| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.19 | | leniency | 0.381 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1465 | | 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) | |
| 82.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1465 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "etched" | | 1 | "indexed" | | 2 | "resolved" | | 3 | "perfect" |
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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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1465 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1032 | | uniqueNames | 10 | | maxNameDensity | 1.45 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Bovril | 1 | | Town | 1 | | Southern | 1 | | Branch | 1 | | Hargreaves | 5 | | Quinn | 15 | | Italian | 1 | | Tight | 1 | | Kowalski | 1 | | Eva | 8 |
| | persons | | 0 | "Hargreaves" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" |
| | places | | | globalScore | 0.773 | | windowScore | 0.833 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a tailored shirt" | | 1 | "looked like two crescents meeting at thei" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.683 | | wordCount | 1465 | | matches | | 0 | "not a crowd but a queue" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 21.87 | | std | 17.77 | | cv | 0.813 | | sampleLengths | | 0 | 22 | | 1 | 67 | | 2 | 3 | | 3 | 32 | | 4 | 41 | | 5 | 1 | | 6 | 9 | | 7 | 51 | | 8 | 8 | | 9 | 18 | | 10 | 38 | | 11 | 10 | | 12 | 13 | | 13 | 49 | | 14 | 18 | | 15 | 10 | | 16 | 1 | | 17 | 49 | | 18 | 3 | | 19 | 7 | | 20 | 30 | | 21 | 9 | | 22 | 58 | | 23 | 22 | | 24 | 3 | | 25 | 35 | | 26 | 66 | | 27 | 5 | | 28 | 13 | | 29 | 5 | | 30 | 26 | | 31 | 16 | | 32 | 2 | | 33 | 65 | | 34 | 6 | | 35 | 8 | | 36 | 23 | | 37 | 2 | | 38 | 10 | | 39 | 8 | | 40 | 27 | | 41 | 32 | | 42 | 3 | | 43 | 26 | | 44 | 18 | | 45 | 10 | | 46 | 44 | | 47 | 49 | | 48 | 35 | | 49 | 51 |
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| 93.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 118 | | matches | | 0 | "were caked" | | 1 | "been removed" | | 2 | "were curled" | | 3 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 159 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 152 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1039 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.02598652550529355 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007699711260827719 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 9.64 | | std | 7.36 | | cv | 0.764 | | sampleLengths | | 0 | 22 | | 1 | 13 | | 2 | 33 | | 3 | 7 | | 4 | 4 | | 5 | 6 | | 6 | 4 | | 7 | 3 | | 8 | 20 | | 9 | 12 | | 10 | 5 | | 11 | 15 | | 12 | 3 | | 13 | 2 | | 14 | 16 | | 15 | 1 | | 16 | 9 | | 17 | 6 | | 18 | 15 | | 19 | 10 | | 20 | 1 | | 21 | 19 | | 22 | 8 | | 23 | 18 | | 24 | 24 | | 25 | 3 | | 26 | 2 | | 27 | 9 | | 28 | 10 | | 29 | 13 | | 30 | 8 | | 31 | 10 | | 32 | 2 | | 33 | 7 | | 34 | 14 | | 35 | 8 | | 36 | 18 | | 37 | 10 | | 38 | 1 | | 39 | 26 | | 40 | 23 | | 41 | 3 | | 42 | 7 | | 43 | 5 | | 44 | 25 | | 45 | 9 | | 46 | 23 | | 47 | 8 | | 48 | 27 | | 49 | 10 |
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| 87.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5364238410596026 | | totalSentences | 151 | | uniqueOpeners | 81 | |
| 66.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 101 | | matches | | 0 | "Then she checked the neck," | | 1 | "Then dispersed into the tunnel." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 101 | | matches | | 0 | "His shoes, Italian leather, were" | | 1 | "They covered an area roughly" | | 2 | "She flipped to a page" | | 3 | "She pressed her thumb against" | | 4 | "She knelt again." | | 5 | "She turned the hand over." | | 6 | "She'd missed it on first" | | 7 | "She moved to the left" | | 8 | "Her breath caught." | | 9 | "She pulled out her phone" | | 10 | "She lowered her voice" | | 11 | "She walked past the forensics" | | 12 | "They overlapped in patterns that" | | 13 | "She counted at least fifteen" | | 14 | "She checked the shaft entrance." | | 15 | "She tracked the footprints." | | 16 | "They led toward the southern" | | 17 | "Her torch caught something wedged" | | 18 | "She pulled on a fresh" | | 19 | "It spun, slow and deliberate," |
| | ratio | 0.238 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 101 | | matches | | 0 | "The body lay face-down on" | | 1 | "Quinn ducked under the crime" | | 2 | "Forensics had set up portable" | | 3 | "The station still had its" | | 4 | "Camden Town, Southern Branch." | | 5 | "Someone had unsealed it." | | 6 | "DS Hargreaves pointed his torch" | | 7 | "Quinn crouched beside the body." | | 8 | "His shoes, Italian leather, were" | | 9 | "Hargreaves held up an evidence" | | 10 | "Quinn took the bag and" | | 11 | "Quinn followed his nod toward" | | 12 | "A leather satchel spilling papers" | | 13 | "Quinn straightened and walked toward" | | 14 | "The symbols ran in rows," | | 15 | "They covered an area roughly" | | 16 | "The woman from the museum" | | 17 | "Eva tucked a curl behind" | | 18 | "Quinn studied the woman's face." | | 19 | "Eva reached into her satchel" |
| | ratio | 0.703 | |
| 49.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 101 | | matches | | 0 | "As if fifteen people had" |
| | ratio | 0.01 | |
| 73.17% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 4 | | matches | | 0 | "The body lay face-down on the platform, arms stretched wide, fingers curled against tiles that hadn't seen a commuter in forty years." | | 1 | "Forensics had set up portable floodlights that turned the curved walls bone-white, and the shadows they cast made the old adverts, peeling Bovril posters and fa…" | | 2 | "His shoes, Italian leather, were caked in a grey dust that didn't match the platform's grime." | | 3 | "She pulled out her phone and scrolled through photographs with the rapid thumb-swipes of someone who had catalogued a thousand artefacts." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.024 | | leniency | 0.048 | | rawRatio | 0 | | effectiveRatio | 0 | |