| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 1 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 918 | | 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) | |
| 40.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 918 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "scanning" | | 1 | "silence" | | 2 | "etched" | | 3 | "intricate" | | 4 | "footsteps" | | 5 | "long shadow" | | 6 | "echoed" | | 7 | "vibrated" | | 8 | "shattered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "seemed to" |
| |
| 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 918 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 873 | | uniqueNames | 17 | | maxNameDensity | 1.72 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Davies | 10 | | Quinn | 1 | | Camden | 2 | | Tube | 1 | | Ma | 2 | | Harlow | 15 | | Watford | 1 | | Morris | 1 | | British | 1 | | Museum | 1 | | Eva | 1 | | Kowalski | 3 | | Aurora | 1 | | Veil | 1 | | Market | 1 | | Glock | 1 | | Police | 1 |
| | persons | | 0 | "Davies" | | 1 | "Quinn" | | 2 | "Harlow" | | 3 | "Morris" | | 4 | "Museum" | | 5 | "Eva" | | 6 | "Kowalski" |
| | places | | | globalScore | 0.641 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 918 | | matches | (empty) | |
| 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 | 52 | | mean | 17.65 | | std | 16.88 | | cv | 0.956 | | sampleLengths | | 0 | 5 | | 1 | 14 | | 2 | 52 | | 3 | 8 | | 4 | 8 | | 5 | 12 | | 6 | 16 | | 7 | 16 | | 8 | 9 | | 9 | 2 | | 10 | 19 | | 11 | 4 | | 12 | 47 | | 13 | 4 | | 14 | 59 | | 15 | 3 | | 16 | 3 | | 17 | 2 | | 18 | 2 | | 19 | 23 | | 20 | 15 | | 21 | 10 | | 22 | 3 | | 23 | 11 | | 24 | 11 | | 25 | 14 | | 26 | 45 | | 27 | 9 | | 28 | 47 | | 29 | 49 | | 30 | 38 | | 31 | 5 | | 32 | 8 | | 33 | 29 | | 34 | 1 | | 35 | 2 | | 36 | 47 | | 37 | 8 | | 38 | 43 | | 39 | 18 | | 40 | 2 | | 41 | 7 | | 42 | 2 | | 43 | 34 | | 44 | 1 | | 45 | 12 | | 46 | 29 | | 47 | 3 | | 48 | 37 | | 49 | 11 |
| |
| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 78 | | matches | | 0 | "was involved" | | 1 | "was torn" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | 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 | 480 | | adjectiveStacks | 1 | | stackExamples | | 0 | "old circular green brass" |
| | adverbCount | 15 | | adverbRatio | 0.03125 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008333333333333333 | |
| 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 | 11.77 | | std | 11.6 | | cv | 0.985 | | sampleLengths | | 0 | 5 | | 1 | 14 | | 2 | 17 | | 3 | 17 | | 4 | 18 | | 5 | 8 | | 6 | 8 | | 7 | 4 | | 8 | 8 | | 9 | 4 | | 10 | 12 | | 11 | 16 | | 12 | 4 | | 13 | 5 | | 14 | 2 | | 15 | 13 | | 16 | 6 | | 17 | 2 | | 18 | 2 | | 19 | 12 | | 20 | 4 | | 21 | 1 | | 22 | 9 | | 23 | 21 | | 24 | 4 | | 25 | 12 | | 26 | 21 | | 27 | 6 | | 28 | 20 | | 29 | 3 | | 30 | 3 | | 31 | 2 | | 32 | 2 | | 33 | 10 | | 34 | 7 | | 35 | 6 | | 36 | 6 | | 37 | 9 | | 38 | 4 | | 39 | 6 | | 40 | 3 | | 41 | 11 | | 42 | 11 | | 43 | 14 | | 44 | 13 | | 45 | 15 | | 46 | 6 | | 47 | 11 | | 48 | 9 | | 49 | 7 |
| |
| 64.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.42857142857142855 | | totalSentences | 77 | | uniqueOpeners | 33 | |
| 50.51% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 66 | | matches | | 0 | "Then the screaming stopped with" |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 66 | | matches | | 0 | "They took his wallet but" | | 1 | "She tilted her head, her" | | 2 | "He pulled his hand back" | | 3 | "She checked the face." | | 4 | "She reached into the victim's" | | 5 | "She pulled it out." | | 6 | "Her fingers held a strange" | | 7 | "It spun erratically, a frantic" | | 8 | "She could not let Davies" | | 9 | "She reached back into the" | | 10 | "It was not a transit" | | 11 | "It was a reader's ticket" | | 12 | "She looked down at the" | | 13 | "They did not move like" |
| | ratio | 0.212 | |
| 66.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 66 | | matches | | 0 | "Harlow Quinn stepped over the" | | 1 | "The forensic team had already" | | 2 | "Harlow knelt by the body," | | 3 | "Looks like a drug deal" | | 4 | "Davies shone his torch onto" | | 5 | "They took his wallet but" | | 6 | "Harlow did not answer." | | 7 | "She tilted her head, her" | | 8 | "A turf war doesn't leave" | | 9 | "Davies hesitated, then reached out" | | 10 | "He pulled his hand back" | | 11 | "The tick-tock of Harlow's worn" | | 12 | "She checked the face." | | 13 | "She reached into the victim's" | | 14 | "She pulled it out." | | 15 | "Her fingers held a strange" | | 16 | "The metal was cool, marred" | | 17 | "The needle did not point" | | 18 | "It spun erratically, a frantic" | | 19 | "Davies leaned in." |
| | ratio | 0.788 | |
| 75.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 66 | | matches | | 0 | "If Kowalski was involved, this" |
| | ratio | 0.015 | |
| 31.25% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 5 | | matches | | 0 | "She reached into the victim's open pocket, her fingers brushing past a transit card before settling on something heavy and cold." | | 1 | "It spun erratically, a frantic blur before locking onto the dark tunnel that led deeper into the abandoned Watford branch." | | 2 | "The childhood friend of Aurora, the girl who spent her days translating dead languages and her nights chasing urban legends." | | 3 | "The muzzle flashes illuminated a face. It was not human. It had eyes of solid gold, and a mouth that opened too wide, lined with rows of needle-sharp teeth." | | 4 | "Harlow scrambled to her knees in the dark, her hands scraping against the sharp stone ballast. She reached for her backup torch, but a cold hand clamped around …" |
| |
| 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 | |