| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.191 | | leniency | 0.383 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1696 | | 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) | |
| 67.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1696 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "weight" | | 1 | "glinting" | | 2 | "footsteps" | | 3 | "flickered" | | 4 | "constructed" | | 5 | "shimmered" | | 6 | "pulsed" | | 7 | "velvet" | | 8 | "tracing" | | 9 | "calculating" | | 10 | "pulse" |
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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 | 124 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 124 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1696 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1294 | | uniqueNames | 22 | | maxNameDensity | 0.85 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Camden | 2 | | Met | 2 | | London | 2 | | Lock | 1 | | Underground | 1 | | Tube | 1 | | Morris | 1 | | Victorian | 1 | | Transport | 1 | | Bovril | 1 | | Glock | 1 | | Saint | 1 | | Christopher | 1 | | Marsh | 1 | | Procedure | 3 | | Tomás | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Procedure" | | 6 | "Tomás" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Raven" | | 4 | "Camden" | | 5 | "London" | | 6 | "Marsh" |
| | globalScore | 1 | | windowScore | 1 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a disused Tube entrance" | | 1 | "velvet that seemed to drink the light rather than reflect it" | | 2 | "illumination that seemed to lean toward her" |
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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 | 1696 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 162 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 22.03 | | std | 23.33 | | cv | 1.059 | | sampleLengths | | 0 | 18 | | 1 | 38 | | 2 | 69 | | 3 | 38 | | 4 | 2 | | 5 | 6 | | 6 | 114 | | 7 | 26 | | 8 | 63 | | 9 | 18 | | 10 | 9 | | 11 | 59 | | 12 | 26 | | 13 | 57 | | 14 | 4 | | 15 | 65 | | 16 | 13 | | 17 | 65 | | 18 | 98 | | 19 | 16 | | 20 | 63 | | 21 | 20 | | 22 | 5 | | 23 | 2 | | 24 | 56 | | 25 | 2 | | 26 | 32 | | 27 | 6 | | 28 | 3 | | 29 | 7 | | 30 | 57 | | 31 | 7 | | 32 | 20 | | 33 | 2 | | 34 | 40 | | 35 | 28 | | 36 | 7 | | 37 | 4 | | 38 | 4 | | 39 | 9 | | 40 | 1 | | 41 | 4 | | 42 | 17 | | 43 | 4 | | 44 | 27 | | 45 | 42 | | 46 | 17 | | 47 | 19 | | 48 | 2 | | 49 | 3 |
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| 91.11% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 124 | | matches | | 0 | "was, moved" | | 1 | "was gone" | | 2 | "been colonised" | | 3 | "were rolled" | | 4 | "was gone" |
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| 83.55% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 229 | | matches | | 0 | "was heading" | | 1 | "wasn't heading" | | 2 | "was running" | | 3 | "was selling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 162 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1301 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.027671022290545733 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004611837048424289 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 10.47 | | std | 8.47 | | cv | 0.809 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 13 | | 3 | 12 | | 4 | 3 | | 5 | 15 | | 6 | 19 | | 7 | 25 | | 8 | 5 | | 9 | 5 | | 10 | 22 | | 11 | 16 | | 12 | 2 | | 13 | 3 | | 14 | 3 | | 15 | 10 | | 16 | 9 | | 17 | 27 | | 18 | 4 | | 19 | 23 | | 20 | 10 | | 21 | 3 | | 22 | 5 | | 23 | 23 | | 24 | 16 | | 25 | 6 | | 26 | 4 | | 27 | 23 | | 28 | 2 | | 29 | 2 | | 30 | 12 | | 31 | 24 | | 32 | 6 | | 33 | 10 | | 34 | 1 | | 35 | 1 | | 36 | 9 | | 37 | 10 | | 38 | 14 | | 39 | 35 | | 40 | 4 | | 41 | 5 | | 42 | 17 | | 43 | 11 | | 44 | 6 | | 45 | 4 | | 46 | 36 | | 47 | 4 | | 48 | 9 | | 49 | 21 |
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| 66.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.4691358024691358 | | totalSentences | 162 | | uniqueOpeners | 76 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 108 | | matches | | 0 | "Then he dropped below the" | | 1 | "Just for a second, a" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 108 | | matches | | 0 | "She'd clocked him outside the" | | 1 | "She'd lost visual twice already." | | 2 | "She wouldn't lose it again." | | 3 | "He vaulted a bollard at" | | 4 | "He didn't stop." | | 5 | "They never stopped." | | 6 | "He was heading deeper into" | | 7 | "He was running toward the" | | 8 | "She rounded the corner onto" | | 9 | "He turned to face her," | | 10 | "He held up his right" | | 11 | "She drew her weapon." | | 12 | "She took the stairs." | | 13 | "They just watched, the way" | | 14 | "He nodded toward her right" | | 15 | "She looked down." | | 16 | "It didn't reach his eyes." | | 17 | "He stepped closer, producing a" | | 18 | "He gestured with an antiseptic" | | 19 | "She extended her hand." |
| | ratio | 0.269 | |
| 57.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 108 | | matches | | 0 | "The suspect cut left on" | | 1 | "She'd clocked him outside the" | | 2 | "Rain sheeted across Camden in" | | 3 | "Quinn's jacket was soaked through," | | 4 | "The suspect, whoever he was," | | 5 | "She'd lost visual twice already." | | 6 | "She wouldn't lose it again." | | 7 | "He vaulted a bollard at" | | 8 | "The worn leather of her" | | 9 | "He didn't stop." | | 10 | "They never stopped." | | 11 | "He was heading deeper into" | | 12 | "Quinn knew these streets." | | 13 | "He was running toward the" | | 14 | "She rounded the corner onto" | | 15 | "Puddles caught her reflection in" | | 16 | "The suspect had stopped." | | 17 | "A faded roundel sign so" | | 18 | "He turned to face her," | | 19 | "He held up his right" |
| | ratio | 0.806 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 108 | | matches | (empty) | | ratio | 0 | |
| 75.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 5 | | matches | | 0 | "The sound changed at the threshold, her footsteps trading the wet slap of pavement for something hollow and resonant, as if the space below was larger than any …" | | 1 | "The way every senior officer at the Met had nodded along to the official report, a mugging gone wrong, as if they hadn't noticed the body was entirely drained o…" | | 2 | "Lanterns hung from the ceiling on chains, casting a warm amber glow over merchandise that made Quinn's fingers tighten on her weapon." | | 3 | "The crowd, if she could call it that, moved with the practised indifference of regular customers." | | 4 | "He worked fast, cleaning the wound with movements that spoke of medical training, precise, economical, the kind of muscle memory a classroom couldn't teach." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 47 | | tagDensity | 0.043 | | leniency | 0.085 | | rawRatio | 0 | | effectiveRatio | 0 | |