| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1447 | | 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) | |
| 55.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1447 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "glint" | | 1 | "glinting" | | 2 | "velvet" | | 3 | "echoes" | | 4 | "wavering" | | 5 | "crystal" | | 6 | "etched" | | 7 | "echoing" |
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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 | 64 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 64 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1440 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1259 | | uniqueNames | 19 | | maxNameDensity | 1.35 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Harlow | 17 | | Quinn | 1 | | Javi | 7 | | Ruiz | 2 | | Covent | 1 | | Garden | 1 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | Tube | 2 | | Camdetown | 1 | | Met | 1 | | Tomás | 7 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Javi" | | 3 | "Ruiz" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Tomás" | | 7 | "Herrera" | | 8 | "Saint" | | 9 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Covent" | | 2 | "Garden" | | 3 | "Seville" |
| | globalScore | 0.825 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1440 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 72 | | std | 39.8 | | cv | 0.553 | | sampleLengths | | 0 | 99 | | 1 | 120 | | 2 | 90 | | 3 | 95 | | 4 | 119 | | 5 | 99 | | 6 | 186 | | 7 | 59 | | 8 | 62 | | 9 | 41 | | 10 | 55 | | 11 | 56 | | 12 | 35 | | 13 | 48 | | 14 | 87 | | 15 | 47 | | 16 | 23 | | 17 | 51 | | 18 | 62 | | 19 | 6 |
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| 88.82% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 64 | | matches | | 0 | "been redacted" | | 1 | "are arranged" | | 2 | "is etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 206 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 71 | | ratio | 0.07 | | matches | | 0 | "She’d been tailing 17-year-old Javi Ruiz—part of the clique she’d been investigating for six weeks—for three blocks." | | 1 | "Three years ago, her partner DS Morris had died on a case just like this—chasing a lead on a supernatural smuggling ring, diving into a hidden basement that no one had bothered to search until it was too late." | | 2 | "She hears a faint clatter from the stairwell—Javi dropping the velvet bag, his boots scrabbling on the stone steps." | | 3 | "Every full moon, the market shifts locations, according to the rumors—she’d laughed then, but now the chaos of the stalls makes sense." | | 4 | "His Saint Christopher medallion hangs around his neck, catching the light when he shifts to block the path to a huddled figure behind him—Javi, clutching the same velvet bag from the alley." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1277 | | adjectiveStacks | 2 | | stackExamples | | 0 | "enchanted rune-carved knives" | | 1 | "small, weathered animal bone" |
| | adverbCount | 28 | | adverbRatio | 0.02192638997650744 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004698512137823023 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 20.28 | | std | 9.83 | | cv | 0.485 | | sampleLengths | | 0 | 27 | | 1 | 33 | | 2 | 39 | | 3 | 17 | | 4 | 26 | | 5 | 21 | | 6 | 56 | | 7 | 8 | | 8 | 25 | | 9 | 21 | | 10 | 24 | | 11 | 12 | | 12 | 12 | | 13 | 21 | | 14 | 27 | | 15 | 21 | | 16 | 14 | | 17 | 12 | | 18 | 11 | | 19 | 39 | | 20 | 34 | | 21 | 23 | | 22 | 19 | | 23 | 6 | | 24 | 15 | | 25 | 17 | | 26 | 24 | | 27 | 18 | | 28 | 29 | | 29 | 25 | | 30 | 34 | | 31 | 22 | | 32 | 24 | | 33 | 25 | | 34 | 27 | | 35 | 20 | | 36 | 23 | | 37 | 16 | | 38 | 30 | | 39 | 32 | | 40 | 19 | | 41 | 22 | | 42 | 27 | | 43 | 20 | | 44 | 8 | | 45 | 14 | | 46 | 6 | | 47 | 17 | | 48 | 19 | | 49 | 18 |
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| 61.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4084507042253521 | | totalSentences | 71 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 64 | | matches | | 0 | "Further down the platform, a" | | 1 | "Then she spots him: Tomás" |
| | ratio | 0.031 | |
| 63.75% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 64 | | matches | | 0 | "Her salt-and-pepper cropped hair mats" | | 1 | "She’d been tailing 17-year-old Javi" | | 2 | "She’s gaining on him, maybe" | | 3 | "He dives through the gap," | | 4 | "She skids to a stop" | | 5 | "His thumb brushes a small," | | 6 | "She’d spent the last three" | | 7 | "She hears a faint clatter" | | 8 | "She tugs her trench coat" | | 9 | "She grabs the atlas, yanking" | | 10 | "She climbs down one step" | | 11 | "She scans the platform, noting" | | 12 | "His Saint Christopher medallion hangs" | | 13 | "It shatters on the tile," | | 14 | "He steps forward, his hands" | | 15 | "She’s not backing down, not" | | 16 | "she says, her voice sharp" | | 17 | "he says, his voice shaking" | | 18 | "Her gaze flickers to the" | | 19 | "he says, his voice quieter" |
| | ratio | 0.391 | |
| 30.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 64 | | matches | | 0 | "Detective Harlow Quinn’s trench coat" | | 1 | "Her salt-and-pepper cropped hair mats" | | 2 | "She’d been tailing 17-year-old Javi" | | 3 | "The kid had left a" | | 4 | "Tail cam footage from the" | | 5 | "Harlow had written the case" | | 6 | "Javi swerves left, darting past" | | 7 | "The bar’s distinctive green neon" | | 8 | "Harlow’s boots slip once on" | | 9 | "She’s gaining on him, maybe" | | 10 | "He dives through the gap," | | 11 | "Harlow follows, her baton unclipped" | | 12 | "The alley reeks of discarded" | | 13 | "She skids to a stop" | | 14 | "His thumb brushes a small," | | 15 | "The whole unit swings open," | | 16 | "Harlow pauses, her hand hovering" | | 17 | "The coroner’s report had been" | | 18 | "She’d spent the last three" | | 19 | "She hears a faint clatter" |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 19.23% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 9 | | matches | | 0 | "Harlow had written the case off as a teen crew fencing stolen designer goods until last week, when a local antique shop owner had reported a stack of enchanted …" | | 1 | "Harlow’s boots slip once on a sheet of standing water, her balance snapping back instantly, the trained muscle memory kicking in." | | 2 | "The coroner’s report had been redacted, but Harlow still remembered the photos: puncture marks on Morris’s throat that didn’t match any earthly weapon, a faint …" | | 3 | "She’d spent the last three years chasing every lead that touched the supernatural, every smuggling ring that could tie back to that night." | | 4 | "She hears a faint clatter from the stairwell—Javi dropping the velvet bag, his boots scrabbling on the stone steps." | | 5 | "The air coming up from below smells like damp earth and burned sage, with a faint undercurrent of sulfur that makes her nose wrinkle." | | 6 | "She scans the platform, noting the stalls are arranged in a jumbled, fluid layout that doesn’t match the faded black-and-white photos of Camdetown Tube station …" | | 7 | "His Saint Christopher medallion hangs around his neck, catching the light when he shifts to block the path to a huddled figure behind him—Javi, clutching the sa…" | | 8 | "It shatters on the tile, the blue liquid fizzling and smoking, sending up a cloud of acrid smoke that stings Harlow’s eyes." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 4 | | matches | | 0 | "she says, her voice sharp over the quiet of the market" | | 1 | "he says, his voice shaking" | | 2 | "he says, his voice quieter now" | | 3 | "she says, her voice tight" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0 | | effectiveRatio | 0 | |