| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1212 | | 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) | |
| 75.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1212 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "etched" | | 2 | "silence" | | 3 | "tracing" | | 4 | "eyebrow" | | 5 | "flickered" |
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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 | 66 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 66 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 57.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 756 | | uniqueNames | 7 | | maxNameDensity | 1.85 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 14 | | Tube | 1 | | Kowalski | 1 | | Thames | 1 | | Polished | 1 | | Verdigris | 1 | | Eva | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Kowalski" | | 2 | "Verdigris" | | 3 | "Eva" |
| | places | | | globalScore | 0.574 | | windowScore | 0.833 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed almost deliberate" |
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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 | 1206 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 106 | | matches | | 0 | "seen that shape" | | 1 | "emerged that life" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 21.16 | | std | 19.64 | | cv | 0.928 | | sampleLengths | | 0 | 63 | | 1 | 11 | | 2 | 5 | | 3 | 57 | | 4 | 9 | | 5 | 29 | | 6 | 3 | | 7 | 9 | | 8 | 20 | | 9 | 60 | | 10 | 4 | | 11 | 23 | | 12 | 61 | | 13 | 6 | | 14 | 19 | | 15 | 30 | | 16 | 7 | | 17 | 56 | | 18 | 3 | | 19 | 2 | | 20 | 44 | | 21 | 1 | | 22 | 3 | | 23 | 8 | | 24 | 19 | | 25 | 45 | | 26 | 1 | | 27 | 25 | | 28 | 52 | | 29 | 15 | | 30 | 62 | | 31 | 16 | | 32 | 10 | | 33 | 4 | | 34 | 3 | | 35 | 45 | | 36 | 34 | | 37 | 3 | | 38 | 58 | | 39 | 5 | | 40 | 6 | | 41 | 43 | | 42 | 5 | | 43 | 19 | | 44 | 16 | | 45 | 2 | | 46 | 27 | | 47 | 1 | | 48 | 3 | | 49 | 4 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 66 | | matches | | 0 | "were curved" | | 1 | "being removed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 120 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 106 | | ratio | 0.066 | | matches | | 0 | "The platform opened up ahead, curved walls lined with old advertising posters from the 1940s—women in hats selling soap powder, their smiles frozen in a different century." | | 1 | "His face held an expression she'd seen before on drowning victims—that particular slackness of features that came with lungs full of fluid." | | 2 | "A folded piece of paper that she drew out and opened—coordinates, handwritten in neat script." | | 3 | "She studied the scene again—the position of the arms, the angle of the legs, the way the coat had fallen open." | | 4 | "\"I noticed.\" Eva opened her satchel wider, and Harlow caught sight of the spines inside—texts in languages she didn't recognise, bindings that looked older than the station itself." | | 5 | "In death, details emerged that life obscured—the calluses on his right hand, the tan line where a ring had sat before being removed, the small scar above his left eyebrow that spoke of old violence." | | 6 | "In the moment of darkness, Harlow heard something from the tunnel—movement, perhaps, or water dripping in a rhythm too regular to be natural." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 763 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.022280471821756225 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.001310615989515072 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 11.38 | | std | 9.48 | | cv | 0.833 | | sampleLengths | | 0 | 5 | | 1 | 18 | | 2 | 16 | | 3 | 24 | | 4 | 11 | | 5 | 5 | | 6 | 5 | | 7 | 25 | | 8 | 27 | | 9 | 9 | | 10 | 18 | | 11 | 11 | | 12 | 3 | | 13 | 9 | | 14 | 20 | | 15 | 4 | | 16 | 14 | | 17 | 22 | | 18 | 4 | | 19 | 16 | | 20 | 4 | | 21 | 19 | | 22 | 4 | | 23 | 2 | | 24 | 2 | | 25 | 3 | | 26 | 17 | | 27 | 37 | | 28 | 6 | | 29 | 19 | | 30 | 5 | | 31 | 3 | | 32 | 5 | | 33 | 15 | | 34 | 2 | | 35 | 7 | | 36 | 27 | | 37 | 11 | | 38 | 18 | | 39 | 3 | | 40 | 2 | | 41 | 5 | | 42 | 17 | | 43 | 6 | | 44 | 4 | | 45 | 12 | | 46 | 1 | | 47 | 3 | | 48 | 8 | | 49 | 8 |
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| 62.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.41509433962264153 | | totalSentences | 106 | | uniqueOpeners | 44 | |
| 54.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 61 | | matches | | 0 | "Then the light steadied, and" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 61 | | matches | | 0 | "She pulled her collar up" | | 1 | "Her leather watch caught on" | | 2 | "She tucked a curl of" | | 3 | "His face held an expression" | | 4 | "She tilted the casing." | | 5 | "Her knees cracked in the" | | 6 | "She walked to the tunnel" | | 7 | "She studied the scene again—the" | | 8 | "His left hand was empty." | | 9 | "She'd seen that shape before." | | 10 | "She held up her thumb" | | 11 | "It no longer pointed down" | | 12 | "It pointed at her." | | 13 | "She tucked her hair behind" |
| | ratio | 0.23 | |
| 33.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 61 | | matches | | 0 | "The stench hit Harlow first." | | 1 | "Copper and something else beneath" | | 2 | "She pulled her collar up" | | 3 | "The beam caught the yellow" | | 4 | "A uniform stood at the" | | 5 | "Harlow passed through the tape." | | 6 | "Her leather watch caught on" | | 7 | "The platform opened up ahead," | | 8 | "The body lay near the" | | 9 | "Eva Kowalski crouched beside it," | | 10 | "She tucked a curl of" | | 11 | "Harlow knelt opposite her." | | 12 | "The victim was male, mid-fifties," | | 13 | "His face held an expression" | | 14 | "The nearest water source was" | | 15 | "Eva shifted her weight and" | | 16 | "Harlow ran the beam up" | | 17 | "Harlow checked the man's pockets." | | 18 | "Wallet, still inside." | | 19 | "A folded piece of paper" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 44.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 4 | | matches | | 0 | "The beam caught the yellow tape strung between two support pillars, fluttering in a draft that carried whispers of movement from the tunnels beyond." | | 1 | "His face held an expression she'd seen before on drowning victims—that particular slackness of features that came with lungs full of fluid." | | 2 | "Harlow ran the beam up his body, taking in the immaculate cuffs, the lack of grime beneath his fingernails, the way his hair lay flat and styled despite the dam…" | | 3 | "In death, details emerged that life obscured—the calluses on his right hand, the tan line where a ring had sat before being removed, the small scar above his le…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |