| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1317 | | 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) | |
| 81.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1317 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "echoed" | | 1 | "scanning" | | 2 | "weight" | | 3 | "depths" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "blood went cold" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 132 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 132 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1319 | | ratio | 0 | | matches | (empty) | |
| 93.75% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 1 | | matches | | 0 | "Victorian-era, she guessed, from the geometric patterns still visible beneath decades of grime." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 1158 | | uniqueNames | 18 | | maxNameDensity | 1.3 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Chapter | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Friday | 1 | | Lock | 1 | | Tavern | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Twelve | 2 | | Morris | 3 | | Pocket | 1 | | Oxford | 1 | | Harlow | 15 | | London | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Morris" | | 1 | "Harlow" | | 2 | "Market" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Lock" | | 4 | "Chalk" | | 5 | "Farm" | | 6 | "Road" | | 7 | "Twelve" | | 8 | "Oxford" | | 9 | "London" | | 10 | "Veil" |
| | globalScore | 0.852 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | 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 | 1319 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 25.37 | | std | 19.12 | | cv | 0.754 | | sampleLengths | | 0 | 3 | | 1 | 11 | | 2 | 50 | | 3 | 43 | | 4 | 3 | | 5 | 7 | | 6 | 35 | | 7 | 15 | | 8 | 7 | | 9 | 18 | | 10 | 10 | | 11 | 10 | | 12 | 48 | | 13 | 44 | | 14 | 8 | | 15 | 61 | | 16 | 72 | | 17 | 14 | | 18 | 33 | | 19 | 6 | | 20 | 22 | | 21 | 21 | | 22 | 6 | | 23 | 23 | | 24 | 44 | | 25 | 62 | | 26 | 40 | | 27 | 45 | | 28 | 6 | | 29 | 34 | | 30 | 69 | | 31 | 48 | | 32 | 4 | | 33 | 11 | | 34 | 11 | | 35 | 28 | | 36 | 25 | | 37 | 32 | | 38 | 20 | | 39 | 5 | | 40 | 8 | | 41 | 55 | | 42 | 15 | | 43 | 4 | | 44 | 2 | | 45 | 30 | | 46 | 6 | | 47 | 41 | | 48 | 34 | | 49 | 39 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 132 | | matches | | 0 | "been ripped" | | 1 | "were bricked" | | 2 | "were gone" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 211 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 147 | | ratio | 0.014 | | matches | | 0 | "A rusted sign clung to the wall by a single bolt: CAMDEN TOWN — NORTHERN LINE." | | 1 | "She'd found his notes stuffed inside a copy of the Pocket Oxford, pages filled with his cramped handwriting and a single phrase underlined three times: BONE TOKEN — ENTRY REQUIREMENT." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1166 | | adjectiveStacks | 2 | | stackExamples | | 0 | "under grief-fuelled nonsense." | | 1 | "open, revealing dead-end" |
| | adverbCount | 28 | | adverbRatio | 0.024013722126929673 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004288164665523156 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 8.97 | | std | 6.69 | | cv | 0.746 | | sampleLengths | | 0 | 3 | | 1 | 11 | | 2 | 4 | | 3 | 3 | | 4 | 14 | | 5 | 29 | | 6 | 10 | | 7 | 2 | | 8 | 21 | | 9 | 10 | | 10 | 3 | | 11 | 3 | | 12 | 4 | | 13 | 14 | | 14 | 11 | | 15 | 4 | | 16 | 2 | | 17 | 4 | | 18 | 1 | | 19 | 3 | | 20 | 11 | | 21 | 3 | | 22 | 4 | | 23 | 18 | | 24 | 1 | | 25 | 9 | | 26 | 2 | | 27 | 8 | | 28 | 12 | | 29 | 10 | | 30 | 14 | | 31 | 5 | | 32 | 7 | | 33 | 15 | | 34 | 5 | | 35 | 2 | | 36 | 16 | | 37 | 3 | | 38 | 3 | | 39 | 8 | | 40 | 15 | | 41 | 21 | | 42 | 5 | | 43 | 2 | | 44 | 2 | | 45 | 16 | | 46 | 4 | | 47 | 6 | | 48 | 3 | | 49 | 4 |
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| 67.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.43537414965986393 | | totalSentences | 147 | | uniqueOpeners | 64 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 116 | | matches | | 0 | "Of course he didn't." | | 1 | "Then another shape." | | 2 | "Then a third." | | 3 | "Somewhere beyond them, Morris's ghost" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 116 | | matches | | 0 | "Her lungs burned." | | 1 | "He turned left into the" | | 2 | "Her worn leather watch dug" | | 3 | "He didn't stop." | | 4 | "She barely registered either." | | 5 | "Her radio crackled." | | 6 | "She'd have him or lose" | | 7 | "She blinked it away and" | | 8 | "She knew this place." | | 9 | "She'd found his notes stuffed" | | 10 | "She'd laughed at it then." | | 11 | "She wasn't laughing now." | | 12 | "Her suspect had gone down" | | 13 | "She'd watched him vanish into" | | 14 | "It clicked into place with" | | 15 | "She thumbed her radio." | | 16 | "She switched the radio off" | | 17 | "She'd be finished in twelve" | | 18 | "She gripped the handrail, but" | | 19 | "She counted them because counting" |
| | ratio | 0.284 | |
| 67.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 116 | | matches | | 0 | "# Chapter 7" 7/1/2001, 12:00:00 AM | | 1 | "The knife caught the streetlight" | | 2 | "Harlow didn't slow down." | | 3 | "Her lungs burned." | | 4 | "The man ahead moved with" | | 5 | "He turned left into the" | | 6 | "Her worn leather watch dug" | | 7 | "He didn't stop." | | 8 | "The alley opened onto Camden" | | 9 | "Harlow pushed through a knot" | | 10 | "Someone spilled a pint." | | 11 | "She barely registered either." | | 12 | "The grey hoodie." | | 13 | "Her radio crackled." | | 14 | "She'd have him or lose" | | 15 | "The suspect cut right, down" | | 16 | "A chain-link fence rattled as" | | 17 | "Harlow followed, the metal catching" | | 18 | "Rain ran into her eyes." | | 19 | "She blinked it away and" |
| | ratio | 0.784 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 116 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 5 | | matches | | 0 | "The man ahead moved with that fluid, unnatural grace she'd learned to recognise over eighteen years of service, the kind that didn't belong to someone running o…" | | 1 | "The fluorescent strips that would once have lit this stairwell hung dark and broken, like the ribs of some picked-clean animal." | | 2 | "Someone had strung bare bulbs along the ceiling at irregular intervals, their light casting harsh, overlapping shadows that made the walls seem to breathe." | | 3 | "The woman watched her with eyes that reflected the bare bulbs above like a cat's, a yellowish gleam that didn't belong to any mammal Harlow could name." | | 4 | "She was taller than Harlow expected, thin as a willow switch, dressed in layers of dark fabric that moved when she did in ways fabric shouldn't." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "the woman repeated (repeat)" |
| | dialogueSentences | 24 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.25 | | effectiveRatio | 0.083 | |