| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 46 | | tagDensity | 0.239 | | leniency | 0.478 | | rawRatio | 0.091 | | effectiveRatio | 0.043 | |
| 96.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1407 | | 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) | |
| 82.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1407 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "predictable" | | 1 | "processed" | | 2 | "scanning" | | 3 | "measured" | | 4 | "rhythmic" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 15 | | totalWords | 1403 | | ratio | 0.011 | | matches | | 0 | "Leave the compass." | | 1 | "The market moves at the full moon. You're running out of dark." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 93.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 973 | | uniqueNames | 11 | | maxNameDensity | 1.13 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Tube | 1 | | Carver | 6 | | Italian | 1 | | Morris | 3 | | Rotherhithe | 1 | | Northern | 1 | | Line | 1 | | Camden | 2 | | Victorian | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Carver" | | 3 | "Morris" |
| | places | | | globalScore | 0.935 | | windowScore | 1 | |
| 25.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like he'd been dropped from a heig" | | 1 | "looked like they'd been cut from bone" | | 2 | "sounded like breathing" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1403 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 24.61 | | std | 22.13 | | cv | 0.899 | | sampleLengths | | 0 | 12 | | 1 | 56 | | 2 | 39 | | 3 | 64 | | 4 | 25 | | 5 | 3 | | 6 | 24 | | 7 | 76 | | 8 | 61 | | 9 | 61 | | 10 | 41 | | 11 | 8 | | 12 | 20 | | 13 | 52 | | 14 | 3 | | 15 | 1 | | 16 | 9 | | 17 | 32 | | 18 | 2 | | 19 | 3 | | 20 | 28 | | 21 | 39 | | 22 | 6 | | 23 | 40 | | 24 | 2 | | 25 | 59 | | 26 | 4 | | 27 | 2 | | 28 | 5 | | 29 | 67 | | 30 | 5 | | 31 | 12 | | 32 | 6 | | 33 | 31 | | 34 | 9 | | 35 | 9 | | 36 | 4 | | 37 | 40 | | 38 | 27 | | 39 | 51 | | 40 | 5 | | 41 | 10 | | 42 | 10 | | 43 | 66 | | 44 | 14 | | 45 | 1 | | 46 | 46 | | 47 | 53 | | 48 | 19 | | 49 | 33 |
| |
| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 92 | | matches | | 0 | "been dropped" | | 1 | "was clenched" | | 2 | "been stripped" | | 3 | "been corrupted" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 168 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 978 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03067484662576687 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006134969325153374 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 11.05 | | std | 8.87 | | cv | 0.803 | | sampleLengths | | 0 | 12 | | 1 | 31 | | 2 | 25 | | 3 | 13 | | 4 | 12 | | 5 | 14 | | 6 | 5 | | 7 | 12 | | 8 | 4 | | 9 | 3 | | 10 | 20 | | 11 | 5 | | 12 | 4 | | 13 | 4 | | 14 | 7 | | 15 | 10 | | 16 | 15 | | 17 | 3 | | 18 | 12 | | 19 | 12 | | 20 | 3 | | 21 | 5 | | 22 | 13 | | 23 | 13 | | 24 | 2 | | 25 | 2 | | 26 | 27 | | 27 | 11 | | 28 | 9 | | 29 | 22 | | 30 | 1 | | 31 | 20 | | 32 | 9 | | 33 | 7 | | 34 | 41 | | 35 | 13 | | 36 | 4 | | 37 | 20 | | 38 | 17 | | 39 | 8 | | 40 | 16 | | 41 | 4 | | 42 | 30 | | 43 | 22 | | 44 | 3 | | 45 | 1 | | 46 | 9 | | 47 | 2 | | 48 | 30 | | 49 | 2 |
| |
| 81.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5275590551181102 | | totalSentences | 127 | | uniqueOpeners | 67 | |
| 43.29% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 77 | | matches | | 0 | "Just three words: *Leave the" |
| | ratio | 0.013 | |
| 79.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 77 | | matches | | 0 | "He held his torch at" | | 1 | "She pulled on nitrile gloves" | | 2 | "She studied the man's hands." | | 3 | "His right fist was clenched" | | 4 | "His left hand lay open," | | 5 | "She stood and swept her" | | 6 | "She counted the ghosts of" | | 7 | "She crossed to the far" | | 8 | "She placed the compass into" | | 9 | "She turned back to the" | | 10 | "She knelt beside the body" | | 11 | "She worked the fingers apart" | | 12 | "She photographed the token in" | | 13 | "She'd requested Morris's autopsy photos" | | 14 | "She'd begun to wonder if" | | 15 | "She wasn't imagining them now." | | 16 | "She stood, scanning the platform" | | 17 | "Her phone buzzed." | | 18 | "She glanced at the screen," | | 19 | "It pointed steadily now, aimed" |
| | ratio | 0.351 | |
| 50.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 77 | | matches | | 0 | "The body lay face-down in" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The air tasted of wet" | | 3 | "DS Carver said, falling into" | | 4 | "He held his torch at" | | 5 | "Quinn crouched beside the body." | | 6 | "The man wore a tailored" | | 7 | "She pulled on nitrile gloves" | | 8 | "People collapsed in predictable ways." | | 9 | "Heart attacks crumpled them." | | 10 | "Stabbings folded them forward." | | 11 | "This man looked like he'd" | | 12 | "Quinn said nothing." | | 13 | "She studied the man's hands." | | 14 | "His right fist was clenched" | | 15 | "His left hand lay open," | | 16 | "A branching, fractal pattern like" | | 17 | "She stood and swept her" | | 18 | "The original station signage had" | | 19 | "She counted the ghosts of" |
| | ratio | 0.818 | |
| 64.94% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 77 | | matches | | 0 | "Where, if she strained her" |
| | ratio | 0.013 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 5 | | matches | | 0 | "The body lay face-down in a puddle of something that wasn't water." | | 1 | "Detective Harlow Quinn ducked under the crime scene tape and dropped into the abandoned Tube station platform, her boots finding purchase on tiles that hadn't s…" | | 2 | "The air tasted of wet stone and copper and something else, something that prickled at the back of her throat like static before a thunderstorm." | | 3 | "She crossed to the far wall and found a scatter of small objects: a cracked vial containing something iridescent, a bundle of dried herbs tied with copper wire,…" | | 4 | "Both times, the pathologist who'd performed the post-mortem had already retired, moved abroad, couldn't be reached." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.109 | | leniency | 0.217 | | rawRatio | 0 | | effectiveRatio | 0 | |