| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1333 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 69.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1333 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "clandestine" | | 1 | "familiar" | | 2 | "electric" | | 3 | "pulse" | | 4 | "coded" | | 5 | "echoing" | | 6 | "pulsed" | | 7 | "footsteps" |
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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 | 1 | | narrationSentences | 111 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1333 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1330 | | uniqueNames | 24 | | maxNameDensity | 0.9 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 3 | | Harlow | 1 | | Quinn | 12 | | Tomás | 1 | | Herrera | 6 | | Camden | 4 | | Saint | 1 | | Christopher | 2 | | Seville-born | 1 | | London | 2 | | Crown | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Morris | 5 | | St | 1 | | Lock | 1 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Crown" | | 9 | "Morris" | | 10 | "Market" | | 11 | "Police" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Seville-born" | | 3 | "London" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "St" | | 8 | "Lock" | | 9 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed deeper than the tunnel should allow" |
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| 49.96% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.5 | | wordCount | 1333 | | matches | | 0 | "not from exertion, but from the familiar, electric surge of the hunt" | | 1 | "not with guilt, but with a pleading terror" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 112 | | matches | | 0 | "knew that behind" | | 1 | "blurred, that part" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 51.27 | | std | 44.8 | | cv | 0.874 | | sampleLengths | | 0 | 141 | | 1 | 86 | | 2 | 4 | | 3 | 17 | | 4 | 168 | | 5 | 126 | | 6 | 37 | | 7 | 51 | | 8 | 54 | | 9 | 46 | | 10 | 2 | | 11 | 53 | | 12 | 4 | | 13 | 49 | | 14 | 10 | | 15 | 3 | | 16 | 129 | | 17 | 54 | | 18 | 42 | | 19 | 50 | | 20 | 51 | | 21 | 2 | | 22 | 75 | | 23 | 16 | | 24 | 42 | | 25 | 21 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 111 | | matches | | 0 | "been locked" | | 1 | "been transformed" | | 2 | "been covered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 225 | | matches | | 0 | "was already moving" | | 1 | "was heading" |
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| 91.84% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 112 | | ratio | 0.018 | | matches | | 0 | "Eighteen years of decorated service had drilled the waste from her movements; she did not sway, did not flail." | | 1 | "The market moved every full moon, she had read; tonight it had anchored itself beneath Camden, a parasite feeding on secrets." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1342 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.020119225037257823 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.005961251862891207 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 11.9 | | std | 9.96 | | cv | 0.837 | | sampleLengths | | 0 | 24 | | 1 | 33 | | 2 | 28 | | 3 | 36 | | 4 | 13 | | 5 | 7 | | 6 | 11 | | 7 | 42 | | 8 | 8 | | 9 | 25 | | 10 | 4 | | 11 | 16 | | 12 | 1 | | 13 | 16 | | 14 | 19 | | 15 | 36 | | 16 | 7 | | 17 | 5 | | 18 | 40 | | 19 | 30 | | 20 | 15 | | 21 | 4 | | 22 | 4 | | 23 | 12 | | 24 | 5 | | 25 | 17 | | 26 | 15 | | 27 | 5 | | 28 | 2 | | 29 | 34 | | 30 | 2 | | 31 | 18 | | 32 | 3 | | 33 | 5 | | 34 | 3 | | 35 | 2 | | 36 | 28 | | 37 | 4 | | 38 | 3 | | 39 | 15 | | 40 | 6 | | 41 | 3 | | 42 | 12 | | 43 | 7 | | 44 | 5 | | 45 | 9 | | 46 | 29 | | 47 | 16 | | 48 | 4 | | 49 | 11 |
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| 54.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.38392857142857145 | | totalSentences | 112 | | uniqueOpeners | 43 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 96 | | matches | | 0 | "Then the stairs ended, and" | | 1 | "Then she stepped through the" |
| | ratio | 0.021 | |
| 57.50% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 96 | | matches | | 0 | "Her jaw, sharp as a" | | 1 | "Her brown eyes never left" | | 2 | "He clutched a canvas satchel" | | 3 | "Her voice cut through the" | | 4 | "She lunged into the street," | | 5 | "She drove forward, elbows tight," | | 6 | "He skirted a pack of" | | 7 | "They left Soho behind." | | 8 | "He crossed Tottenham Court Road" | | 9 | "She weaved through the chaos," | | 10 | "Her heart hammered, not from" | | 11 | "His olive skin washed grey" | | 12 | "He turned toward Camden." | | 13 | "Her boots squelched." | | 14 | "She kept her eyes on" | | 15 | "He ducked beneath a railway" | | 16 | "It gaped open, breathing out" | | 17 | "She drew her torch." | | 18 | "Her pulse ticked in her" | | 19 | "Her torch beam scraped across" |
| | ratio | 0.406 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 96 | | matches | | 0 | "The green neon of The" | | 1 | "Detective Harlow Quinn stood across" | | 2 | "Her jaw, sharp as a" | | 3 | "Her brown eyes never left" | | 4 | "The warm brown eyes that" | | 5 | "He clutched a canvas satchel" | | 6 | "The Saint Christopher medallion around" | | 7 | "Quinn was already moving." | | 8 | "Her voice cut through the" | | 9 | "She lunged into the street," | | 10 | "She drove forward, elbows tight," | | 11 | "The twenty-nine-year-old Seville-born former paramedic" | | 12 | "A five-foot-ten man who had" | | 13 | "He skirted a pack of" | | 14 | "Quinn followed, her breath controlled," | | 15 | "They left Soho behind." | | 16 | "Herrera did not slow." | | 17 | "He crossed Tottenham Court Road" | | 18 | "Quinn did not break stride." | | 19 | "She weaved through the chaos," |
| | ratio | 0.833 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 9 | | matches | | 0 | "Detective Harlow Quinn stood across the street beneath a broken awning, water dripping from the canvas in steady, metronomic drops that matched the ticking of t…" | | 1 | "The warm brown eyes that had laughed at her two nights prior, when she had questioned him in the corridor of his Camden bedsit, snapped toward her hiding spot w…" | | 2 | "A five-foot-ten man who had moved to London to heal people, who had lost his NHS license for administering unauthorized treatments to patients the Crown did not…" | | 3 | "She weaved through the chaos, rain lashing her face, blurring the streetlamps into halos of sodium glare." | | 4 | "Quinn rounded the corner thirty seconds later and found nothing but a dead end walled by black rubbish bins and a rusted iron gate that should have been locked." | | 5 | "Stalls of reclaimed Tube carriages and rotting mahogany stretched into shadows that seemed deeper than the tunnel should allow." | | 6 | "Vendors hunched beneath canopies of stitched leather, selling wares that hummed and whispered: jars of trapped lightning, meat that pulsed though no heart beat …" | | 7 | "She thought of protocol, of the disciplinary review that had nearly stripped her rank after Morris's death, of the superintendent's warning to stop chasing ghos…" | | 8 | "She had a sharp jaw, eighteen years of decorated service, and a dead partner who had trusted her to finish what they started." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |