| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.404 | | leniency | 0.809 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1236 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 59.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1236 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "glinting" | | 3 | "intricate" | | 4 | "magnetic" | | 5 | "weight" | | 6 | "perfect" | | 7 | "echoed" | | 8 | "familiar" | | 9 | "shimmered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1229 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 871 | | uniqueNames | 10 | | maxNameDensity | 1.95 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Piccadilly | 1 | | Harlow | 1 | | Quinn | 17 | | Mills | 10 | | Italian | 1 | | Clean | 1 | | Makeshift | 1 | | Empty | 3 | | Morris | 2 | | Chen | 5 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Mills" | | 3 | "Clean" | | 4 | "Makeshift" | | 5 | "Morris" | | 6 | "Chen" |
| | places | (empty) | | globalScore | 0.524 | | windowScore | 0.667 | |
| 5.07% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like vendor stalls hastily abandon" | | 1 | "as if grasping at something invisible" | | 2 | "seemed eager to pack up and leave" | | 3 | "looked like bone" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1229 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 23.63 | | std | 15.36 | | cv | 0.65 | | sampleLengths | | 0 | 53 | | 1 | 16 | | 2 | 45 | | 3 | 14 | | 4 | 39 | | 5 | 33 | | 6 | 8 | | 7 | 40 | | 8 | 10 | | 9 | 39 | | 10 | 41 | | 11 | 6 | | 12 | 4 | | 13 | 30 | | 14 | 36 | | 15 | 11 | | 16 | 27 | | 17 | 45 | | 18 | 19 | | 19 | 18 | | 20 | 6 | | 21 | 37 | | 22 | 7 | | 23 | 24 | | 24 | 48 | | 25 | 9 | | 26 | 48 | | 27 | 33 | | 28 | 16 | | 29 | 39 | | 30 | 21 | | 31 | 49 | | 32 | 15 | | 33 | 1 | | 34 | 6 | | 35 | 18 | | 36 | 5 | | 37 | 3 | | 38 | 36 | | 39 | 8 | | 40 | 30 | | 41 | 38 | | 42 | 31 | | 43 | 26 | | 44 | 5 | | 45 | 3 | | 46 | 48 | | 47 | 32 | | 48 | 20 | | 49 | 17 |
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| 98.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 111 | | matches | | 0 | "were carved" | | 1 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 163 | | matches | (empty) | |
| 81.19% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 139 | | ratio | 0.022 | | matches | | 0 | "The victim wore expensive clothes—tailored wool coat, Italian leather shoes." | | 1 | "Makeshift stalls lined the walls—tables fashioned from planks and crates, fabric partitions creating narrow corridors." | | 2 | "Leather pouches that smelled of herbs and something else—sulphur?" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 874 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.034324942791762014 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.018306636155606407 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 8.84 | | std | 5.47 | | cv | 0.619 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 21 | | 3 | 12 | | 4 | 4 | | 5 | 4 | | 6 | 17 | | 7 | 9 | | 8 | 5 | | 9 | 10 | | 10 | 4 | | 11 | 10 | | 12 | 9 | | 13 | 10 | | 14 | 8 | | 15 | 4 | | 16 | 1 | | 17 | 2 | | 18 | 4 | | 19 | 1 | | 20 | 15 | | 21 | 3 | | 22 | 3 | | 23 | 12 | | 24 | 3 | | 25 | 5 | | 26 | 10 | | 27 | 15 | | 28 | 5 | | 29 | 6 | | 30 | 4 | | 31 | 10 | | 32 | 5 | | 33 | 5 | | 34 | 7 | | 35 | 2 | | 36 | 9 | | 37 | 11 | | 38 | 9 | | 39 | 11 | | 40 | 9 | | 41 | 12 | | 42 | 6 | | 43 | 4 | | 44 | 14 | | 45 | 10 | | 46 | 6 | | 47 | 7 | | 48 | 9 | | 49 | 9 |
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| 89.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5539568345323741 | | totalSentences | 139 | | uniqueOpeners | 77 | |
| 66.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 101 | | matches | | 0 | "Just that expression of absolute" | | 1 | "Too organised for a squat." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 101 | | matches | | 0 | "His eyes remained open, pupils" | | 1 | "She checked his pockets." | | 2 | "She examined the man's hands" | | 3 | "She noticed burn marks on" | | 4 | "Her torch beam caught something" | | 5 | "she called to Mills" | | 6 | "She pocketed the compass instead," | | 7 | "she asked the coroner's assistant" | | 8 | "She ignored the question, focused" | | 9 | "She squeezed through the gap," | | 10 | "Her torch illuminated more symbols" | | 11 | "She chose left on instinct," | | 12 | "She emerged behind a pub," | | 13 | "She lifted the victim's eyelid" | | 14 | "She pulled it out, watching" | | 15 | "She lowered her voice." | | 16 | "She plucked it free, holding" | | 17 | "It shimmered with an oily" | | 18 | "She bagged it separately, then" | | 19 | "She headed for the exit" |
| | ratio | 0.198 | |
| 78.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 101 | | matches | | 0 | "The abandoned Piccadilly line platform" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "The beam from her torch" | | 3 | "Sergeant Mills gestured toward a" | | 4 | "Quinn approached the corpse." | | 5 | "The man lay sprawled on" | | 6 | "His eyes remained open, pupils" | | 7 | "Quinn crouched beside the body," | | 8 | "The victim wore expensive clothes—tailored" | | 9 | "She checked his pockets." | | 10 | "She examined the man's hands" | | 11 | "Mills shifted uncomfortably." | | 12 | "Quinn swept her torch across" | | 13 | "Symbols chalked on the tiles." | | 14 | "Candle wax pooled on the" | | 15 | "Quinn stood, studying the layout." | | 16 | "The stalls formed deliberate pathways," | | 17 | "She noticed burn marks on" | | 18 | "Her torch beam caught something" | | 19 | "A small brass compass lay" |
| | ratio | 0.762 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 101 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "The man lay sprawled on his back, arms outstretched, fingers curled as if grasping at something invisible." | | 1 | "Leather pouches that smelled of herbs and something else—sulphur?" | | 2 | "Chen glanced at Mills, who was busy directing the forensics team." | | 3 | "It shimmered with an oily iridescence, colors that shouldn't exist on any bird she knew." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 47 | | tagDensity | 0.106 | | leniency | 0.213 | | rawRatio | 0.2 | | effectiveRatio | 0.043 | |