| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.234 | | leniency | 0.468 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1707 | | totalAiIsmAdverbs | 2 | | 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) | |
| 67.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1707 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silk" | | 1 | "standard" | | 2 | "shattered" | | 3 | "chill" | | 4 | "lilt" | | 5 | "tracing" | | 6 | "perfect" | | 7 | "shimmered" | | 8 | "flickered" | | 9 | "footsteps" |
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| 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 | 129 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 129 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 165 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1707 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1221 | | uniqueNames | 12 | | maxNameDensity | 1.23 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Good | 1 | | Davies | 5 | | Bovril | 1 | | Eastern | 1 | | European | 1 | | London | 1 | | Quinn | 15 | | Tucked | 1 | | Eva | 11 | | Chose | 1 |
| | persons | | 0 | "Davies" | | 1 | "Bovril" | | 2 | "Quinn" | | 3 | "Eva" |
| | places | | | globalScore | 0.886 | | windowScore | 0.667 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like actual bone, smooth and warm" | | 1 | "quite there" | | 2 | "something between terror and exhilaration" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1707 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 165 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 25.86 | | std | 22.48 | | cv | 0.869 | | sampleLengths | | 0 | 5 | | 1 | 96 | | 2 | 60 | | 3 | 37 | | 4 | 1 | | 5 | 4 | | 6 | 63 | | 7 | 7 | | 8 | 4 | | 9 | 6 | | 10 | 4 | | 11 | 42 | | 12 | 47 | | 13 | 79 | | 14 | 6 | | 15 | 45 | | 16 | 3 | | 17 | 9 | | 18 | 18 | | 19 | 43 | | 20 | 6 | | 21 | 67 | | 22 | 2 | | 23 | 66 | | 24 | 2 | | 25 | 34 | | 26 | 14 | | 27 | 16 | | 28 | 23 | | 29 | 13 | | 30 | 49 | | 31 | 3 | | 32 | 3 | | 33 | 51 | | 34 | 4 | | 35 | 31 | | 36 | 11 | | 37 | 33 | | 38 | 58 | | 39 | 6 | | 40 | 43 | | 41 | 12 | | 42 | 41 | | 43 | 41 | | 44 | 5 | | 45 | 27 | | 46 | 35 | | 47 | 51 | | 48 | 3 | | 49 | 19 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 129 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 196 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 165 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1223 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.035977105478331974 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007358953393295176 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 165 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 165 | | mean | 10.35 | | std | 9.04 | | cv | 0.874 | | sampleLengths | | 0 | 5 | | 1 | 26 | | 2 | 23 | | 3 | 35 | | 4 | 9 | | 5 | 3 | | 6 | 5 | | 7 | 13 | | 8 | 6 | | 9 | 8 | | 10 | 10 | | 11 | 18 | | 12 | 20 | | 13 | 2 | | 14 | 1 | | 15 | 3 | | 16 | 11 | | 17 | 1 | | 18 | 4 | | 19 | 16 | | 20 | 5 | | 21 | 3 | | 22 | 24 | | 23 | 5 | | 24 | 1 | | 25 | 9 | | 26 | 7 | | 27 | 4 | | 28 | 3 | | 29 | 3 | | 30 | 4 | | 31 | 10 | | 32 | 12 | | 33 | 20 | | 34 | 7 | | 35 | 32 | | 36 | 7 | | 37 | 1 | | 38 | 15 | | 39 | 4 | | 40 | 18 | | 41 | 9 | | 42 | 4 | | 43 | 17 | | 44 | 8 | | 45 | 4 | | 46 | 6 | | 47 | 8 | | 48 | 28 | | 49 | 3 |
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| 80.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.509090909090909 | | totalSentences | 165 | | uniqueOpeners | 84 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 110 | | matches | | 0 | "Too eager, sometimes." | | 1 | "Somewhere deep in the tunnel" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 110 | | matches | | 0 | "She crouched beside the victim." | | 1 | "He'd come here on purpose," | | 2 | "His expression held something she" | | 3 | "She checked the other eye." | | 4 | "She pulled out her phone" | | 5 | "She stood and surveyed the" | | 6 | "She bagged it and kept" | | 7 | "He gestured at the overturned" | | 8 | "It was a reasonable theory." | | 9 | "She walked a slow circuit" | | 10 | "She stopped and held her" | | 11 | "Her hand's shadow fell across" | | 12 | "His body still threw nothing" | | 13 | "She clutched a worn leather" | | 14 | "She held up a lanyard" | | 15 | "She hadn't paid attention to" | | 16 | "She pushed her glasses up" | | 17 | "She didn't touch anything, but" | | 18 | "she lifted the evidence bag" | | 19 | "She pulled a notebook from" |
| | ratio | 0.291 | |
| 46.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 110 | | matches | | 0 | "The body had no shadow." | | 1 | "Quinn noticed it before anything" | | 2 | "The portable halogen floods her" | | 3 | "Everything threw long black shapes" | | 4 | "Everything except him." | | 5 | "She crouched beside the victim." | | 6 | "Italian leather shoes, no scuff" | | 7 | "A tailored charcoal coat with" | | 8 | "He'd come here on purpose," | | 9 | "Nobody wandered into an abandoned" | | 10 | "Davies, her sergeant, flipped through" | | 11 | "Quinn pulled a latex glove" | | 12 | "His expression held something she" | | 13 | "She checked the other eye." | | 14 | "Both irises had turned the" | | 15 | "Davies leaned in." | | 16 | "Quinn shook her head" | | 17 | "She pulled out her phone" | | 18 | "She stood and surveyed the" | | 19 | "The abandoned station had the" |
| | ratio | 0.827 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 110 | | matches | | 0 | "As if the light passed" | | 1 | "As if he wasn't quite" |
| | ratio | 0.018 | |
| 9.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 8 | | matches | | 0 | "Forty-one years on this earth, eighteen on the force, and she'd never seen a corpse that didn't cast a shadow under direct light." | | 1 | "The portable halogen floods her team had dragged down cast everything else in sharp relief, the old ticket booth, the rusted turnstiles, the faded roundel sign …" | | 2 | "She walked a slow circuit of the body, watching the halogen light carve her own shadow into the concrete at her feet, a tall dark shape that followed her faithf…" | | 3 | "Eva ducked under the cordon and approached the wall carvings with the hungry focus of someone who'd spent too long reading books and not enough time around the …" | | 4 | "She didn't touch anything, but her eyes swept the debris with a practised efficiency that belied her nervous posture." | | 5 | "She turned back to Eva, who was kneeling beside one of the overturned tables, her attention locked on something half-buried under a pile of shattered glass." | | 6 | "Its casing carried a thick patina of green corrosion, and its face bore etchings that matched the warding sigils on the walls." | | 7 | "The temperature on the platform dropped by several degrees in the space of a single breath, and from deep inside the tunnel came a sound that wasn't water dripp…" |
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| 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 | |