| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1031 | | 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) | |
| 75.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1031 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "whisper" | | 2 | "silence" | | 3 | "footsteps" |
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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 | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 5 | | totalWords | 1036 | | ratio | 0.005 | | matches | | 0 | "Unexplained." | | 1 | "unexplained" | | 2 | "Not this time." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 992 | | uniqueNames | 26 | | maxNameDensity | 0.4 | | worstName | "Morris" | | maxWindowNameDensity | 1 | | worstWindowName | "Camden" | | discoveredNames | | Camden | 3 | | Harlow | 2 | | Quinn | 2 | | Saint | 2 | | Christopher | 2 | | Raven | 1 | | Nest | 1 | | Herrera | 2 | | Silas | 2 | | High | 1 | | Street | 2 | | Buck | 1 | | Tube | 1 | | Road | 1 | | Underground | 1 | | General | 1 | | Medical | 1 | | Council | 1 | | Superintendent | 1 | | Morris | 4 | | God-knew-what | 1 | | Static | 1 | | Eight | 1 | | Edwardian | 1 | | Tomás | 4 | | Eighteen | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Silas" | | 6 | "Buck" | | 7 | "Superintendent" | | 8 | "Morris" | | 9 | "Tomás" |
| | places | | 0 | "Camden" | | 1 | "Raven" | | 2 | "High" | | 3 | "Street" | | 4 | "Road" | | 5 | "Underground" | | 6 | "General" | | 7 | "Edwardian" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | 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 | 1036 | | matches | (empty) | |
| 89.15% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 86 | | matches | | 0 | "hung that way" | | 1 | "seen that symbol" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 38.37 | | std | 31.68 | | cv | 0.826 | | sampleLengths | | 0 | 74 | | 1 | 71 | | 2 | 3 | | 3 | 108 | | 4 | 4 | | 5 | 82 | | 6 | 16 | | 7 | 67 | | 8 | 8 | | 9 | 38 | | 10 | 69 | | 11 | 25 | | 12 | 112 | | 13 | 6 | | 14 | 47 | | 15 | 7 | | 16 | 9 | | 17 | 17 | | 18 | 56 | | 19 | 62 | | 20 | 16 | | 21 | 11 | | 22 | 30 | | 23 | 29 | | 24 | 39 | | 25 | 13 | | 26 | 17 |
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| 92.88% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 85 | | matches | | 0 | "been hung" | | 1 | "been struck" | | 2 | "been sealed" |
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| 5.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 137 | | matches | | 0 | "was slowing" | | 1 | "wasn't slowing" | | 2 | "were chasing" | | 3 | "was calling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 86 | | ratio | 0.058 | | matches | | 0 | "The man ahead of her — five-ten, olive skin, a Saint Christopher bouncing against his collarbone with every stride — had clocked her outside the Raven's Nest twenty minutes ago and not slowed since." | | 1 | "A taxi laid on its horn; she waved her warrant card without looking, and the driver swallowed whatever he'd been about to shout." | | 2 | "\"Herrera!\" Her voice cracked out across the street and he glanced back — just once, just long enough for her to see the expression on his face." | | 3 | "The silence struck first — thick, wadded, the kind of silence that belonged underground." | | 4 | "Beneath it, a smaller mark — a single rough line like a tally." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 991 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03229061553985873 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008072653884964682 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 12.05 | | std | 10.18 | | cv | 0.845 | | sampleLengths | | 0 | 40 | | 1 | 34 | | 2 | 2 | | 3 | 33 | | 4 | 10 | | 5 | 13 | | 6 | 13 | | 7 | 3 | | 8 | 11 | | 9 | 23 | | 10 | 14 | | 11 | 3 | | 12 | 20 | | 13 | 2 | | 14 | 22 | | 15 | 3 | | 16 | 4 | | 17 | 6 | | 18 | 4 | | 19 | 28 | | 20 | 35 | | 21 | 3 | | 22 | 6 | | 23 | 4 | | 24 | 6 | | 25 | 16 | | 26 | 27 | | 27 | 5 | | 28 | 4 | | 29 | 2 | | 30 | 3 | | 31 | 26 | | 32 | 8 | | 33 | 5 | | 34 | 8 | | 35 | 25 | | 36 | 5 | | 37 | 8 | | 38 | 16 | | 39 | 8 | | 40 | 32 | | 41 | 25 | | 42 | 17 | | 43 | 13 | | 44 | 26 | | 45 | 19 | | 46 | 10 | | 47 | 1 | | 48 | 11 | | 49 | 15 |
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| 74.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5 | | totalSentences | 86 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 73 | | matches | | 0 | "Then he slipped inside and" | | 1 | "Especially not when the man" | | 2 | "Especially not when your partner" | | 3 | "Then the smell: damp brick," | | 4 | "Somewhere below, very faint, she" | | 5 | "Then she started down the" |
| | ratio | 0.082 | |
| 77.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 73 | | matches | | 0 | "She'd memorized the name off" | | 1 | "He'd simply turned and moved," | | 2 | "She was better." | | 3 | "Her boots slapped across the" | | 4 | "She pushed harder." | | 5 | "He was slowing." | | 6 | "He had to be." | | 7 | "He'd been sprinting nearly a" | | 8 | "He crossed beneath a dripping" | | 9 | "Her voice cracked out across" | | 10 | "It wasn't fear of her." | | 11 | "It was something else." | | 12 | "She reached it eight seconds" | | 13 | "She stood with one hand" | | 14 | "She knew the unwritten rules" | | 15 | "You didn't follow a running" | | 16 | "You didn't go down into" | | 17 | "She could still see the" | | 18 | "She had followed every lead" | | 19 | "She pressed the plywood." |
| | ratio | 0.356 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 73 | | matches | | 0 | "Rain came down in sheets" | | 1 | "The man ahead of her" | | 2 | "She'd memorized the name off" | | 3 | "He'd simply turned and moved," | | 4 | "She was better." | | 5 | "Her boots slapped across the" | | 6 | "A taxi laid on its" | | 7 | "Tomás darted left into Buck" | | 8 | "She pushed harder." | | 9 | "The leather watch on her" | | 10 | "He was slowing." | | 11 | "He had to be." | | 12 | "He'd been sprinting nearly a" | | 13 | "He crossed beneath a dripping" | | 14 | "Camden Road Underground." | | 15 | "Plywood over the arch." | | 16 | "A faded notice in council" | | 17 | "Tomás put his shoulder into" | | 18 | "Her voice cracked out across" | | 19 | "It wasn't fear of her." |
| | ratio | 0.685 | |
| 68.49% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 73 | | matches | | 0 | "And, lately, a man with" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 1 | | matches | | 0 | "Rain came down in sheets across Camden, the kind that drummed on tin and turned gutters into rivers, and Harlow Quinn ran with it flattening her cropped hair ag…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, voice low and flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |