| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "She moved around [around]" |
| | dialogueSentences | 49 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.071 | | effectiveRatio | 0.041 | |
| 96.93% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1630 | | 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) | |
| 78.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1630 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "pulse" | | 1 | "database" | | 2 | "traced" | | 3 | "silence" | | 4 | "etched" | | 5 | "navigating" |
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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 | 1 | | 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 | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 77 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1630 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 96.04% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1112 | | uniqueNames | 13 | | maxNameDensity | 1.08 | | worstName | "Greer" | | maxWindowNameDensity | 2 | | worstWindowName | "Greer" | | discoveredNames | | Quinn | 6 | | Arches | 1 | | Greer | 12 | | Bermondsey | 1 | | Dust | 2 | | Practice | 1 | | Silence | 1 | | Six | 3 | | Kentish | 1 | | Town | 1 | | Shade | 1 | | Camden | 2 | | Warm | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Greer" | | 2 | "Silence" |
| | places | | 0 | "Bermondsey" | | 1 | "Dust" | | 2 | "Kentish" | | 3 | "Town" | | 4 | "Camden" |
| | globalScore | 0.96 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1630 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 153 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 66 | | mean | 24.7 | | std | 22.66 | | cv | 0.918 | | sampleLengths | | 0 | 51 | | 1 | 69 | | 2 | 6 | | 3 | 36 | | 4 | 26 | | 5 | 3 | | 6 | 12 | | 7 | 1 | | 8 | 1 | | 9 | 30 | | 10 | 9 | | 11 | 34 | | 12 | 60 | | 13 | 4 | | 14 | 2 | | 15 | 5 | | 16 | 52 | | 17 | 5 | | 18 | 8 | | 19 | 27 | | 20 | 85 | | 21 | 28 | | 22 | 17 | | 23 | 9 | | 24 | 45 | | 25 | 8 | | 26 | 7 | | 27 | 41 | | 28 | 5 | | 29 | 16 | | 30 | 46 | | 31 | 87 | | 32 | 1 | | 33 | 32 | | 34 | 31 | | 35 | 4 | | 36 | 63 | | 37 | 6 | | 38 | 4 | | 39 | 55 | | 40 | 16 | | 41 | 14 | | 42 | 68 | | 43 | 14 | | 44 | 24 | | 45 | 12 | | 46 | 10 | | 47 | 71 | | 48 | 5 | | 49 | 35 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 118 | | matches | | 0 | "were laced" | | 1 | "been posted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 183 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 153 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1113 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.03593890386343217 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004492362982929021 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 10.65 | | std | 10.03 | | cv | 0.942 | | sampleLengths | | 0 | 21 | | 1 | 2 | | 2 | 28 | | 3 | 14 | | 4 | 1 | | 5 | 12 | | 6 | 15 | | 7 | 20 | | 8 | 7 | | 9 | 6 | | 10 | 21 | | 11 | 3 | | 12 | 8 | | 13 | 4 | | 14 | 15 | | 15 | 11 | | 16 | 3 | | 17 | 8 | | 18 | 4 | | 19 | 1 | | 20 | 1 | | 21 | 8 | | 22 | 22 | | 23 | 9 | | 24 | 13 | | 25 | 6 | | 26 | 15 | | 27 | 7 | | 28 | 8 | | 29 | 1 | | 30 | 7 | | 31 | 6 | | 32 | 31 | | 33 | 4 | | 34 | 2 | | 35 | 3 | | 36 | 2 | | 37 | 29 | | 38 | 7 | | 39 | 16 | | 40 | 5 | | 41 | 8 | | 42 | 5 | | 43 | 22 | | 44 | 7 | | 45 | 15 | | 46 | 5 | | 47 | 3 | | 48 | 20 | | 49 | 10 |
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| 76.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.5032679738562091 | | totalSentences | 153 | | uniqueOpeners | 77 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 103 | | matches | | 0 | "Then the light found the" | | 1 | "All heading in." | | 2 | "Somewhere deeper in the tunnel," | | 3 | "Then twitched again." | | 4 | "Then a second." |
| | ratio | 0.049 | |
| 80.19% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 103 | | matches | | 0 | "He sat propped against a" | | 1 | "His shoes were polished." | | 2 | "He stopped two paces short" | | 3 | "Her watch ticked against her" | | 4 | "She counted the cuts on" | | 5 | "She had seen hesitation marks" | | 6 | "She had seen them on" | | 7 | "She pointed her torch at" | | 8 | "He moved off instead toward" | | 9 | "She did not touch them." | | 10 | "She counted instead." | | 11 | "She had found two more" | | 12 | "She had counted them on" | | 13 | "She played the torch across" | | 14 | "Her knee clicked, an old" | | 15 | "He was younger by a" | | 16 | "She liked him well enough." | | 17 | "He did not waste words" | | 18 | "He gestured at the stalls" | | 19 | "She crouched at the eastern" |
| | ratio | 0.35 | |
| 95.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 103 | | matches | | 0 | "The last three steps had" | | 1 | "Torchlight swept the platform and" | | 2 | "Jars of grey powder, bundles" | | 3 | "Someone had chalked a wide" | | 4 | "The wax had burned down" | | 5 | "He sat propped against a" | | 6 | "A good coat." | | 7 | "Wool, dark, money in the" | | 8 | "His shoes were polished." | | 9 | "DS Greer came down behind" | | 10 | "He stopped two paces short" | | 11 | "Greer shrugged his coat tighter" | | 12 | "Quinn tilted her wrist so" | | 13 | "Her watch ticked against her" | | 14 | "She counted the cuts on" | | 15 | "She had seen hesitation marks" | | 16 | "She had seen them on" | | 17 | "These were not those." | | 18 | "Greer peered closer." | | 19 | "She pointed her torch at" |
| | ratio | 0.728 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "He sat propped against a tiled pillar with his hands folded in his lap, ankles crossed, head tilted as though listening." | | 1 | "The face carried etched marks she recognised from the chalk circle, tiny and precise, the work of someone who had done it ten thousand times." | | 2 | "Down the tunnel, past the tape and past the constable who should have been posted at the entrance and was not, gravel shifted under a slow and careful foot." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.041 | | leniency | 0.082 | | rawRatio | 0 | | effectiveRatio | 0 | |