| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1412 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "perfectly" | | 1 | "deliberately" |
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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) | |
| 92.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1412 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 67 | | ratio | 0 | | matches | (empty) | |
| 32.75% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 12 | | markdownWords | 119 | | totalWords | 1423 | | ratio | 0.084 | | matches | | 0 | "administration of non-formulary substances" | | 1 | "Christ." | | 2 | "Detective Quinn, in your own words, what happened to DS Morris?" | | 3 | "to" | | 4 | "down" | | 5 | "Camden. Abandoned station. Full moon was — Thursday? Friday?" | | 6 | "He was there, and then he was there but not there, and then he was nothing." | | 7 | "Support: none. Comms: no signal down there, guaranteed, and even if there was, what do I say — send units to the disused platform at the bottom of a hole that isn't on the map? Ground: unknown. Exits: unknown. Numbers: unknown, plural, hostile-probable. Threat: unquantifiable." | | 8 | "withdraw, contain, request specialist support" | | 9 | "the last time you did it properly, you did it properly, and Morris still didn't come home." | | 10 | "Quinn, from control — say again?" | | 11 | "Received. Twenty-three fifty-one." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1364 | | uniqueNames | 25 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Tuesday | 1 | | March | 1 | | Whitcombe | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 6 | | Seville-born | 1 | | Berwick | 1 | | Broadwick | 1 | | Street | 1 | | Post | 1 | | Office | 1 | | Tower | 1 | | Camden | 2 | | Town | 1 | | Road | 1 | | Section | 2 | | Underground | 1 | | Thursday | 1 | | Met | 1 | | Morris | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Morris" |
| | places | | 0 | "Raven" | | 1 | "March" | | 2 | "Seville-born" | | 3 | "Berwick" | | 4 | "Street" | | 5 | "Camden" | | 6 | "Town" | | 7 | "Road" | | 8 | "Met" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like fried onions and drains" |
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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 | 1423 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 27.9 | | std | 22.8 | | cv | 0.817 | | sampleLengths | | 0 | 11 | | 1 | 65 | | 2 | 60 | | 3 | 5 | | 4 | 50 | | 5 | 22 | | 6 | 25 | | 7 | 2 | | 8 | 40 | | 9 | 4 | | 10 | 5 | | 11 | 2 | | 12 | 52 | | 13 | 65 | | 14 | 21 | | 15 | 13 | | 16 | 63 | | 17 | 24 | | 18 | 2 | | 19 | 80 | | 20 | 43 | | 21 | 3 | | 22 | 44 | | 23 | 40 | | 24 | 3 | | 25 | 20 | | 26 | 19 | | 27 | 46 | | 28 | 53 | | 29 | 13 | | 30 | 9 | | 31 | 62 | | 32 | 2 | | 33 | 70 | | 34 | 5 | | 35 | 14 | | 36 | 45 | | 37 | 14 | | 38 | 35 | | 39 | 50 | | 40 | 5 | | 41 | 67 | | 42 | 12 | | 43 | 34 | | 44 | 14 | | 45 | 6 | | 46 | 9 | | 47 | 8 | | 48 | 3 | | 49 | 32 |
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| 89.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 114 | | matches | | 0 | "been told" | | 1 | "were supposed" | | 2 | "been asked" | | 3 | "was gone" | | 4 | "been given" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 222 | | matches | | 0 | "was following" | | 1 | "was watching" | | 2 | "was carrying" | | 3 | "was taking" | | 4 | "wasn't running" | | 5 | "was running" | | 6 | "wasn't officially working" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 120 | | ratio | 0.067 | | matches | | 0 | "Detective Harlow Quinn had been standing across from The Raven's Nest for two hours and eleven minutes, and the green neon of its sign had bled into every surface — the puddles, the wet brick, the inside of her own eyelids when she blinked." | | 1 | "Under the streetlight his olive skin looked grey, and she saw him do the arithmetic — the coat, the shoes, the way she stood — and get the answer in less than a second." | | 2 | "He led her north, and the streets emptied as they went, and the rain got worse — proper rain now, the kind that came down in sheets and turned the world into a smear of orange light." | | 3 | "She could hear him — the ring of his footsteps changing, going hollow, going *down*." | | 4 | "And light — but wrong light, a low flickering amber that didn't come from any bulb, and something else layered under it, greenish, like the neon outside Silas' bar." | | 5 | "Full moon was — Thursday?" | | 6 | "Comms: no signal down there, guaranteed, and even if there was, what do I say — send units to the disused platform at the bottom of a hole that isn't on the map?" | | 7 | "Size nine, tread half-worn on the outside edge — a man who rolled his ankles when he ran." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 373 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.024128686327077747 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.005361930294906166 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 11.86 | | std | 12.12 | | cv | 1.022 | | sampleLengths | | 0 | 11 | | 1 | 44 | | 2 | 4 | | 3 | 17 | | 4 | 13 | | 5 | 13 | | 6 | 34 | | 7 | 5 | | 8 | 6 | | 9 | 21 | | 10 | 7 | | 11 | 16 | | 12 | 22 | | 13 | 6 | | 14 | 3 | | 15 | 16 | | 16 | 2 | | 17 | 2 | | 18 | 4 | | 19 | 34 | | 20 | 4 | | 21 | 5 | | 22 | 2 | | 23 | 44 | | 24 | 3 | | 25 | 2 | | 26 | 3 | | 27 | 23 | | 28 | 5 | | 29 | 3 | | 30 | 2 | | 31 | 32 | | 32 | 2 | | 33 | 3 | | 34 | 16 | | 35 | 13 | | 36 | 9 | | 37 | 4 | | 38 | 50 | | 39 | 5 | | 40 | 3 | | 41 | 16 | | 42 | 2 | | 43 | 37 | | 44 | 17 | | 45 | 10 | | 46 | 16 | | 47 | 2 | | 48 | 18 | | 49 | 19 |
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| 78.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5083333333333333 | | totalSentences | 120 | | uniqueOpeners | 61 | |
| 71.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 93 | | matches | | 0 | "Then a long, lung-burning haul" | | 1 | "Then she went down the" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 93 | | matches | | 0 | "She'd told the DCI she" | | 1 | "He came out at 11:47." | | 2 | "She'd read the disciplinary transcript" | | 3 | "It read like someone had" | | 4 | "He was carrying a canvas" | | 5 | "She didn't hurry." | | 6 | "He went left onto Berwick," | | 7 | "She keyed her radio with" | | 8 | "She'd had no words then." | | 9 | "She still didn't." | | 10 | "He led her north, and" | | 11 | "She checked the worn leather" | | 12 | "He was running *to*." | | 13 | "She could hear him —" | | 14 | "It smelled of woodsmoke and" | | 15 | "She'd heard the words." | | 16 | "You didn't do eighteen years" | | 17 | "She'd written it off as" | | 18 | "*He was there, and then" | | 19 | "She'd been given a commendation" |
| | ratio | 0.269 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 93 | | matches | | 0 | "The rain came down like" | | 1 | "Detective Harlow Quinn had been" | | 2 | "Soho on a Tuesday night" | | 3 | "She'd told the DCI she" | | 4 | "That was true in the" | | 5 | "He came out at 11:47." | | 6 | "Quinn knew him from the" | | 7 | "Tomás Herrera, twenty-nine, Seville-born, struck" | | 8 | "She'd read the disciplinary transcript" | | 9 | "It read like someone had" | | 10 | "He was carrying a canvas" | | 11 | "Quinn stepped out from the" | | 12 | "She didn't hurry." | | 13 | "*Christ.* Quinn went after him," | | 14 | "He went left onto Berwick," | | 15 | "The holdall swung and clanked." | | 16 | "Glass in there." | | 17 | "Quinn's shoes slipped on the" | | 18 | "Herrera vaulted a bollard without" | | 19 | "She keyed her radio with" |
| | ratio | 0.645 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 93 | | matches | | 0 | "What she was actually doing" | | 1 | "Because calling it in meant" | | 2 | "If she went back up" | | 3 | "If she went down, nobody" |
| | ratio | 0.043 | |
| 49.69% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 6 | | matches | | 0 | "*Christ.* Quinn went after him, and her body did what it always did, dropping into the old rhythm, elbows in, chin down, and some detached part of her brain fil…" | | 1 | "He led her north, and the streets emptied as they went, and the rain got worse — proper rain now, the kind that came down in sheets and turned the world into a …" | | 2 | "A faded notice about a Section 106 agreement, a planning reference, a phone number with a dialling code that hadn't existed since 2000." | | 3 | "She could hear him — the ring of his footsteps changing, going hollow, going *down*." | | 4 | "And below, a long way below, something that was not the sound of an empty station." | | 5 | "She looked at it, this small black thing that connected her to nine thousand colleagues and a control room and the whole vast weight of the law." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn's shoes slipped on (slip on)" |
| | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 1 | | effectiveRatio | 0.222 | |