| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1636 | | 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) | |
| 26.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1636 | | totalAiIsms | 24 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "fractured" | | 1 | "footsteps" | | 2 | "cascading" | | 3 | "glinting" | | 4 | "depths" | | 5 | "standard" | | 6 | "footfall" | | 7 | "echoed" | | 8 | "flicked" | | 9 | "glint" | | 10 | "scanned" | | 11 | "velvet" | | 12 | "silence" | | 13 | "potential" | | 14 | "measured" | | 15 | "etched" | | 16 | "weight" | | 17 | "familiar" |
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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 | 148 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 148 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 160 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1636 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1559 | | uniqueNames | 19 | | maxNameDensity | 1.28 | | worstName | "Herrera" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Quinn | 1 | | Oxford | 1 | | Street | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 1 | | Herrera | 20 | | Camden | 1 | | Morris | 2 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | English | 1 | | Harlow | 14 | | Water | 3 | | Vendors | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Tube" | | 7 | "Harlow" | | 8 | "Water" | | 9 | "Vendors" |
| | places | | 0 | "Oxford" | | 1 | "Street" | | 2 | "Tottenham" | | 3 | "Court" | | 4 | "Road" | | 5 | "Camden" |
| | globalScore | 0.859 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 120 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1636 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 160 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 43.05 | | std | 24.66 | | cv | 0.573 | | sampleLengths | | 0 | 58 | | 1 | 86 | | 2 | 63 | | 3 | 101 | | 4 | 61 | | 5 | 66 | | 6 | 65 | | 7 | 77 | | 8 | 14 | | 9 | 67 | | 10 | 94 | | 11 | 43 | | 12 | 62 | | 13 | 31 | | 14 | 59 | | 15 | 23 | | 16 | 61 | | 17 | 30 | | 18 | 47 | | 19 | 43 | | 20 | 37 | | 21 | 4 | | 22 | 11 | | 23 | 16 | | 24 | 2 | | 25 | 43 | | 26 | 53 | | 27 | 27 | | 28 | 56 | | 29 | 34 | | 30 | 43 | | 31 | 25 | | 32 | 8 | | 33 | 24 | | 34 | 32 | | 35 | 26 | | 36 | 10 | | 37 | 34 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 148 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 267 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 160 | | ratio | 0.006 | | matches | | 0 | "By then Herrera could vanish among the moving stalls; the market shifted locations every full moon, according to the single informant tip she had extracted from a terrified fence last week." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 296 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 2 | | adverbRatio | 0.006756756756756757 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.006756756756756757 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 160 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 160 | | mean | 10.23 | | std | 5.67 | | cv | 0.554 | | sampleLengths | | 0 | 12 | | 1 | 10 | | 2 | 8 | | 3 | 13 | | 4 | 15 | | 5 | 4 | | 6 | 11 | | 7 | 14 | | 8 | 10 | | 9 | 23 | | 10 | 24 | | 11 | 5 | | 12 | 6 | | 13 | 13 | | 14 | 6 | | 15 | 3 | | 16 | 13 | | 17 | 4 | | 18 | 13 | | 19 | 7 | | 20 | 16 | | 21 | 5 | | 22 | 11 | | 23 | 14 | | 24 | 18 | | 25 | 7 | | 26 | 3 | | 27 | 20 | | 28 | 3 | | 29 | 6 | | 30 | 12 | | 31 | 8 | | 32 | 10 | | 33 | 13 | | 34 | 9 | | 35 | 13 | | 36 | 12 | | 37 | 8 | | 38 | 8 | | 39 | 18 | | 40 | 7 | | 41 | 7 | | 42 | 16 | | 43 | 14 | | 44 | 13 | | 45 | 15 | | 46 | 6 | | 47 | 2 | | 48 | 6 | | 49 | 13 |
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| 55.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.33125 | | totalSentences | 160 | | uniqueOpeners | 53 | |
| 22.99% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 145 | | matches | | 0 | "Instead she extended a palm" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 145 | | matches | | 0 | "She shouted across the traffic," | | 1 | "Her worn leather watch bumped" | | 2 | "She kept the figure in" | | 3 | "She recognized his outline from" | | 4 | "She reached the alley mouth." | | 5 | "She slid on the wet" | | 6 | "He shouldered a service door" | | 7 | "She checked the corridor behind" | | 8 | "Her mind flashed to DS" | | 9 | "She stepped down." | | 10 | "She spotted Herrera weaving between" | | 11 | "He handed his bundle to" | | 12 | "His eyes, warm brown even" | | 13 | "She kept her service pistol" | | 14 | "Her sharp jaw tensed." | | 15 | "She scanned for exits." | | 16 | "She weighed the cost." | | 17 | "His hood concealed the eyes" | | 18 | "He gestured with the blade" | | 19 | "She catalogued the risks instead." |
| | ratio | 0.29 | |
| 11.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 145 | | matches | | 0 | "Harlow Quinn spotted the silhouette" | | 1 | "Streetlights cast fractured reflections on" | | 2 | "She shouted across the traffic," | | 3 | "Footsteps slapped water as the" | | 4 | "Harlow lengthened her stride." | | 5 | "Her worn leather watch bumped" | | 6 | "She kept the figure in" | | 7 | "The man clutched something small" | | 8 | "She recognized his outline from" | | 9 | "The Saint Christopher medallion likely" | | 10 | "She reached the alley mouth." | | 11 | "A chain-link gate swung open" | | 12 | "Harlow followed, shoulder brushing wet" | | 13 | "Pipes dripped overhead." | | 14 | "The alley narrowed into a" | | 15 | "Steam rose from grates." | | 16 | "She slid on the wet" | | 17 | "Herrera bolted right at the" | | 18 | "He shouldered a service door" | | 19 | "Harlow paused on the threshold." |
| | ratio | 0.897 | |
| 34.48% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 145 | | matches | | | ratio | 0.007 | |
| 78.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 78 | | technicalSentenceCount | 7 | | matches | | 0 | "He shouldered a service door that led to a stairwell descending beneath the Camden street grid." | | 1 | "He handed his bundle to a vendor who tucked it beneath a stack of rolled maps." | | 2 | "An old black-and-white photograph tacked to one wall showed a 1950s crowd on this same platform, faces blurred as if the emulsion itself rejected their features…" | | 3 | "The vendors carried goods that defied standard evidence protocols, substances banned in every NHS advisory." | | 4 | "Blood traces on that case matched patterns the pathologist labeled anomalous, patterns that appeared again in recent theft scenes." | | 5 | "Herrera appeared again at the next bend, pausing beside a stall that displayed silvered mirrors etched with symbols." | | 6 | "Each foot placement avoided pools of standing water that might mask footfalls or hide tripwires." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |