| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1397 | | 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) | |
| 89.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1397 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "velvet" | | 1 | "churned" | | 2 | "pulse" |
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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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1397 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1077 | | uniqueNames | 12 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Fisher" | | discoveredNames | | Kettle | 6 | | Quinn | 11 | | Kentish | 1 | | Town | 1 | | Borough | 1 | | Market | 1 | | Morris | 1 | | Fisher | 4 | | Gregory | 1 | | Sallow | 1 | | One | 1 | | Omega | 1 |
| | persons | | 0 | "Kettle" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Fisher" | | 4 | "Gregory" | | 5 | "Sallow" |
| | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "Borough" | | 3 | "Market" |
| | globalScore | 0.989 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1397 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 119 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 24.51 | | std | 23.8 | | cv | 0.971 | | sampleLengths | | 0 | 21 | | 1 | 45 | | 2 | 34 | | 3 | 4 | | 4 | 1 | | 5 | 31 | | 6 | 25 | | 7 | 94 | | 8 | 5 | | 9 | 20 | | 10 | 3 | | 11 | 6 | | 12 | 65 | | 13 | 13 | | 14 | 7 | | 15 | 24 | | 16 | 6 | | 17 | 30 | | 18 | 60 | | 19 | 9 | | 20 | 2 | | 21 | 10 | | 22 | 60 | | 23 | 8 | | 24 | 83 | | 25 | 9 | | 26 | 6 | | 27 | 33 | | 28 | 35 | | 29 | 2 | | 30 | 15 | | 31 | 11 | | 32 | 74 | | 33 | 52 | | 34 | 8 | | 35 | 15 | | 36 | 51 | | 37 | 6 | | 38 | 71 | | 39 | 18 | | 40 | 10 | | 41 | 3 | | 42 | 22 | | 43 | 3 | | 44 | 38 | | 45 | 11 | | 46 | 3 | | 47 | 81 | | 48 | 5 | | 49 | 14 |
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| 82.13% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 91 | | matches | | 0 | "being made" | | 1 | "been sealed" | | 2 | "was churned" | | 3 | "been polished" | | 4 | "were folded" | | 5 | "was told" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 157 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 119 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 756 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.026455026455026454 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 11.74 | | std | 10.92 | | cv | 0.93 | | sampleLengths | | 0 | 21 | | 1 | 6 | | 2 | 39 | | 3 | 8 | | 4 | 26 | | 5 | 4 | | 6 | 1 | | 7 | 10 | | 8 | 21 | | 9 | 5 | | 10 | 20 | | 11 | 10 | | 12 | 48 | | 13 | 8 | | 14 | 28 | | 15 | 5 | | 16 | 20 | | 17 | 3 | | 18 | 6 | | 19 | 8 | | 20 | 14 | | 21 | 16 | | 22 | 7 | | 23 | 20 | | 24 | 7 | | 25 | 6 | | 26 | 7 | | 27 | 10 | | 28 | 14 | | 29 | 6 | | 30 | 4 | | 31 | 26 | | 32 | 4 | | 33 | 10 | | 34 | 1 | | 35 | 45 | | 36 | 5 | | 37 | 4 | | 38 | 2 | | 39 | 10 | | 40 | 21 | | 41 | 4 | | 42 | 2 | | 43 | 33 | | 44 | 3 | | 45 | 5 | | 46 | 7 | | 47 | 35 | | 48 | 2 | | 49 | 17 |
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| 89.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5630252100840336 | | totalSentences | 119 | | uniqueOpeners | 67 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Then the compass needle swung," |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 72 | | matches | | 0 | "He'd been an old-school detective" | | 1 | "His arms lay above his" | | 2 | "She looked at the dust" | | 3 | "He clicked his pen" | | 4 | "He didn't want to." | | 5 | "He came anyway, knees cracking," | | 6 | "His pen had stopped clicking." | | 7 | "She scraped a fleck with" | | 8 | "It came up waxy and" | | 9 | "She crouched by the nearest" | | 10 | "She stood up." | | 11 | "Their eyes were milk white" | | 12 | "Her mouth had gone dry." | | 13 | "She let it." | | 14 | "It pointed at the tunnel" | | 15 | "She turned in a slow" | | 16 | "She looked at the raised" | | 17 | "They'd sat at 4:11 for" | | 18 | "She looked at the dead" | | 19 | "Her glasses caught what was" |
| | ratio | 0.278 | |
| 64.17% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 72 | | matches | | 0 | "The body lay in the" | | 1 | "That was the first thing" | | 2 | "Quinn crouched beside the man's" | | 3 | "DS Kettle said from behind" | | 4 | "Quinn lifted the trouser hem" | | 5 | "Kettle exhaled through his nose." | | 6 | "He'd been an old-school detective" | | 7 | "South Kentish Town smelled of" | | 8 | "The station had been sealed" | | 9 | "Cables sagged from the ceiling" | | 10 | "Quinn moved up the body" | | 11 | "His arms lay above his" | | 12 | "The posture of a man" | | 13 | "She looked at the dust" | | 14 | "That was the second thing" | | 15 | "He clicked his pen" | | 16 | "He didn't want to." | | 17 | "He came anyway, knees cracking," | | 18 | "The silt was churned." | | 19 | "A hundred years of dust" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 81.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 3 | | matches | | 0 | "A hundred years of dust beaten down into a hard grey crust the way a footpath forms across a park, with the crust worn brightest along a line that ran the lengt…" | | 1 | "Six hours later she'd been standing in a car park holding his warrant card and nobody had ever given her a sentence about it that made sense." | | 2 | "Someone had cut sigils into the face around the cardinal points, fine work, not decorative, the lines interrupting each other in a way that looked deliberate." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.22 | | leniency | 0.439 | | rawRatio | 0 | | effectiveRatio | 0 | |