| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 953 | | 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) | |
| 52.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 953 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "fractured" | | 1 | "maw" | | 2 | "dancing" | | 3 | "echoed" | | 4 | "flicker" | | 5 | "glistening" | | 6 | "velvet" | | 7 | "silk" | | 8 | "database" |
| |
| 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 | 75 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 946 | | 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 | 27 | | wordCount | 938 | | uniqueNames | 17 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | Lock | 1 | | Raven | 1 | | Nest | 1 | | Met | 1 | | Victorian | 2 | | Morris | 2 | | Veil | 1 | | Market | 1 | | London | 1 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Herrera | 1 | | Royal | 1 | | Free | 1 | | Quinn | 7 | | Tomás | 3 |
| | persons | | 0 | "Lock" | | 1 | "Nest" | | 2 | "Met" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Quinn" | | 8 | "Tomás" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Royal" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 1 | | matches | | |
| 94.29% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.057 | | wordCount | 946 | | matches | | 0 | "Not the rusted maintenance access she expected from the maps, but polished oak banded with iron" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 35.04 | | std | 28.03 | | cv | 0.8 | | sampleLengths | | 0 | 3 | | 1 | 46 | | 2 | 79 | | 3 | 49 | | 4 | 31 | | 5 | 10 | | 6 | 5 | | 7 | 2 | | 8 | 75 | | 9 | 72 | | 10 | 37 | | 11 | 3 | | 12 | 95 | | 13 | 47 | | 14 | 27 | | 15 | 1 | | 16 | 90 | | 17 | 37 | | 18 | 52 | | 19 | 43 | | 20 | 18 | | 21 | 50 | | 22 | 18 | | 23 | 29 | | 24 | 9 | | 25 | 2 | | 26 | 16 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 174 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 78 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 945 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 29 | | adverbRatio | 0.030687830687830688 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.010582010582010581 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 12.13 | | std | 8.6 | | cv | 0.709 | | sampleLengths | | 0 | 3 | | 1 | 3 | | 2 | 14 | | 3 | 14 | | 4 | 15 | | 5 | 2 | | 6 | 36 | | 7 | 17 | | 8 | 24 | | 9 | 4 | | 10 | 9 | | 11 | 3 | | 12 | 14 | | 13 | 4 | | 14 | 6 | | 15 | 3 | | 16 | 6 | | 17 | 5 | | 18 | 26 | | 19 | 4 | | 20 | 3 | | 21 | 3 | | 22 | 5 | | 23 | 2 | | 24 | 13 | | 25 | 13 | | 26 | 4 | | 27 | 23 | | 28 | 22 | | 29 | 7 | | 30 | 23 | | 31 | 19 | | 32 | 23 | | 33 | 6 | | 34 | 15 | | 35 | 16 | | 36 | 3 | | 37 | 15 | | 38 | 20 | | 39 | 16 | | 40 | 15 | | 41 | 29 | | 42 | 18 | | 43 | 12 | | 44 | 17 | | 45 | 5 | | 46 | 22 | | 47 | 1 | | 48 | 23 | | 49 | 3 |
| |
| 59.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.41025641025641024 | | totalSentences | 78 | | uniqueOpeners | 32 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Somewhere, a clock ticked too" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 69 | | matches | | 0 | "She'd chased him three blocks" | | 1 | "She knew the area." | | 2 | "He kicked through a chain-link" | | 3 | "She could turn back." | | 4 | "Her boots struck each step" | | 5 | "She'd seen symbols like that" | | 6 | "He wasn't alone." | | 7 | "His warm brown eyes fixed" | | 8 | "Her voice carried flat authority," | | 9 | "It wasn't friendly." | | 10 | "He raised his left hand," | | 11 | "He spoke a word that" | | 12 | "It had been a placeholder." |
| | ratio | 0.188 | |
| 39.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 69 | | matches | | 0 | "The wool ripped." | | 1 | "Quinn stumbled forward, her fingers" | | 2 | "The suspect vaulted over a" | | 3 | "Camden Lock glistened behind them," | | 4 | "She'd chased him three blocks" | | 5 | "Rain plastered her salt-and-pepper hair" | | 6 | "The suspect, tall and hooded" | | 7 | "Quinn checked her watch." | | 8 | "The worn old leather strap" | | 9 | "The station had closed six" | | 10 | "She knew the area." | | 11 | "Everyone on the Met knew" | | 12 | "Drug drops, mostly." | | 13 | "He kicked through a chain-link" | | 14 | "She could turn back." | | 15 | "The suspect was getting away." | | 16 | "The stairs descended into darkness" | | 17 | "Something sweet and wrong, like" | | 18 | "Quinn drew her torch." | | 19 | "The beam caught dust motes" |
| | ratio | 0.841 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 13 | | matches | | 0 | "Quinn stumbled forward, her fingers clutching empty fabric that smelled of petrichor and copper." | | 1 | "He kicked through a chain-link fence that shouldn't have given way, the metal parting like wet paper, and vanished into the black maw of the stairwell." | | 2 | "The beam caught dust motes dancing in air that felt thicker than it should, pressurized, as if the earth itself held its breath." | | 3 | "Her boots struck each step with sharp cracks that echoed too long, stretching into sounds almost like words whispered just below comprehension." | | 4 | "Not the rusted maintenance access she expected from the maps, but polished oak banded with iron that looked older than the city above." | | 5 | "A bone token lay discarded on the threshold, carved with spiral symbols that made her torch flicker and dim." | | 6 | "A man in a sharp grey suit traded whispers for teeth, his mouth stretching too wide when he smiled at a customer with skin that reflected the light wrong." | | 7 | "The man's left sleeve rode up, revealing a pale scar that ran from wrist to elbow, twisting like a snake." | | 8 | "The one whose NHS file had vanished the same week Morris died, leaving behind only a note about unauthorized treatments for patients who didn't exist in any dat…" | | 9 | "The crowd sensed her intention, pressing back against the curved tunnel walls, creating a path that felt less like courtesy and more like herding." | | 10 | "The suspect melted into the throng, disappearing behind a stall selling clockwork hearts that ticked out of time." | | 11 | "He raised his left hand, the scarred forearm fully exposed now, and made a gesture that hurt to look at directly, his fingers bending at angles that suggested b…" | | 12 | "He spoke a word that tasted of static electricity on her tongue." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |