| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1325 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "gently" | | 2 | "perfectly" | | 3 | "carefully" |
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| 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) | |
| 77.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1325 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "etched" | | 2 | "stomach" | | 3 | "standard" | | 4 | "footsteps" | | 5 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 95 | | matches | (empty) | |
| 97.74% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 5 | | totalWords | 1343 | | ratio | 0.004 | | matches | | 0 | "Shade-made, Camden." | | 1 | "M. Morris, 2004." 1/1/2004, 12:00:00 AM |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.08% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 1020 | | uniqueNames | 6 | | maxNameDensity | 1.08 | | worstName | "Rees" | | maxWindowNameDensity | 2 | | worstWindowName | "Rees" | | discoveredNames | | Camden | 2 | | Town | 1 | | Rees | 11 | | Quinn | 10 | | Morris | 2 | | Deptford | 1 |
| | persons | | | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Deptford" |
| | globalScore | 0.961 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.745 | | wordCount | 1343 | | matches | | 0 | "not north but directly at the dead man" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 22.02 | | std | 19.77 | | cv | 0.898 | | sampleLengths | | 0 | 64 | | 1 | 16 | | 2 | 3 | | 3 | 81 | | 4 | 6 | | 5 | 23 | | 6 | 36 | | 7 | 4 | | 8 | 63 | | 9 | 14 | | 10 | 3 | | 11 | 55 | | 12 | 51 | | 13 | 4 | | 14 | 35 | | 15 | 7 | | 16 | 38 | | 17 | 42 | | 18 | 30 | | 19 | 17 | | 20 | 1 | | 21 | 18 | | 22 | 4 | | 23 | 9 | | 24 | 40 | | 25 | 9 | | 26 | 25 | | 27 | 19 | | 28 | 2 | | 29 | 12 | | 30 | 36 | | 31 | 8 | | 32 | 4 | | 33 | 3 | | 34 | 58 | | 35 | 7 | | 36 | 7 | | 37 | 5 | | 38 | 53 | | 39 | 12 | | 40 | 20 | | 41 | 2 | | 42 | 20 | | 43 | 32 | | 44 | 7 | | 45 | 1 | | 46 | 47 | | 47 | 4 | | 48 | 38 | | 49 | 15 |
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| 86.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 95 | | matches | | 0 | "been chained" | | 1 | "were calloused" | | 2 | "were clipped" | | 3 | "been carried" | | 4 | "being tested" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 174 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 125 | | ratio | 0.088 | | matches | | 0 | "Market stalls lined the length of the platform — rough timber and tarpaulin, arranged in two rows with enough space between for a crowd to move through." | | 1 | "His shoes were clean — not scuffed, not dusty — as though he'd never walked on any surface rougher than carpet." | | 2 | "A watch — not his own." | | 3 | "Something caught the beam — a small brass object sitting on top of a crate, half-hidden under a fold of canvas." | | 4 | "The needle twitched — just barely — and then settled back to point at the body." | | 5 | "Engraved inside the band — tiny letters, almost worn away: *M." | | 6 | "She turned a full circle — the needle followed the body, locked onto it regardless of orientation." | | 7 | "The needle drifted — slow, almost imperceptible — and settled pointing down the tunnel, past the body, toward a section of wall where the tiles had crumbled away to bare brick." | | 8 | "Behind the broken tiles, a gap opened into the wall — wider than a crack, wide enough for a man to slide through sideways." | | 9 | "The dead man's clean shoes pointed at the wall, and she knelt again and ran her torch along the floor between his heels and the broken tiles — not a mark, not a scuff, not a single drag mark on the dusty concrete." | | 10 | "A trail laid out for her — but she couldn't tell if she was the detective or the one being tested." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1011 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.03461918892185954 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006923837784371909 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 10.74 | | std | 7.99 | | cv | 0.744 | | sampleLengths | | 0 | 32 | | 1 | 32 | | 2 | 16 | | 3 | 3 | | 4 | 13 | | 5 | 14 | | 6 | 8 | | 7 | 27 | | 8 | 6 | | 9 | 13 | | 10 | 6 | | 11 | 13 | | 12 | 10 | | 13 | 6 | | 14 | 14 | | 15 | 10 | | 16 | 6 | | 17 | 4 | | 18 | 17 | | 19 | 16 | | 20 | 21 | | 21 | 9 | | 22 | 4 | | 23 | 5 | | 24 | 5 | | 25 | 3 | | 26 | 39 | | 27 | 16 | | 28 | 8 | | 29 | 6 | | 30 | 20 | | 31 | 7 | | 32 | 10 | | 33 | 4 | | 34 | 18 | | 35 | 17 | | 36 | 7 | | 37 | 13 | | 38 | 25 | | 39 | 3 | | 40 | 13 | | 41 | 21 | | 42 | 5 | | 43 | 2 | | 44 | 14 | | 45 | 14 | | 46 | 4 | | 47 | 13 | | 48 | 1 | | 49 | 8 |
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| 68.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.432 | | totalSentences | 125 | | uniqueOpeners | 54 | |
| 39.22% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 85 | | matches | | | ratio | 0.012 | |
| 69.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 85 | | matches | | 0 | "She stepped over the tape" | | 1 | "They walked further down the" | | 2 | "He wore a grey suit" | | 3 | "His shoes were clean —" | | 4 | "His face was calm, eyes" | | 5 | "She could see her breath." | | 6 | "She lifted the dead man's" | | 7 | "His fingers were calloused at" | | 8 | "She walked the length of" | | 9 | "She pulled the canvas back." | | 10 | "She picked it up." | | 11 | "He came over, looked at" | | 12 | "She looked at him." | | 13 | "His face was the colour" | | 14 | "She handed him the compass." | | 15 | "He took it, held it" | | 16 | "She unclasped it carefully, turned" | | 17 | "Her stomach dropped." | | 18 | "She kept her face still." | | 19 | "She closed her fingers around" |
| | ratio | 0.376 | |
| 30.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 85 | | matches | | 0 | "The service entrance behind the" | | 1 | "Quinn ducked under the padlocked" | | 2 | "A DC named Rees waited" | | 3 | "She stepped over the tape" | | 4 | "Rees shifted his weight, hands" | | 5 | "They walked further down the" | | 6 | "The torch beam caught on" | | 7 | "Someone had knocked over a" | | 8 | "A man lay on his" | | 9 | "He wore a grey suit" | | 10 | "His shoes were clean —" | | 11 | "His face was calm, eyes" | | 12 | "Quinn knelt beside him." | | 13 | "The air here was colder." | | 14 | "She could see her breath." | | 15 | "Rees crouched on the other" | | 16 | "Quinn leaned closer to the" | | 17 | "A watch — not his" | | 18 | "The leather band was old," | | 19 | "She lifted the dead man's" |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Rees shifted, hands stuffed in his jacket pockets" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0 | | effectiveRatio | 0 | |