| 26.09% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn knelt again [again]" | | 1 | "Hayes said slowly [slowly]" |
| | dialogueSentences | 23 | | tagDensity | 0.478 | | leniency | 0.957 | | rawRatio | 0.182 | | effectiveRatio | 0.174 | |
| 70.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1028 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | adverb | "deliberately" | | count | 2 |
| | 3 | | | 4 | |
| | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "deliberately" | | 3 | "carefully" | | 4 | "slightly" |
| |
| 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) | |
| 61.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1028 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "weight" | | 1 | "standard" | | 2 | "framework" | | 3 | "aligned" | | 4 | "constructed" | | 5 | "silence" | | 6 | "quivered" | | 7 | "trembled" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 117 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 117 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1022 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 850 | | uniqueNames | 6 | | maxNameDensity | 1.65 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Hayes" | | discoveredNames | | Tube | 2 | | Hayes | 13 | | Quinn | 14 | | Metropolitan | 1 | | Police | 1 | | Morris | 2 |
| | persons | | 0 | "Hayes" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.676 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1022 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 27.62 | | std | 16.44 | | cv | 0.595 | | sampleLengths | | 0 | 43 | | 1 | 19 | | 2 | 52 | | 3 | 27 | | 4 | 44 | | 5 | 13 | | 6 | 57 | | 7 | 13 | | 8 | 26 | | 9 | 53 | | 10 | 23 | | 11 | 32 | | 12 | 8 | | 13 | 61 | | 14 | 5 | | 15 | 17 | | 16 | 20 | | 17 | 26 | | 18 | 30 | | 19 | 17 | | 20 | 55 | | 21 | 36 | | 22 | 16 | | 23 | 9 | | 24 | 4 | | 25 | 48 | | 26 | 12 | | 27 | 35 | | 28 | 40 | | 29 | 8 | | 30 | 33 | | 31 | 16 | | 32 | 50 | | 33 | 9 | | 34 | 20 | | 35 | 35 | | 36 | 10 |
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| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 117 | | matches | | 0 | "been pulled" | | 1 | "been called" | | 2 | "been taught" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 153 | | matches | (empty) | |
| 98.56% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 129 | | ratio | 0.016 | | matches | | 0 | "The compass in her jacket—the one she'd acquired through channels she didn't discuss—felt heavier than usual." | | 1 | "But the token, the location, the wound pattern—none of it aligned with conventional violence." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 855 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.021052631578947368 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010526315789473684 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 7.92 | | std | 5.12 | | cv | 0.647 | | sampleLengths | | 0 | 10 | | 1 | 17 | | 2 | 2 | | 3 | 6 | | 4 | 2 | | 5 | 6 | | 6 | 10 | | 7 | 1 | | 8 | 8 | | 9 | 15 | | 10 | 8 | | 11 | 13 | | 12 | 16 | | 13 | 10 | | 14 | 17 | | 15 | 5 | | 16 | 17 | | 17 | 6 | | 18 | 6 | | 19 | 10 | | 20 | 5 | | 21 | 1 | | 22 | 1 | | 23 | 6 | | 24 | 17 | | 25 | 7 | | 26 | 14 | | 27 | 8 | | 28 | 1 | | 29 | 10 | | 30 | 13 | | 31 | 2 | | 32 | 14 | | 33 | 10 | | 34 | 7 | | 35 | 12 | | 36 | 5 | | 37 | 16 | | 38 | 6 | | 39 | 7 | | 40 | 10 | | 41 | 3 | | 42 | 10 | | 43 | 12 | | 44 | 3 | | 45 | 11 | | 46 | 1 | | 47 | 2 | | 48 | 3 | | 49 | 5 |
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| 73.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4645669291338583 | | totalSentences | 127 | | uniqueOpeners | 59 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Ever since Morris vanished, she'd" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 95 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "She'd been pulled from bed" | | 2 | "She approached the body without" | | 3 | "She crouched beside the corpse," | | 4 | "She ran her finger along" | | 5 | "His forehead creased in that" | | 6 | "She found it near the" | | 7 | "She remembered his voice on" | | 8 | "She pulled an evidence bag" | | 9 | "She'd been carrying it for" | | 10 | "He pulled out his notebook," | | 11 | "She angled it across one" | | 12 | "His eyes narrowed." | | 13 | "She watched the moment comprehension" | | 14 | "He'd seen something wrong with" | | 15 | "She stood, walked toward the" | | 16 | "His silence filled the tunnel" | | 17 | "She extracted it carefully, using" | | 18 | "It quivered, settling toward a" | | 19 | "Her hands remained steady." |
| | ratio | 0.232 | |
| 91.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 95 | | matches | | 0 | "The abandoned Tube station reeked" | | 1 | "Quinn stood in the mouth" | | 2 | "Her worn leather watch caught" | | 3 | "She'd been pulled from bed" | | 4 | "The body lay near the" | | 5 | "Constable Hayes stood near the" | | 6 | "Hayes said, gesturing vaguely at" | | 7 | "Quinn held up a hand." | | 8 | "She approached the body without" | | 9 | "The wounds Hayes described didn't" | | 10 | "She crouched beside the corpse," | | 11 | "She ran her finger along" | | 12 | "The flesh curled inward at" | | 13 | "A knife would have left" | | 14 | "These wounds had edges that" | | 15 | "Quinn stood, brushed imaginary dust" | | 16 | "His forehead creased in that" | | 17 | "Quinn walked the perimeter of" | | 18 | "The Metropolitan Police had been" | | 19 | "That detail tugged at her." |
| | ratio | 0.737 | |
| 52.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 95 | | matches | | 0 | "As if something had moved" |
| | ratio | 0.011 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "She approached the body without rushing, her military bearing evident in the precise placement of each step." | | 1 | "His forehead creased in that way that suggested he'd already committed to his theory." | | 2 | "It quivered, settling toward a direction that had no relationship to geography." | | 3 | "Murderers didn't leave behind artifacts that belonged in museums or occult shops or places that officially didn't exist." |
| |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn knelt again, this time producing a torch from her belt" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |