| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1605 | | totalAiIsmAdverbs | 1 | | 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) | |
| 56.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1605 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "unravel" | | 3 | "silence" | | 4 | "pulse" | | 5 | "gloom" | | 6 | "stomach" | | 7 | "could feel" | | 8 | "echo" | | 9 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "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 | 128 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1627 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 1 | | matches | | 0 | "His olive skin glistened with rain and effort, and he was favoring his right side — the knife wound, she guessed, rememb…" |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1626 | | uniqueNames | 15 | | maxNameDensity | 0.62 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tommy" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Tomás | 1 | | Herrera | 5 | | Saint | 1 | | Christopher | 1 | | Tommy | 10 | | Thames | 1 | | London | 1 | | Tube | 2 | | Morris | 2 | | Whitechapel | 1 | | Christmas | 1 | | One | 3 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tommy" | | 7 | "Tube" | | 8 | "Morris" | | 9 | "One" | | 10 | "You" |
| | places | | 0 | "Thames" | | 1 | "London" | | 2 | "Whitechapel" |
| | globalScore | 1 | | windowScore | 1 | |
| 57.41% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a decommissioned electrical s" | | 1 | "looked like maps, but the lines on them s" | | 2 | "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 | 1 | | per1kWords | 0.615 | | wordCount | 1627 | | matches | | 0 | "not the sterile folding tables of a street market but crude wooden structures draped with cloth, candles burning i" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 45.19 | | std | 29.39 | | cv | 0.65 | | sampleLengths | | 0 | 97 | | 1 | 48 | | 2 | 74 | | 3 | 41 | | 4 | 1 | | 5 | 68 | | 6 | 54 | | 7 | 23 | | 8 | 51 | | 9 | 39 | | 10 | 66 | | 11 | 14 | | 12 | 61 | | 13 | 2 | | 14 | 115 | | 15 | 60 | | 16 | 9 | | 17 | 51 | | 18 | 75 | | 19 | 25 | | 20 | 5 | | 21 | 106 | | 22 | 53 | | 23 | 13 | | 24 | 85 | | 25 | 48 | | 26 | 28 | | 27 | 42 | | 28 | 39 | | 29 | 48 | | 30 | 5 | | 31 | 59 | | 32 | 60 | | 33 | 46 | | 34 | 13 | | 35 | 3 |
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| 97.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 128 | | matches | | 0 | "was lit" | | 1 | "been installed" | | 2 | "been transformed" | | 3 | "was gone" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 12 | | totalVerbs | 255 | | matches | | 0 | "was slowing" | | 1 | "was closing" | | 2 | "was favoring" | | 3 | "was running" | | 4 | "was sitting" | | 5 | "was standing" | | 6 | "was still, listening" | | 7 | "was meeting" | | 8 | "was buying" | | 9 | "were walking" | | 10 | "was staring" | | 11 | "were pulling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 22 | | semicolonCount | 0 | | flaggedSentences | 18 | | totalSentences | 129 | | ratio | 0.14 | | matches | | 0 | "The rain came down like a confession — relentless, heavy with the weight of things better left unsaid." | | 1 | "The worn leather watch on her left wrist caught the amber glow of a passing bus as it hissed through a puddle, and she checked the time without thinking — 11:47 PM — then pushed harder off the wet pavement." | | 2 | "His olive skin glistened with rain and effort, and he was favoring his right side — the knife wound, she guessed, remembering the scar that ran along his left forearm." | | 3 | "The air that rose from below was different — cooler, carrying the mineral tang of old underground spaces, the iron-and-dust smell of abandoned infrastructure." | | 4 | "She thumbed a quick text to the desk sergeant — her location, a request for backup, a single word in the notes field: Herrera — then pocketed it and started down." | | 5 | "The sounds of the city above — sirens, engines, the constant wet hum of rain — faded into a muffled hush, replaced by a silence so complete she could hear her own pulse in her ears." | | 6 | "This had been a station once — she could see the curved tile walls, the remnants of a platform edge, the bones of a place that had once teemed with commuters and was now something else entirely." | | 7 | "Stalls lined both sides — not the sterile folding tables of a street market but crude wooden structures draped with cloth, candles burning in glass jars, smoke rising from clay pots that hung on chains." | | 8 | "A woman in a layered shawl examined a row of glass bottles filled with liquids that shifted color as Quinn watched — amber to violet to a green so deep it looked black." | | 9 | "She was still in the shadow of the stairwell, still dressed in her work clothes — dark trousers, white shirt clinging wet to her shoulders, the shoulder holster visible now that her jacket was gone." | | 10 | "The crowd thickened as she moved deeper, and she kept her eyes moving — left, right, ahead, checking the stalls for any sign of Tommy's dark curls, his olive skin, the flash of that medallion." | | 11 | "Tommy's hands moved as he spoke — urgent, pleading." | | 12 | "It would move — she'd heard rumors it relocated on some kind of cycle, every full moon — and she'd lose it." | | 13 | "If she let him go, followed him, learned who he was meeting, what he was buying, where the information flowed — she could crack the whole thing open." | | 14 | "A token, pale and curved — it looked like bone." | | 15 | "She saw it hit him — the recognition, the fear, the calculation." | | 16 | "Eyes followed her — some human, some she wasn't sure about." | | 17 | "The third was straight and level, and from it she could hear the distant echo of footsteps — Tommy's footsteps, she was certain, the uneven rhythm of a man compensating for an old wound." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1614 | | adjectiveStacks | 1 | | stackExamples | | 0 | "left, right, ahead, checking" |
| | adverbCount | 44 | | adverbRatio | 0.027261462205700124 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0030978934324659233 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 12.61 | | std | 10.99 | | cv | 0.871 | | sampleLengths | | 0 | 18 | | 1 | 27 | | 2 | 7 | | 3 | 5 | | 4 | 40 | | 5 | 19 | | 6 | 29 | | 7 | 3 | | 8 | 18 | | 9 | 25 | | 10 | 5 | | 11 | 5 | | 12 | 18 | | 13 | 4 | | 14 | 26 | | 15 | 6 | | 16 | 5 | | 17 | 1 | | 18 | 4 | | 19 | 30 | | 20 | 2 | | 21 | 10 | | 22 | 22 | | 23 | 4 | | 24 | 34 | | 25 | 3 | | 26 | 13 | | 27 | 6 | | 28 | 3 | | 29 | 1 | | 30 | 13 | | 31 | 1 | | 32 | 2 | | 33 | 24 | | 34 | 9 | | 35 | 15 | | 36 | 4 | | 37 | 4 | | 38 | 31 | | 39 | 25 | | 40 | 5 | | 41 | 36 | | 42 | 2 | | 43 | 1 | | 44 | 1 | | 45 | 10 | | 46 | 4 | | 47 | 20 | | 48 | 37 | | 49 | 2 |
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| 46.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.37209302325581395 | | totalSentences | 129 | | uniqueOpeners | 48 | |
| 57.97% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 115 | | matches | | 0 | "Then the stairs opened onto" | | 1 | "Just a man who had" |
| | ratio | 0.017 | |
| 84.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 115 | | matches | | 0 | "She'd abandoned her coat two" | | 1 | "It was slowing her down." | | 2 | "She hit the alley three" | | 3 | "She hurdled the mattress." | | 4 | "Her lungs burned in the" | | 5 | "Her legs were longer than" | | 6 | "She was closing the gap." | | 7 | "He didn't look back." | | 8 | "His olive skin glistened with" | | 9 | "She filed it away the" | | 10 | "He slipped through." | | 11 | "She reached it two seconds" | | 12 | "She pulled it open slowly," | | 13 | "She pulled her phone." | | 14 | "She thumbed a quick text" | | 15 | "She knew London's underground." | | 16 | "She'd studied the Tube maps," | | 17 | "She was still in the" | | 18 | "She didn't belong here." | | 19 | "She knew it the way" |
| | ratio | 0.339 | |
| 64.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 91 | | totalSentences | 115 | | matches | | 0 | "The rain came down like" | | 1 | "Detective Harlow Quinn cut through" | | 2 | "She'd abandoned her coat two" | | 3 | "It was slowing her down." | | 4 | "The worn leather watch on" | | 5 | "The Saint Christopher medallion around" | | 6 | "Quinn didn't slow." | | 7 | "She hit the alley three" | | 8 | "The buildings on either side" | | 9 | "A bin of rotting produce." | | 10 | "A mattress soaked beyond use." | | 11 | "A cat that screamed and" | | 12 | "She hurdled the mattress." | | 13 | "Her lungs burned in the" | | 14 | "Her legs were longer than" | | 15 | "She was closing the gap." | | 16 | "He didn't look back." | | 17 | "His olive skin glistened with" | | 18 | "She filed it away the" | | 19 | "The alley doglegged right." |
| | ratio | 0.791 | |
| 86.96% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 115 | | matches | | 0 | "If she arrested Tommy here," | | 1 | "If she let him go," |
| | ratio | 0.017 | |
| 2.55% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 11 | | matches | | 0 | "She hit the alley three seconds behind him, boots slapping through shallow rivers that ran between the cobblestones." | | 1 | "The buildings on either side pressed close, brick slick and dark, fire escapes climbing like iron veins toward a sky that had forgotten the stars." | | 2 | "His olive skin glistened with rain and effort, and he was favoring his right side — the knife wound, she guessed, remembering the scar that ran along his left f…" | | 3 | "Something that made the fine hairs at the back of her neck stand at attention." | | 4 | "The stairwell was lit by intermittent emergency lights, the kind that had been installed decades ago and never maintained, their plastic covers yellowed and cra…" | | 5 | "She'd studied the Tube maps, the abandoned stations, the sealed tunnels that ran like dead arteries beneath the living city." | | 6 | "A man with burn scars covering both hands haggled over something wrapped in cloth that twitched." | | 7 | "Another stall sold what looked like maps, but the lines on them shifted when she looked away and looked back, rearranging themselves into configurations that ma…" | | 8 | "A vendor held out a small stone that hummed against her palm when she passed too close, and she jerked her hand back and kept walking." | | 9 | "This was a supernatural black market operating out of an abandoned Tube station, selling things that shouldn't exist to people who shouldn't know they existed." | | 10 | "Into a place she didn't understand, among people who trafficked in things that bent the edges of what she'd spent eighteen years believing was real." |
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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 | |