| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1293 | | 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) | |
| 80.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1293 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "tinged" | | 1 | "pulsed" | | 2 | "echoed" | | 3 | "flicker" | | 4 | "gloom" |
| |
| 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 | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1284 | | ratio | 0 | | matches | (empty) | |
| 75.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 1 | | matches | | 0 | "Junkie folklore, she told herself." |
| |
| 90.43% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1259 | | uniqueNames | 11 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Harlow | 1 | | Quinn | 15 | | Morris | 3 | | Met | 1 | | Tube | 1 | | Victorian | 2 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.904 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | 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 | 1284 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 137 | | matches | | 0 | "watching that satchel" | | 1 | "ignore that warning" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 30.57 | | std | 18.61 | | cv | 0.609 | | sampleLengths | | 0 | 48 | | 1 | 32 | | 2 | 52 | | 3 | 53 | | 4 | 48 | | 5 | 35 | | 6 | 66 | | 7 | 22 | | 8 | 65 | | 9 | 11 | | 10 | 24 | | 11 | 56 | | 12 | 44 | | 13 | 9 | | 14 | 50 | | 15 | 65 | | 16 | 12 | | 17 | 32 | | 18 | 23 | | 19 | 3 | | 20 | 43 | | 21 | 7 | | 22 | 56 | | 23 | 7 | | 24 | 16 | | 25 | 25 | | 26 | 10 | | 27 | 27 | | 28 | 26 | | 29 | 29 | | 30 | 5 | | 31 | 49 | | 32 | 4 | | 33 | 23 | | 34 | 6 | | 35 | 53 | | 36 | 32 | | 37 | 12 | | 38 | 32 | | 39 | 25 | | 40 | 28 | | 41 | 19 |
| |
| 97.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 134 | | matches | | 0 | "been gouged" | | 1 | "being caught" | | 2 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 221 | | matches | | 0 | "was going" | | 1 | "was running" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 137 | | ratio | 0.051 | | matches | | 0 | "The suspect ran thirty yards ahead—a wiry figure in a black hood, one hand clamped to a canvas satchel like it held something alive." | | 1 | "The hole exhaled cold air tinged with dust and damp and something else—something metallic and sharp, like ozone after lightning." | | 2 | "She'd seen the marks on his chest—pale burns the shape of fingerprints, laid in a pattern no human hand could make." | | 3 | "Graffiti saturated the walls—tags and symbols she didn't recognise." | | 4 | "He passed something to a stallholder—a small pale object, a smooth knuckle of bone—and received a folded cloth in return." | | 5 | "Then a figure stepped into her path—a man with olive skin and short curly dark hair, a Saint Christopher medallion catching the amber light." | | 6 | "Beyond the arch—nothing." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1272 | | adjectiveStacks | 1 | | stackExamples | | 0 | "short curly dark hair," |
| | adverbCount | 22 | | adverbRatio | 0.01729559748427673 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0023584905660377358 | |
| 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 | 9.37 | | std | 6.46 | | cv | 0.689 | | sampleLengths | | 0 | 7 | | 1 | 17 | | 2 | 24 | | 3 | 7 | | 4 | 17 | | 5 | 8 | | 6 | 15 | | 7 | 3 | | 8 | 5 | | 9 | 14 | | 10 | 4 | | 11 | 11 | | 12 | 9 | | 13 | 6 | | 14 | 8 | | 15 | 10 | | 16 | 20 | | 17 | 6 | | 18 | 16 | | 19 | 14 | | 20 | 12 | | 21 | 8 | | 22 | 7 | | 23 | 20 | | 24 | 11 | | 25 | 11 | | 26 | 14 | | 27 | 6 | | 28 | 3 | | 29 | 21 | | 30 | 9 | | 31 | 5 | | 32 | 8 | | 33 | 12 | | 34 | 17 | | 35 | 2 | | 36 | 1 | | 37 | 1 | | 38 | 25 | | 39 | 7 | | 40 | 11 | | 41 | 18 | | 42 | 6 | | 43 | 9 | | 44 | 9 | | 45 | 14 | | 46 | 9 | | 47 | 3 | | 48 | 12 | | 49 | 4 |
| |
| 49.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3357664233576642 | | totalSentences | 137 | | uniqueOpeners | 46 | |
| 78.13% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 128 | | matches | | 0 | "Then she went through the" | | 1 | "Then he was running again," | | 2 | "Then a figure stepped into" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 128 | | matches | | 0 | "She'd been watching that satchel" | | 1 | "It matched the one from" | | 2 | "She kept going." | | 3 | "She'd chased murderers, smugglers, men" | | 4 | "He moved like someone who" | | 5 | "He cut left into an" | | 6 | "Her thumb brushed the worn" | | 7 | "She'd worn it through his" | | 8 | "She'd seen the marks on" | | 9 | "She was certain of it." | | 10 | "He'd fled the moment she'd" | | 11 | "Her contacts had whispered about" | | 12 | "She'd filed those stories under" | | 13 | "She pulled out her phone," | | 14 | "She reached the landing." | | 15 | "She crept past the barriers," | | 16 | "Her service pistol sat in" | | 17 | "She left it there, for" | | 18 | "He passed something to a" | | 19 | "His gaze crossed the cavern" |
| | ratio | 0.281 | |
| 30.31% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 110 | | totalSentences | 128 | | matches | | 0 | "Rain lashed Camden High Street" | | 1 | "Detective Harlow Quinn's boots slapped" | | 2 | "The suspect ran thirty yards" | | 3 | "She'd been watching that satchel" | | 4 | "It matched the one from" | | 5 | "The one the CCTV caught," | | 6 | "Quinn vaulted a low iron" | | 7 | "She kept going." | | 8 | "She'd chased murderers, smugglers, men" | | 9 | "This one was different." | | 10 | "He moved like someone who" | | 11 | "He cut left into an" | | 12 | "Quinn followed, shoulder clipping a" | | 13 | "The alley reeked of rot" | | 14 | "The suspect went over in" | | 15 | "Quinn hit the fence, climbed," | | 16 | "The old Camden deep-level shelter," | | 17 | "The suspect wrenched a loose" | | 18 | "Quinn slowed to a halt" | | 19 | "Rain drummed on the hoarding" |
| | ratio | 0.859 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 128 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "Beyond them, a set of steps descended, raw-edged and freshly cut, stone that didn't match the Victorian work." | | 1 | "Follow a man through a doorway into a world that would break every assumption she'd built her life on, or turn around, walk back up into the rain, and let DS Mo…" |
| |
| 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 | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |