| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1783 | | 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) | |
| 60.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1783 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "gloom" | | 1 | "chill" | | 2 | "silk" | | 3 | "database" | | 4 | "standard" | | 5 | "traced" | | 6 | "churned" | | 7 | "amidst" | | 8 | "crystal" | | 9 | "etched" | | 10 | "magnetic" | | 11 | "echoed" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 84 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1778 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1232 | | uniqueNames | 16 | | maxNameDensity | 1.06 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 2 | | Street | 2 | | Harlow | 1 | | Quinn | 13 | | London | 1 | | Metropolitan | 1 | | Police | 1 | | Sergeant | 2 | | Callum | 1 | | Ross | 12 | | Tube | 1 | | Victorian | 1 | | Rotherhithe | 1 | | Detective | 3 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Sergeant" | | 4 | "Callum" | | 5 | "Ross" | | 6 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.972 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | 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 | 1778 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 29.63 | | std | 21.13 | | cv | 0.713 | | sampleLengths | | 0 | 89 | | 1 | 51 | | 2 | 19 | | 3 | 36 | | 4 | 7 | | 5 | 56 | | 6 | 51 | | 7 | 49 | | 8 | 28 | | 9 | 49 | | 10 | 6 | | 11 | 12 | | 12 | 31 | | 13 | 19 | | 14 | 7 | | 15 | 10 | | 16 | 84 | | 17 | 40 | | 18 | 5 | | 19 | 10 | | 20 | 7 | | 21 | 30 | | 22 | 24 | | 23 | 25 | | 24 | 68 | | 25 | 16 | | 26 | 9 | | 27 | 6 | | 28 | 7 | | 29 | 72 | | 30 | 12 | | 31 | 17 | | 32 | 56 | | 33 | 20 | | 34 | 14 | | 35 | 23 | | 36 | 32 | | 37 | 71 | | 38 | 30 | | 39 | 55 | | 40 | 10 | | 41 | 15 | | 42 | 22 | | 43 | 31 | | 44 | 25 | | 45 | 5 | | 46 | 9 | | 47 | 7 | | 48 | 60 | | 49 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 81 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 189 | | matches | (empty) | |
| 9.35% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 2 | | flaggedSentences | 5 | | totalSentences | 107 | | ratio | 0.047 | | matches | | 0 | "Above the asphalt, an autumn full moon bleached the empty London roofs; inside this buried cavern, portable police floodlights carved harsh white corridors through a century of subterranean gloom." | | 1 | "She drew steel tweezers from her coat pouch and lifted a shard of hand-blown violet glass thick as a coin; viscous silver paste coated its concave inner curve." | | 2 | "Beneath the scratched crystal lens, acid-etched protective sigils—interlocking triangles and barbed wards—replaced the four cardinal points." | | 3 | "Cold recognition tightened her chest—a flooded Rotherhithe warehouse three winters past, rain hammering a corrugated roof while she searched an empty dockside room for Detective Sergeant Morris, the air thick with this identical tang of scorched resin and ozone." | | 4 | "Dozens of overlapping footprints—hobnailed work boots, narrow leather oxfords, heavy lugged soles—formed a dense six-foot lane across the platform." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1259 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Magnetic north lay past" |
| | adverbCount | 3 | | adverbRatio | 0.0023828435266084196 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0007942811755361397 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 16.62 | | std | 11.35 | | cv | 0.683 | | sampleLengths | | 0 | 18 | | 1 | 21 | | 2 | 17 | | 3 | 4 | | 4 | 29 | | 5 | 17 | | 6 | 15 | | 7 | 19 | | 8 | 19 | | 9 | 17 | | 10 | 19 | | 11 | 7 | | 12 | 10 | | 13 | 13 | | 14 | 7 | | 15 | 26 | | 16 | 51 | | 17 | 18 | | 18 | 31 | | 19 | 9 | | 20 | 19 | | 21 | 12 | | 22 | 9 | | 23 | 19 | | 24 | 9 | | 25 | 6 | | 26 | 12 | | 27 | 31 | | 28 | 19 | | 29 | 7 | | 30 | 10 | | 31 | 84 | | 32 | 4 | | 33 | 13 | | 34 | 23 | | 35 | 5 | | 36 | 10 | | 37 | 7 | | 38 | 30 | | 39 | 16 | | 40 | 8 | | 41 | 25 | | 42 | 13 | | 43 | 25 | | 44 | 12 | | 45 | 18 | | 46 | 16 | | 47 | 9 | | 48 | 6 | | 49 | 7 |
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| 99.07% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.616822429906542 | | totalSentences | 107 | | uniqueOpeners | 66 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Flat, solid thuds echoed beneath" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 81 | | matches | | 0 | "She halted beneath the bottom" | | 1 | "She brushed a flake of" | | 2 | "She squatted beside the corpse," | | 3 | "She traced her light along" | | 4 | "She drew steel tweezers from" | | 5 | "She aimed her beam at" | | 6 | "She directed her penlight beneath" | | 7 | "She dropped the bone token" | | 8 | "She crossed eight paces to" | | 9 | "She approached the eastern arch" | | 10 | "He dragged his fingertips across" | | 11 | "She raised her eyes from" |
| | ratio | 0.148 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 81 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "She halted beneath the bottom" | | 2 | "Quinn stood five-foot-nine, spine rigid," | | 3 | "She brushed a flake of" | | 4 | "Detective Sergeant Callum Ross emerged" | | 5 | "Quinn swept her brown eyes" | | 6 | "Cracks spiderwebbed the glazed oxblood" | | 7 | "Ross gestured with his pen" | | 8 | "Quinn crossed the concrete, boots" | | 9 | "She squatted beside the corpse," | | 10 | "The dead man wore a" | | 11 | "Ross flipped his notebook open" | | 12 | "Quinn leaned closer to the" | | 13 | "Ross gestured toward the vaulted" | | 14 | "Quinn tilted her head." | | 15 | "Here, necrotic white lines branched" | | 16 | "Ross lowered his notebook, a" | | 17 | "Quinn unclipped a steel penlight" | | 18 | "She traced her light along" | | 19 | "Ross stepped beside her, squinting" |
| | ratio | 0.568 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 1 | | matches | | 0 | "She raised her eyes from the trampled silt to the unbroken barrier of century-old brick, her jaw squaring against the bitter trace of burnt sage hanging in the …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |