| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1770 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "quickly" | | 1 | "completely" | | 2 | "gently" |
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| 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) | |
| 66.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1770 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "navigated" | | 1 | "quickened" | | 2 | "eyebrow" | | 3 | "electric" | | 4 | "pulsed" | | 5 | "weight" | | 6 | "pulse" | | 7 | "navigating" | | 8 | "could feel" | | 9 | "mechanical" |
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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 | 130 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 130 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1795 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1795 | | uniqueNames | 19 | | maxNameDensity | 0.72 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 13 | | Soho | 1 | | Morris | 6 | | Old | 1 | | Compton | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Quinn | 8 | | Tube | 5 | | Saint | 1 | | Christopher | 1 | | London | 1 | | Whitechapel | 1 | | Veil | 2 | | Market | 2 | | Lewisham | 1 | | Camden | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Quinn" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "London" | | 5 | "Whitechapel" | | 6 | "Veil" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 1 | | matches | | 0 | "tasted like the city — exhaust and metal" |
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| 88.58% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.114 | | wordCount | 1795 | | matches | | 0 | "Not military, not police, but there was a sureness to the way he navigated the crowded pav" | | 1 | "not police, but there was a sureness to the way he navigated the crowded pav" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 130 | | matches | | 0 | "shut that thought" | | 1 | "was that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 56.09 | | std | 33.14 | | cv | 0.591 | | sampleLengths | | 0 | 7 | | 1 | 79 | | 2 | 96 | | 3 | 11 | | 4 | 23 | | 5 | 87 | | 6 | 85 | | 7 | 54 | | 8 | 65 | | 9 | 94 | | 10 | 16 | | 11 | 90 | | 12 | 4 | | 13 | 75 | | 14 | 31 | | 15 | 66 | | 16 | 3 | | 17 | 80 | | 18 | 4 | | 19 | 42 | | 20 | 86 | | 21 | 103 | | 22 | 47 | | 23 | 98 | | 24 | 104 | | 25 | 72 | | 26 | 70 | | 27 | 73 | | 28 | 31 | | 29 | 55 | | 30 | 36 | | 31 | 8 |
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| 80.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 130 | | matches | | 0 | "were ruined" | | 1 | "been buried" | | 2 | "was plastered" | | 3 | "being swallowed" | | 4 | "were curved" | | 5 | "been painted " | | 6 | "been fired" | | 7 | "were arranged" | | 8 | "were carved" | | 9 | "been trained" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 18 | | totalVerbs | 282 | | matches | | 0 | "was already burning" | | 1 | "wasn't meeting" | | 2 | "wasn't heading" | | 3 | "was heading" | | 4 | "were checking" | | 5 | "were checking" | | 6 | "was protesting " | | 7 | "wasn't negotiating" | | 8 | "was standing" | | 9 | "was looking" | | 10 | "was still hammering" | | 11 | "was talking" | | 12 | "was choosing" | | 13 | "was standing" | | 14 | "was standing" | | 15 | "wasn't navigating" | | 16 | "was still ticking " | | 17 | "was buying" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 25 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 130 | | ratio | 0.123 | | matches | | 0 | "Her blazer clung to her shoulders like a second skin, cold and heavy, and the water running down her face tasted like the city — exhaust and metal and something underneath both that she could never quite name." | | 1 | "The complaint had been buried fast — too fast — and Herrera had dropped off the registry and, presumably, into the kind of work that didn't require paperwork." | | 2 | "The bar was a known hub — she'd been building a case against it for months — but Herrera wasn't heading for the front door." | | 3 | "Her knee was protesting — old injury, six years back — but pain was a negotiation, and she wasn't negotiating tonight." | | 4 | "He was standing in front of a set of iron stairs that descended into the ground — an old Tube access point, the kind that had been officially sealed decades ago." | | 5 | "He was looking down into the dark, and his hand went to his chest — to the Saint Christopher medallion she'd seen in his surveillance photos." | | 6 | "The stairs were steep, the iron slick with rain and rust, and at the bottom she could see the faintest suggestion of light — not electric, not the harsh white of Tube station fluorescents." | | 7 | "Her heart rate hadn't dropped from the chase — it was still hammering against her ribs — but it wasn't the exertion anymore." | | 8 | "The walls were curved, original Tube brickwork, but they'd been painted — no, not painted." | | 9 | "A black market — the file her informant had given her called it the Veil Market — should have been cramped and ugly, a place of tarpaulin stalls and desperation." | | 10 | "The abandoned Tube platform stretched out in front of her, its arched ceiling rising forty feet overhead, and from that ceiling hung hundreds — maybe thousands — of small glass vessels, each one lit from within by a flame of a different color." | | 11 | "She saw a child — or something shaped like a child — sitting cross-legged on a rug, arranging small bones in patterns that made her eyes water when she tried to follow them." | | 12 | "It caught the light for a fraction of a second — bone-white, shaped like a coin — and the figure pocketed it and gestured toward one of the stalls." | | 13 | "The worn leather watch on her wrist was still ticking — she could feel it against her pulse, her own heartbeat and the steady mechanical beat of the seconds overlapping, out of sync." | | 14 | "File what she'd seen as a probable supernatural venue and hand it off to whatever shadow unit handled these things — if such a unit even existed, if it wasn't just another bureaucratic fiction designed to make the unmanageable disappear into committees." | | 15 | "She could follow the thread that started with a dead partner and an empty coat and ended — if it ended at all — somewhere in the dark at the far end of an abandoned Tube station beneath Camden." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1775 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 52 | | adverbRatio | 0.029295774647887324 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.0067605633802816905 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 13.81 | | std | 12.02 | | cv | 0.87 | | sampleLengths | | 0 | 7 | | 1 | 37 | | 2 | 4 | | 3 | 38 | | 4 | 8 | | 5 | 32 | | 6 | 4 | | 7 | 2 | | 8 | 22 | | 9 | 28 | | 10 | 11 | | 11 | 10 | | 12 | 1 | | 13 | 1 | | 14 | 11 | | 15 | 13 | | 16 | 33 | | 17 | 5 | | 18 | 5 | | 19 | 25 | | 20 | 5 | | 21 | 1 | | 22 | 4 | | 23 | 42 | | 24 | 2 | | 25 | 16 | | 26 | 10 | | 27 | 11 | | 28 | 8 | | 29 | 25 | | 30 | 21 | | 31 | 11 | | 32 | 18 | | 33 | 2 | | 34 | 25 | | 35 | 9 | | 36 | 9 | | 37 | 31 | | 38 | 4 | | 39 | 26 | | 40 | 24 | | 41 | 4 | | 42 | 12 | | 43 | 34 | | 44 | 2 | | 45 | 3 | | 46 | 13 | | 47 | 16 | | 48 | 22 | | 49 | 4 |
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| 37.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 23 | | diversityRatio | 0.33076923076923076 | | totalSentences | 130 | | uniqueOpeners | 43 | |
| 30.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 111 | | matches | | | ratio | 0.009 | |
| 72.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 111 | | matches | | 0 | "Her shoes were ruined." | | 1 | "Her blazer clung to her" | | 2 | "She'd read his file." | | 3 | "He wasn't meeting Silas tonight." | | 4 | "She'd already ruled that out." | | 5 | "He was heading past it." | | 6 | "She quickened her stride." | | 7 | "He kept glancing behind him," | | 8 | "She wiped her eyes with" | | 9 | "Her knee was protesting —" | | 10 | "She pressed herself against the" | | 11 | "He was standing in front" | | 12 | "He was looking down into" | | 13 | "He gripped it, held it" | | 14 | "It smelled of copper and" | | 15 | "She thought of Morris." | | 16 | "She always thought of Morris" | | 17 | "His family had buried an" | | 18 | "Her heart rate hadn't dropped" | | 19 | "It was something older." |
| | ratio | 0.369 | |
| 36.58% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 94 | | totalSentences | 111 | | matches | | 0 | "The rain came down like" | | 1 | "Quinn had been following Herrera" | | 2 | "Her shoes were ruined." | | 3 | "Her blazer clung to her" | | 4 | "Herrera moved well for someone" | | 5 | "She'd read his file." | | 6 | "The complaint had been buried" | | 7 | "The kind of work that" | | 8 | "Quinn shut that thought down" | | 9 | "Herrera turned left on Old" | | 10 | "The green glow of the" | | 11 | "He wasn't meeting Silas tonight." | | 12 | "She'd already ruled that out." | | 13 | "The bar was a known" | | 14 | "He was heading past it." | | 15 | "She quickened her stride." | | 16 | "A group of teenagers spilled" | | 17 | "He kept glancing behind him," | | 18 | "People who were checking for" | | 19 | "People who looked at rooftops" |
| | ratio | 0.847 | |
| 45.05% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 111 | | matches | | 0 | "Now that she could see" |
| | ratio | 0.009 | |
| 21.98% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 11 | | matches | | 0 | "Not military, not police, but there was a sureness to the way he navigated the crowded pavement, a fluid economy that came from years of running toward things o…" | | 1 | "The complaint had been buried fast — too fast — and Herrera had dropped off the registry and, presumably, into the kind of work that didn't require paperwork." | | 2 | "The kind of work that got people like DS Morris killed." | | 3 | "He was standing in front of a set of iron stairs that descended into the ground — an old Tube access point, the kind that had been officially sealed decades ago…" | | 4 | "And underneath that, she caught a thread of something that made the hair on her arms stand up beneath her wet sleeves." | | 5 | "Something she'd been carrying for three years that had a shape and a weight and was pressing against the inside of her chest like it wanted out." | | 6 | "The color on it, a deep burgundy, was inside the brick itself, as though the clay had been fired with something that was only now, in the presence of air, begin…" | | 7 | "They swayed gently, though there was no wind, and the light they cast moved across the platform in slow, liquid patterns, making shadows that didn't match the s…" | | 8 | "She saw a child — or something shaped like a child — sitting cross-legged on a rug, arranging small bones in patterns that made her eyes water when she tried to…" | | 9 | "This was the place that had swallowed Morris and everything she'd understood about the rules that governed reality, and she was standing at its threshold with w…" | | 10 | "She could follow the thread that started with a dead partner and an empty coat and ended — if it ended at all — somewhere in the dark at the far end of an aband…" |
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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 | |