| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1407 | | 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) | |
| 71.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1407 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "scanning" | | 1 | "scanned" | | 2 | "warmth" | | 3 | "footsteps" | | 4 | "crystal" | | 5 | "weight" | | 6 | "silence" | | 7 | "could feel" |
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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 | 85 | | matches | (empty) | |
| 58.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1426 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1416 | | uniqueNames | 14 | | maxNameDensity | 0.42 | | worstName | "Quinn" | | maxWindowNameDensity | 1 | | worstWindowName | "Quinn" | | discoveredNames | | Euston | 1 | | Road | 1 | | Quinn | 6 | | Nest | 3 | | Silas | 1 | | Harlow | 1 | | Raven | 1 | | Morris | 4 | | Camden | 3 | | High | 1 | | Street | 1 | | Tube | 1 | | Victorian | 1 | | People | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Silas" | | 2 | "Harlow" | | 3 | "Raven" | | 4 | "Morris" | | 5 | "People" |
| | places | | 0 | "Euston" | | 1 | "Road" | | 2 | "Nest" | | 3 | "Camden" | | 4 | "High" | | 5 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 38.06% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 3 | | matches | | 0 | "quite traffic" | | 1 | "egendary; this apparently was one of them" | | 2 | "looked like a reclaimed door laid across" |
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| 59.75% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.403 | | wordCount | 1426 | | matches | | 0 | "not calmly, not helpfully, but in a shock of elbows" | | 1 | "not helpfully, but in a shock of elbows" |
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| 90.04% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 87 | | matches | | 0 | "watched that bar" | | 1 | "feel that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 47.53 | | std | 30.23 | | cv | 0.636 | | sampleLengths | | 0 | 127 | | 1 | 66 | | 2 | 33 | | 3 | 80 | | 4 | 27 | | 5 | 76 | | 6 | 3 | | 7 | 71 | | 8 | 21 | | 9 | 99 | | 10 | 20 | | 11 | 7 | | 12 | 79 | | 13 | 68 | | 14 | 10 | | 15 | 16 | | 16 | 85 | | 17 | 89 | | 18 | 63 | | 19 | 47 | | 20 | 29 | | 21 | 42 | | 22 | 48 | | 23 | 30 | | 24 | 42 | | 25 | 41 | | 26 | 31 | | 27 | 37 | | 28 | 13 | | 29 | 26 |
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| 84.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 85 | | matches | | 0 | "been dragged" | | 1 | "been colonised" | | 2 | "been improvised" | | 3 | "been told" | | 4 | "been reclassified" | | 5 | "was gone " |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 247 | | matches | | 0 | "was measuring" | | 1 | "was selling" | | 2 | "was still swinging" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 19 | | semicolonCount | 3 | | flaggedSentences | 16 | | totalSentences | 87 | | ratio | 0.184 | | matches | | 0 | "Ahead, the suspect — lean, hooded, moving with the easy economy of someone who'd done this before — cut left between a laundrette and a shuttered off-licence, his trainers slapping through standing water." | | 1 | "Her leather watch — the one Morris had given her the week before he died, the strap soft as old skin — caught a gleam from a security light as she raised her torch." | | 2 | "\"Federali!\" she shouted, because it was the first loud thing that came to her and because it worked — he stumbled, half-turning, and she gained four feet." | | 3 | "Camden was a river of light and bodies even at this hour — the market crowd bleeding out into the pubs, a bus kneeling at the stop, a food van throwing steam into the drizzle." | | 4 | "People scattered the way people always scatter — not calmly, not helpfully, but in a shock of elbows and swearing." | | 5 | "The buildings crowded in — brickwork black with rain, fire escapes, a wall of peeling posters advertising nights that had happened years ago — and at the end of it, set into the ground like a wound, a stairwell descending below street level." | | 6 | "Beyond the barrier, darkness with a faint, impossible warmth to it — amber light, far below, and a sound that wasn't quite traffic." | | 7 | "She reached for her shoulder mic and her fingers found only wet nylon — the rain had soaked through, and when she thumbed the button, the speaker coughed static and nothing else." | | 8 | "Camden's dead spots were legendary; this apparently was one of them." | | 9 | "The stairs were slick with seepage and took her into air that was warm and wrong — too warm for a night like this, and thick with the smell of old iron and something herbal, something sweet and resinous she couldn't name." | | 10 | "Glass jars lined a table, and inside them things moved — slow, pale, unhurried." | | 11 | "That was the detail that raised the hair on her forearms — not that they ignored a police detective standing at the top of a stairwell in full uniform, but that ignoring her was clearly a rule rather than a choice." | | 12 | "If she let him reach it, she lost him — she could feel that the way she'd always felt a trail going cold, a knot tightening behind her sternum." | | 13 | "Three options, and her training laid them out with the flat clarity of a briefing: call it in — she couldn't; wait for backup — she'd never get it down here; follow him alone into an unauthorised location full of people who had arranged not to see her." | | 14 | "He slowed, glancing back at her — that same flat, wet fear, but with something else underneath it now, something almost like a warning." | | 15 | "He passed under the arch and was gone — not around a corner, gone, the air folding shut behind him like it had never been open." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1404 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.03205128205128205 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.007122507122507123 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 16.39 | | std | 11.75 | | cv | 0.717 | | sampleLengths | | 0 | 19 | | 1 | 33 | | 2 | 11 | | 3 | 36 | | 4 | 4 | | 5 | 4 | | 6 | 20 | | 7 | 17 | | 8 | 15 | | 9 | 34 | | 10 | 27 | | 11 | 6 | | 12 | 3 | | 13 | 3 | | 14 | 23 | | 15 | 13 | | 16 | 6 | | 17 | 32 | | 18 | 3 | | 19 | 7 | | 20 | 17 | | 21 | 35 | | 22 | 13 | | 23 | 28 | | 24 | 3 | | 25 | 20 | | 26 | 15 | | 27 | 9 | | 28 | 1 | | 29 | 2 | | 30 | 24 | | 31 | 21 | | 32 | 6 | | 33 | 43 | | 34 | 7 | | 35 | 13 | | 36 | 7 | | 37 | 23 | | 38 | 8 | | 39 | 3 | | 40 | 9 | | 41 | 7 | | 42 | 26 | | 43 | 3 | | 44 | 7 | | 45 | 32 | | 46 | 11 | | 47 | 5 | | 48 | 15 | | 49 | 48 |
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| 62.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4367816091954023 | | totalSentences | 87 | | uniqueOpeners | 38 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 83 | | matches | | 0 | "Then a shape in a" | | 1 | "Then he turned into the" | | 2 | "Somewhere water dripped with the" | | 3 | "Then she thought about the" | | 4 | "Then he raised one hand" | | 5 | "Somewhere deeper down, a door" |
| | ratio | 0.072 | |
| 75.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 83 | | matches | | 0 | "She counted the seconds since" | | 1 | "He'd been watching the Nest" | | 2 | "She took the corner tight," | | 3 | "Her leather watch — the" | | 4 | "she shouted, because it was" | | 5 | "He didn't stop." | | 6 | "He never stopped." | | 7 | "He vaulted a low wall" | | 8 | "She landed in a crouch," | | 9 | "He went anyway." | | 10 | "She pushed after him, breath" | | 11 | "She scanned faces in flashes" | | 12 | "She ploughed through, caught the" | | 13 | "He glanced back at her" | | 14 | "Her chest heaved." | | 15 | "She knew, with the animal" | | 16 | "She reached for her shoulder" | | 17 | "She looked at the stairwell." | | 18 | "She looked at her watch," | | 19 | "She thought of DS Morris," |
| | ratio | 0.361 | |
| 92.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 83 | | matches | | 0 | "The rain came sideways off" | | 1 | "She counted the seconds since" | | 2 | "He'd been watching the Nest" | | 3 | "People watched that bar." | | 4 | "People waited outside it." | | 5 | "She took the corner tight," | | 6 | "A bin had gone over" | | 7 | "Her leather watch — the" | | 8 | "she shouted, because it was" | | 9 | "He didn't stop." | | 10 | "He never stopped." | | 11 | "He vaulted a low wall" | | 12 | "She landed in a crouch," | | 13 | "The yard fed into a" | | 14 | "He went anyway." | | 15 | "Stupidity or route-planning, she couldn't" | | 16 | "She pushed after him, breath" | | 17 | "Camden was a river of" | | 18 | "She scanned faces in flashes" | | 19 | "People scattered the way people" |
| | ratio | 0.735 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | | ratio | 0.012 | |
| 12.99% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 10 | | matches | | 0 | "Ahead, the suspect — lean, hooded, moving with the easy economy of someone who'd done this before — cut left between a laundrette and a shuttered off-licence, h…" | | 1 | "He'd been watching the Nest for two hours before that, standing under Silas's green neon with his collar up, and that was the part that had put him in her noteb…" | | 2 | "Pale, wet, with the particular flat terror of a man who hadn't expected to be caught but also hadn't expected anyone to keep up." | | 3 | "The buildings crowded in — brickwork black with rain, fire escapes, a wall of peeling posters advertising nights that had happened years ago — and at the end of…" | | 4 | "Beyond the barrier, darkness with a faint, impossible warmth to it — amber light, far below, and a sound that wasn't quite traffic." | | 5 | "She thought of DS Morris, of a corridor three years ago that had smelled of nothing at all, of a door that had been there and then hadn't, of the file she'd reb…" | | 6 | "Somewhere water dripped with the metronomic patience of a place that had been dripping since before the line was closed." | | 7 | "That was the detail that raised the hair on her forearms — not that they ignored a police detective standing at the top of a stairwell in full uniform, but that…" | | 8 | "Beside it, at roughly chest height, a bracket held a shallow dish, and in the dish lay a small white object that caught the torchlight and did not reflect it." | | 9 | "Three options, and her training laid them out with the flat clarity of a briefing: call it in — she couldn't; wait for backup — she'd never get it down here; fo…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 1 | | effectiveRatio | 0.667 | |