| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.448 | | leniency | 0.897 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1245 | | 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) | |
| 55.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1245 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "fluttered" | | 1 | "gloom" | | 2 | "measured" | | 3 | "surreal" | | 4 | "dancing" | | 5 | "scanned" | | 6 | "furrowed" | | 7 | "silence" | | 8 | "perfect" | | 9 | "amidst" | | 10 | "chilling" |
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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 | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 94 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1239 | | ratio | 0.002 | | matches | | 0 | "who" | | 1 | "created" | | 2 | "Morris?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 837 | | uniqueNames | 10 | | maxNameDensity | 1.31 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Aldwych | 1 | | Tube | 1 | | Davies | 8 | | Alistair | 2 | | Veil | 1 | | Market | 1 | | Eva | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Alistair" | | 4 | "Eva" |
| | places | | | globalScore | 0.843 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like yellowed bone" |
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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 | 1239 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 94 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 38.72 | | std | 22.74 | | cv | 0.587 | | sampleLengths | | 0 | 51 | | 1 | 46 | | 2 | 48 | | 3 | 67 | | 4 | 9 | | 5 | 12 | | 6 | 32 | | 7 | 61 | | 8 | 13 | | 9 | 47 | | 10 | 50 | | 11 | 45 | | 12 | 16 | | 13 | 12 | | 14 | 56 | | 15 | 7 | | 16 | 1 | | 17 | 39 | | 18 | 38 | | 19 | 99 | | 20 | 34 | | 21 | 14 | | 22 | 11 | | 23 | 49 | | 24 | 67 | | 25 | 49 | | 26 | 42 | | 27 | 49 | | 28 | 19 | | 29 | 71 | | 30 | 22 | | 31 | 63 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 78 | | matches | | 0 | "was gone" | | 1 | "were scored" | | 2 | "being led" | | 3 | "was obscured" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 133 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 94 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 750 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.02 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006666666666666667 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 94 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 94 | | mean | 13.18 | | std | 9.09 | | cv | 0.689 | | sampleLengths | | 0 | 14 | | 1 | 25 | | 2 | 11 | | 3 | 1 | | 4 | 21 | | 5 | 4 | | 6 | 21 | | 7 | 28 | | 8 | 20 | | 9 | 7 | | 10 | 20 | | 11 | 19 | | 12 | 21 | | 13 | 6 | | 14 | 3 | | 15 | 12 | | 16 | 29 | | 17 | 3 | | 18 | 12 | | 19 | 14 | | 20 | 2 | | 21 | 15 | | 22 | 14 | | 23 | 4 | | 24 | 13 | | 25 | 8 | | 26 | 39 | | 27 | 3 | | 28 | 14 | | 29 | 4 | | 30 | 7 | | 31 | 22 | | 32 | 10 | | 33 | 35 | | 34 | 16 | | 35 | 12 | | 36 | 9 | | 37 | 6 | | 38 | 6 | | 39 | 3 | | 40 | 13 | | 41 | 19 | | 42 | 7 | | 43 | 1 | | 44 | 19 | | 45 | 20 | | 46 | 13 | | 47 | 25 | | 48 | 15 | | 49 | 16 |
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| 63.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.425531914893617 | | totalSentences | 94 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 72 | | matches | | 0 | "She flashed her badge at" | | 1 | "she corrected, her tone leaving" | | 2 | "She descended into the gloom," | | 3 | "he grunted without looking up" | | 4 | "She recognised it instantly." | | 5 | "She walked over to the" | | 6 | "Her mind, a catalogue of" | | 7 | "She pointed to the token" | | 8 | "It was a perfect, deliberate" | | 9 | "She knelt, ignoring the protest" | | 10 | "She didn’t touch it, just" | | 11 | "she said, her voice flat" | | 12 | "She gestured to the circle" | | 13 | "She was a procurer." | | 14 | "She’d arranged a meet with" | | 15 | "They’d killed Alistair, used his" | | 16 | "Her hand went to her" | | 17 | "she barked into the radio," | | 18 | "Her phone screen lit up," | | 19 | "It showed Eva, her curly" |
| | ratio | 0.278 | |
| 36.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 72 | | matches | | 0 | "The scent hit Detective Harlow" | | 1 | "This was pine, sandalwood, and" | | 2 | "The caution tape fluttered across" | | 3 | "Quinn’s sharp jaw tightened." | | 4 | "She flashed her badge at" | | 5 | "she corrected, her tone leaving" | | 6 | "She descended into the gloom," | | 7 | "The platform below was a" | | 8 | "Emergency lights bathed the curved," | | 9 | "Forensics teams in white bunny" | | 10 | "he grunted without looking up" | | 11 | "Davies trailed off, waving a" | | 12 | "Quinn’s eyes scanned the area," | | 13 | "The chalk outline was neat," | | 14 | "A worn leather satchel, its" | | 15 | "She recognised it instantly." | | 16 | "Quinn’s voice was low, a" | | 17 | "Davies finally looked at her," | | 18 | "Quinn didn’t answer." | | 19 | "She walked over to the" |
| | ratio | 0.847 | |
| 69.44% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 72 | | matches | | 0 | "Before he could speak, Quinn’s" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 1 | | matches | | 0 | "And tucked into a side pocket, something that didn't belong: a small, irregularly shaped token carved from what looked like yellowed bone." |
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| 48.08% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 2 | | matches | | 0 | "she corrected, her tone leaving no room for debate" | | 1 | "she said, her voice flat" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 5 | | fancyTags | | 0 | "she corrected (correct)" | | 1 | "he grunted (grunt)" | | 2 | "Davies continued (continue)" | | 3 | "The dispatcher’s voice faltered (falter)" | | 4 | "she barked (bark)" |
| | dialogueSentences | 29 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0.625 | | effectiveRatio | 0.345 | |