then he dropped, slid through a gap at the base where the metal had been peeled back, and vanished." | | 1 | "Through the gap came a draught of air that didn't belong to the surface—stale, mineral, threaded with something sweet and rotten underneath." | | 2 | "The smart move was to call it in—location, suspect, request backup, hold the perimeter." | | 3 | "Quinn counted the turns out of habit—four, five, six—long past the depth of any platform she knew." | | 4 | "Stalls crammed the space rail to rail, lit by lamps that burned with no flame she could name—blue, green, a violet that hurt to look at directly." | | 5 | "A bone the size of a thumb hung on a cord at the throat of the man nearest her—a token, she understood somehow, the price of entry." | | 6 | "There—threading between two stalls near the far end, glancing back." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1057 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.01608325449385052 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000946073793755913 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.48 | | std | 8.65 | | cv | 0.693 | | sampleLengths | | 0 | 22 | | 1 | 19 | | 2 | 5 | | 3 | 4 | | 4 | 25 | | 5 | 26 | | 6 | 7 | | 7 | 26 | | 8 | 2 | | 9 | 14 | | 10 | 7 | | 11 | 3 | | 12 | 23 | | 13 | 10 | | 14 | 17 | | 15 | 5 | | 16 | 3 | | 17 | 12 | | 18 | 2 | | 19 | 8 | | 20 | 34 | | 21 | 5 | | 22 | 8 | | 23 | 22 | | 24 | 3 | | 25 | 4 | | 26 | 10 | | 27 | 4 | | 28 | 4 | | 29 | 22 | | 30 | 15 | | 31 | 22 | | 32 | 16 | | 33 | 5 | | 34 | 14 | | 35 | 14 | | 36 | 4 | | 37 | 10 | | 38 | 28 | | 39 | 2 | | 40 | 6 | | 41 | 27 | | 42 | 3 | | 43 | 6 | | 44 | 5 | | 45 | 10 | | 46 | 22 | | 47 | 4 | | 48 | 6 | | 49 | 9 |
| |
| 72.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4666666666666667 | | totalSentences | 90 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 81 | | matches | | 0 | "Just a smell she'd never" | | 1 | "Then she was through, on" | | 2 | "Somewhere a caged thing screamed" |
| | ratio | 0.037 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 81 | | matches | | 0 | "She'd been tailing him for" | | 1 | "He'd bolted, and a man" | | 2 | "She didn't stop." | | 3 | "She reached the hoarding gasping." | | 4 | "She knew the type." | | 5 | "She knelt there in the" | | 6 | "She thought of Morris." | | 7 | "Her partner, gone down a" | | 8 | "She keyed it, then stopped." | | 9 | "She let go of the" | | 10 | "she muttered, and squeezed herself" | | 11 | "She drew her torch and" | | 12 | "She followed them." | | 13 | "Her training screamed at her" | | 14 | "Her training had no category" | | 15 | "She had nothing like it." | | 16 | "She'd walked in past whatever" | | 17 | "She scanned the crowd for" | | 18 | "He'd slowed, thinking himself safe." | | 19 | "His eyes flicked over her," |
| | ratio | 0.272 | |
| 64.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 81 | | matches | | 0 | "Rain came down in sheets," | | 1 | "Quinn ran with her coat" | | 2 | "The figure didn't slow." | | 3 | "A dark hood, a flash" | | 4 | "Quinn's worn leather watch dug" | | 5 | "She'd been tailing him for" | | 6 | "He'd bolted, and a man" | | 7 | "The alley narrowed." | | 8 | "Brick walls slick with grime" | | 9 | "The man vaulted a low" | | 10 | "Quinn hit it at a" | | 11 | "The landing jarred her knees." | | 12 | "She didn't stop." | | 13 | "A council seal three years" | | 14 | "The man hit the hoarding" | | 15 | "She reached the hoarding gasping." | | 16 | "Quinn crouched, peered through." | | 17 | "Darkness, and a flight of" | | 18 | "A disused Tube station." | | 19 | "She knew the type." |
| | ratio | 0.79 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 57.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 5 | | matches | | 0 | "Rain came down in sheets, turning the Camden pavement to a black mirror that swallowed the neon and spat it back broken." | | 1 | "Quinn ran with her coat slapping against her thighs, lungs burning, eyes fixed on the figure twenty metres ahead." | | 2 | "Quinn's worn leather watch dug into her wrist as she pumped her arms, gaining a metre, losing it again when she skidded on a drain cover." | | 3 | "Through the gap came a draught of air that didn't belong to the surface—stale, mineral, threaded with something sweet and rotten underneath." | | 4 | "The beam caught white tile gone yellow with age, a curling poster for a play that had closed before she was born, and the suspect's wet bootprints leading down." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "He laughed (laugh)" |
| | dialogueSentences | 9 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 1 | | effectiveRatio | 0.444 | |