| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.467 | | leniency | 0.933 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.95% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1637 | | 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) | |
| 84.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1637 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "streaming" | | 1 | "footsteps" | | 2 | "echoing" | | 3 | "echoed" | | 4 | "vibrated" |
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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 | 3 | | narrationSentences | 128 | | matches | | 0 | "was scared" | | 1 | "was angry" | | 2 | "was scared" |
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| 87.05% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1645 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 73 | | wordCount | 1511 | | uniqueNames | 31 | | maxNameDensity | 1.13 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 17 | | Raven | 2 | | Nest | 3 | | London | 1 | | Vane | 2 | | Herrera | 12 | | Silas | 5 | | Saint | 2 | | Christopher | 2 | | Shaftesbury | 1 | | Met | 1 | | Greek | 1 | | Street | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Leicester | 1 | | Square | 1 | | Northern | 1 | | Underground | 1 | | Camden | 2 | | Victorian | 1 | | Tube | 2 | | Stalls | 1 | | Veil | 1 | | Market | 1 | | Harlow | 2 | | Morris | 3 | | English | 1 | | Seville | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Vane" | | 3 | "Herrera" | | 4 | "Silas" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Met" | | 8 | "Underground" | | 9 | "Camden" | | 10 | "Market" | | 11 | "Harlow" | | 12 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Shaftesbury" | | 3 | "Greek" | | 4 | "Street" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "Leicester" | | 9 | "English" | | 10 | "Seville" |
| | globalScore | 0.937 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "cause this was obviously, dangerously off-bo" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.608 | | wordCount | 1645 | | matches | | 0 | "not on her but on a figure standing between two stalls" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 27.88 | | std | 24.2 | | cv | 0.868 | | sampleLengths | | 0 | 9 | | 1 | 94 | | 2 | 33 | | 3 | 10 | | 4 | 59 | | 5 | 17 | | 6 | 28 | | 7 | 5 | | 8 | 70 | | 9 | 2 | | 10 | 25 | | 11 | 2 | | 12 | 3 | | 13 | 56 | | 14 | 27 | | 15 | 53 | | 16 | 3 | | 17 | 2 | | 18 | 49 | | 19 | 43 | | 20 | 22 | | 21 | 48 | | 22 | 21 | | 23 | 14 | | 24 | 43 | | 25 | 57 | | 26 | 51 | | 27 | 3 | | 28 | 49 | | 29 | 9 | | 30 | 58 | | 31 | 10 | | 32 | 4 | | 33 | 13 | | 34 | 14 | | 35 | 102 | | 36 | 11 | | 37 | 8 | | 38 | 25 | | 39 | 35 | | 40 | 5 | | 41 | 87 | | 42 | 13 | | 43 | 4 | | 44 | 10 | | 45 | 10 | | 46 | 9 | | 47 | 18 | | 48 | 37 | | 49 | 18 |
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| 91.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 128 | | matches | | 0 | "being made " | | 1 | "been bricked" | | 2 | "been hung" | | 3 | "was scared" | | 4 | "been struck" |
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| 65.59% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 248 | | matches | | 0 | "wasn't trying" | | 1 | "was already opening" | | 2 | "wasn't running" | | 3 | "was walking" | | 4 | "was already reaching" |
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| 17.73% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 137 | | ratio | 0.044 | | matches | | 0 | "Eighteen years on the Met had taught her how to tail without being made — not too close, never the same side of the street for long, using reflections in shop windows — but Herrera was jumpy." | | 1 | "He shoved through the barriers, vaulted a decommissioned service gate that said NO ENTRY - STAFF ONLY, and disappeared down a service stairwell that smelled of damp concrete and old electricity." | | 2 | "It had to be — the curved Victorian tilework, the faded roundel half-covered in graffiti." | | 3 | "People — if you could call all of them people — walked between the stalls." | | 4 | "His warm brown eyes were fixed not on her but on a figure standing between two stalls — a woman in a waxed coat holding out a palm." | | 5 | "If she followed —" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.02014218009478673 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008293838862559242 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 12.01 | | std | 9.39 | | cv | 0.782 | | sampleLengths | | 0 | 9 | | 1 | 33 | | 2 | 3 | | 3 | 26 | | 4 | 9 | | 5 | 23 | | 6 | 12 | | 7 | 1 | | 8 | 20 | | 9 | 10 | | 10 | 5 | | 11 | 6 | | 12 | 23 | | 13 | 2 | | 14 | 13 | | 15 | 10 | | 16 | 12 | | 17 | 5 | | 18 | 24 | | 19 | 4 | | 20 | 5 | | 21 | 8 | | 22 | 16 | | 23 | 37 | | 24 | 9 | | 25 | 2 | | 26 | 18 | | 27 | 7 | | 28 | 2 | | 29 | 3 | | 30 | 15 | | 31 | 16 | | 32 | 4 | | 33 | 21 | | 34 | 6 | | 35 | 8 | | 36 | 13 | | 37 | 13 | | 38 | 7 | | 39 | 4 | | 40 | 29 | | 41 | 3 | | 42 | 2 | | 43 | 26 | | 44 | 16 | | 45 | 7 | | 46 | 6 | | 47 | 6 | | 48 | 31 | | 49 | 22 |
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| 61.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.4411764705882353 | | totalSentences | 136 | | uniqueOpeners | 60 | |
| 88.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 113 | | matches | | 0 | "Only it wasn't abandoned now." | | 1 | "Perhaps a cop leaning into" | | 2 | "Perhaps Quinn's ignorance was its" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 113 | | matches | | 0 | "Her left wrist itched under" | | 1 | "She knew the taller one." | | 2 | "She gave him thirty feet" | | 3 | "He checked over his shoulder" | | 4 | "He cut left down an" | | 5 | "She went after him, boots" | | 6 | "He knocked over a stack" | | 7 | "He was faster than she" | | 8 | "They burst onto Charing Cross" | | 9 | "He sprinted straight across, nearly" | | 10 | "He ducked down into Leicester" | | 11 | "She took the stairs two" | | 12 | "Her watch ticked loud against" | | 13 | "He shoved through the barriers," | | 14 | "She pushed it and stepped" | | 15 | "It was Camden." | | 16 | "It had to be —" | | 17 | "She had written in her" | | 18 | "Her notebook was in her" | | 19 | "His warm brown eyes were" |
| | ratio | 0.274 | |
| 79.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 113 | | matches | | 0 | "The rain had turned Soho's" | | 1 | "Harlow Quinn stood under the" | | 2 | "The Raven's Nest." | | 3 | "The sign buzzed and threw" | | 4 | "Her left wrist itched under" | | 5 | "The back exit cut open" | | 6 | "She knew the taller one." | | 7 | "Silas Vane, owner of the" | | 8 | "The other was younger, olive" | | 9 | "Quinn had a photo of" | | 10 | "The bookshelf that swung inward." | | 11 | "Herrera said something to Silas," | | 12 | "Silas went back inside." | | 13 | "Quinn pushed off the wall." | | 14 | "She gave him thirty feet" | | 15 | "Rain needled her closely cropped" | | 16 | "He checked over his shoulder" | | 17 | "Quinn dropped the pretense and" | | 18 | "He cut left down an" | | 19 | "She went after him, boots" |
| | ratio | 0.761 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 113 | | matches | | 0 | "To any drunk tourist it" | | 1 | "To Quinn, after six months" | | 2 | "To call it in." | | 3 | "To not step one foot" | | 4 | "If she let Herrera go," | | 5 | "If she followed —" |
| | ratio | 0.053 | |
| 72.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 6 | | matches | | 0 | "He shoved through the barriers, vaulted a decommissioned service gate that said NO ENTRY - STAFF ONLY, and disappeared down a service stairwell that smelled of …" | | 1 | "Cold air came through it that had no business being cold this far from the surface." | | 2 | "A hidden supernatural black market that sells enchanted goods, banned alchemical substances, information." | | 3 | "Every rational part of Harlow Quinn's brain, the part that had filled out forms in triplicate and done firearms recertification and stood at DS Morris's funeral…" | | 4 | "The Saint Christopher medallion glinted under the lanterns, the patron saint of travelers, of people who carry others across dangerous water." | | 5 | "A bell rang somewhere deep in the tunnel, a low, brassy toll that vibrated in her teeth." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 16.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn breathed (breathe)" | | 1 | "she shouted (shout)" |
| | dialogueSentences | 15 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.333 | | effectiveRatio | 0.267 | |