| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1083 | | 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) | |
| 81.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1083 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "pulse" | | 1 | "treacherous" | | 2 | "jaw clenched" | | 3 | "unsettled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 96.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 7 | | totalWords | 1083 | | ratio | 0.006 | | matches | | 0 | "the Market" | | 1 | "Focus." | | 2 | "Call it in." | | 3 | "unresolved" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1016 | | uniqueNames | 10 | | maxNameDensity | 0.69 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Camden" | | discoveredNames | | Berwick | 1 | | Street | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Camden | 3 | | Tube | 2 | | Morris | 3 | | Quinn | 5 | | Herrera | 7 |
| | persons | | 0 | "Morris" | | 1 | "Quinn" | | 2 | "Herrera" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "Charing" | | 3 | "Cross" | | 4 | "Road" | | 5 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1083 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 33.84 | | std | 33.14 | | cv | 0.979 | | sampleLengths | | 0 | 29 | | 1 | 2 | | 2 | 9 | | 3 | 89 | | 4 | 51 | | 5 | 1 | | 6 | 57 | | 7 | 8 | | 8 | 77 | | 9 | 3 | | 10 | 72 | | 11 | 15 | | 12 | 61 | | 13 | 13 | | 14 | 31 | | 15 | 3 | | 16 | 126 | | 17 | 5 | | 18 | 73 | | 19 | 36 | | 20 | 17 | | 21 | 5 | | 22 | 10 | | 23 | 24 | | 24 | 8 | | 25 | 50 | | 26 | 23 | | 27 | 6 | | 28 | 75 | | 29 | 3 | | 30 | 92 | | 31 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 173 | | matches | | 0 | "wasn't slowing" | | 1 | "was chasing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1025 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.0351219512195122 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005853658536585366 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 15.7 | | std | 12.26 | | cv | 0.781 | | sampleLengths | | 0 | 19 | | 1 | 10 | | 2 | 2 | | 3 | 2 | | 4 | 4 | | 5 | 3 | | 6 | 17 | | 7 | 32 | | 8 | 9 | | 9 | 31 | | 10 | 18 | | 11 | 19 | | 12 | 2 | | 13 | 12 | | 14 | 1 | | 15 | 15 | | 16 | 14 | | 17 | 20 | | 18 | 8 | | 19 | 8 | | 20 | 26 | | 21 | 18 | | 22 | 4 | | 23 | 23 | | 24 | 6 | | 25 | 3 | | 26 | 13 | | 27 | 5 | | 28 | 31 | | 29 | 23 | | 30 | 1 | | 31 | 14 | | 32 | 39 | | 33 | 8 | | 34 | 14 | | 35 | 13 | | 36 | 20 | | 37 | 11 | | 38 | 3 | | 39 | 25 | | 40 | 36 | | 41 | 13 | | 42 | 52 | | 43 | 5 | | 44 | 14 | | 45 | 28 | | 46 | 31 | | 47 | 5 | | 48 | 1 | | 49 | 4 |
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| 76.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4927536231884058 | | totalSentences | 69 | | uniqueOpeners | 34 | |
| 60.61% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 55 | | matches | | | ratio | 0.018 | |
| 96.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 55 | | matches | | 0 | "She'd been tailing him for" | | 1 | "He vaulted a low fence" | | 2 | "She scrambled after him, palm" | | 3 | "she muttered, and ran" | | 4 | "She caught sight of him" | | 5 | "She didn't slow down." | | 6 | "He knew these streets better" | | 7 | "She followed on instinct, skidding" | | 8 | "He'd led her all the" | | 9 | "Her hand found her radio" | | 10 | "She went down the steps." | | 11 | "Her torch clicked on, the" | | 12 | "He said it plainly, no" | | 13 | "He glanced past his shoulder," | | 14 | "He didn't answer." | | 15 | "He reached into his coat" | | 16 | "She thought of Morris, of" |
| | ratio | 0.309 | |
| 41.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 55 | | matches | | 0 | "Quinn's boots hit the pavement" | | 1 | "Herrera had a thirty-metre lead" | | 2 | "Nobody ever did." | | 3 | "Rain sheeted down Berwick Street" | | 4 | "She'd been tailing him for" | | 5 | "Tonight he'd finally slipped, made" | | 6 | "Herrera cut left down an" | | 7 | "Quinn followed, lungs burning, the" | | 8 | "Morris used to tell her" | | 9 | "He vaulted a low fence" | | 10 | "She scrambled after him, palm" | | 11 | "Herrera had already vanished round" | | 12 | "she muttered, and ran" | | 13 | "She caught sight of him" | | 14 | "A bus hissed past, throwing" | | 15 | "She didn't slow down." | | 16 | "Puddles were how you lost" | | 17 | "A woman with a shopping" | | 18 | "Herrera didn't even glance back." | | 19 | "He knew these streets better" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 10 | | matches | | 0 | "Quinn's boots hit the pavement in a rhythm that matched her pulse, hard and fast, no room for hesitation." | | 1 | "She'd been tailing him for six days, watching him slip in and out of derelict buildings with a medical bag that shouldn't have existed anymore, not since the NH…" | | 2 | "A bus hissed past, throwing up a wave of gutter water that soaked her trousers to the knee." | | 3 | "Six days of surveillance and all she had were rumours, a name half-heard through a wall, whispers of a night market that didn't appear on any map, that moved li…" | | 4 | "The kind of thing DS Morris used to scribble in the margins of his notebook, right before he disappeared off a rooftop that should have been empty and came back…" | | 5 | "Beyond it, darkness pooled thick enough to taste, an old platform smell of dust and ozone and something underneath it that reminded her of a butcher's cold room…" | | 6 | "Music that didn't belong to any radio station she'd ever heard, all pipe and string and something that thrummed under her sternum like a second heartbeat." | | 7 | "He glanced past his shoulder, towards the light, towards the sound of a market that had no business existing under a condemned Tube station in Camden." | | 8 | "The wall began to close behind him, tile knitting back into tile, and eighteen years of procedure screamed at her to stop, to hold position, to wait for backup …" | | 9 | "She thought of Morris, of the case file marked *unresolved* that she still kept in her bottom drawer, of every question that had gone unanswered for three years…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 59.09% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 11 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.333 | | effectiveRatio | 0.182 | |