| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1372 | | totalAiIsmAdverbs | 2 | | 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) | |
| 59.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1372 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silence" | | 1 | "footfall" | | 2 | "footsteps" | | 3 | "echo" | | 4 | "furrowed" | | 5 | "flicked" | | 6 | "pulse" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 154 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 154 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 157 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 7 | | markdownWords | 48 | | totalWords | 1372 | | ratio | 0.035 | | matches | | 0 | "CRISPY DUCK, EGG FRIED RICE, PRAWN CRACKERS x2. DELIVER TO THE OAK STONES, RICHMOND PARK. FOR LAILA." | | 1 | "Good. Brilliant. Totally normal." | | 2 | "An echo. Bracken does weird things to sound." | | 3 | "Right. Okay. Grand." | | 4 | "Layla" | | 5 | "never create evidence you can't take back" | | 6 | "I didn't step in. I didn't step in." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1335 | | uniqueNames | 17 | | maxNameDensity | 0.22 | | worstName | "Yu-Fei" | | maxWindowNameDensity | 1 | | worstWindowName | "Yu-Fei" | | discoveredNames | | Golden | 2 | | Empress | 2 | | Yu-Fei | 3 | | Laila | 2 | | London | 1 | | Crisp | 1 | | Park | 1 | | Robin | 1 | | Hood | 1 | | Gate | 1 | | Kingston | 2 | | Heartstone | 1 | | Heathrow | 1 | | Evan | 1 | | Except | 1 | | November | 1 | | Cardiff | 1 |
| | persons | | 0 | "Empress" | | 1 | "Yu-Fei" | | 2 | "Laila" | | 3 | "Crisp" | | 4 | "Robin" | | 5 | "Hood" | | 6 | "Gate" | | 7 | "Heartstone" | | 8 | "Evan" |
| | places | | 0 | "Golden" | | 1 | "London" | | 2 | "Park" | | 3 | "Kingston" | | 4 | "Heathrow" | | 5 | "November" | | 6 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 91.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a rumour" | | 1 | "appeared all at once" |
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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 | 1372 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 157 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 24.95 | | std | 20.39 | | cv | 0.818 | | sampleLengths | | 0 | 19 | | 1 | 51 | | 2 | 30 | | 3 | 46 | | 4 | 7 | | 5 | 71 | | 6 | 8 | | 7 | 55 | | 8 | 4 | | 9 | 44 | | 10 | 7 | | 11 | 66 | | 12 | 2 | | 13 | 7 | | 14 | 41 | | 15 | 28 | | 16 | 23 | | 17 | 8 | | 18 | 7 | | 19 | 46 | | 20 | 8 | | 21 | 51 | | 22 | 13 | | 23 | 8 | | 24 | 56 | | 25 | 25 | | 26 | 6 | | 27 | 44 | | 28 | 27 | | 29 | 13 | | 30 | 20 | | 31 | 3 | | 32 | 61 | | 33 | 8 | | 34 | 21 | | 35 | 13 | | 36 | 55 | | 37 | 9 | | 38 | 36 | | 39 | 12 | | 40 | 9 | | 41 | 51 | | 42 | 3 | | 43 | 53 | | 44 | 8 | | 45 | 4 | | 46 | 56 | | 47 | 6 | | 48 | 32 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 154 | | matches | | 0 | "was expected" | | 1 | "were gone" |
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| 66.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 199 | | matches | | 0 | "were going" | | 1 | "was still walking" | | 2 | "wasn't making" | | 3 | "was glowing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 157 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1345 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.03345724907063197 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0044609665427509295 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 157 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 157 | | mean | 8.74 | | std | 7.21 | | cv | 0.826 | | sampleLengths | | 0 | 19 | | 1 | 8 | | 2 | 20 | | 3 | 14 | | 4 | 7 | | 5 | 2 | | 6 | 1 | | 7 | 6 | | 8 | 23 | | 9 | 10 | | 10 | 2 | | 11 | 4 | | 12 | 16 | | 13 | 14 | | 14 | 3 | | 15 | 4 | | 16 | 8 | | 17 | 24 | | 18 | 22 | | 19 | 11 | | 20 | 6 | | 21 | 8 | | 22 | 14 | | 23 | 16 | | 24 | 5 | | 25 | 20 | | 26 | 1 | | 27 | 1 | | 28 | 2 | | 29 | 3 | | 30 | 15 | | 31 | 26 | | 32 | 7 | | 33 | 2 | | 34 | 4 | | 35 | 3 | | 36 | 16 | | 37 | 3 | | 38 | 5 | | 39 | 13 | | 40 | 3 | | 41 | 6 | | 42 | 11 | | 43 | 2 | | 44 | 7 | | 45 | 10 | | 46 | 2 | | 47 | 4 | | 48 | 25 | | 49 | 9 |
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| 79.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5064102564102564 | | totalSentences | 156 | | uniqueOpeners | 79 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 123 | | matches | | 0 | "Too many of them." | | 1 | "Of course I did." | | 2 | "Just a trunk, grey and" | | 3 | "Just the gap, the flowers," | | 4 | "Only a darker patch where" | | 5 | "Exactly as I remembered it" | | 6 | "Too many joints." |
| | ratio | 0.057 | |
| 80.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 123 | | matches | | 0 | "I checked it again under" | | 1 | "Her mouth had tightened around" | | 2 | "I went anyway." | | 3 | "I'd left the scooter chained" | | 4 | "I kept my free hand" | | 5 | "It had arrived in a" | | 6 | "My map app showed a" | | 7 | "I took the left fork" | | 8 | "I looked down." | | 9 | "My legs pushed the fronds" | | 10 | "They bent, and sprang back," | | 11 | "I didn't turn around." | | 12 | "I said to the dark" | | 13 | "My voice came out flat," | | 14 | "I walked on." | | 15 | "Their branches knotted overhead, and" | | 16 | "My torch beam carved a" | | 17 | "I swung the torch." | | 18 | "I held the beam on" | | 19 | "I didn't point the torch" |
| | ratio | 0.35 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 123 | | matches | | 0 | "The prawn crackers were going" | | 1 | "I checked it again under" | | 2 | "The Golden Empress logo, a" | | 3 | "Nobody in London knew that" | | 4 | "Nobody had called me that" | | 5 | "Yu-Fei had said, tapping the" | | 6 | "Her mouth had tightened around" | | 7 | "I went anyway." | | 8 | "Richmond Park after closing was" | | 9 | "I'd left the scooter chained" | | 10 | "The bracken rose waist-high on" | | 11 | "The city felt like a" | | 12 | "I kept my free hand" | | 13 | "The Heartstone sat under my" | | 14 | "It had arrived in a" | | 15 | "Tonight it had started warming" | | 16 | "The path forked." | | 17 | "My map app showed a" | | 18 | "I took the left fork" | | 19 | "Silence I could handle." |
| | ratio | 0.667 | |
| 40.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 123 | | matches | | 0 | "As if something else's heart" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "I took the left fork because the right one smelled of fox and something sweeter underneath, a rotten-honey stink that coated the back of my throat." | | 1 | "The next, a ring of standing stones that weren't stone at all but oak, petrified and grey-black, each one taller than me and leaning inward like mourners at a g…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.5 | | effectiveRatio | 0.333 | |