| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.242 | | leniency | 0.485 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1322 | | 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) | |
| 92.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1322 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1332 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 933 | | uniqueNames | 6 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Tube | 1 | | Mercer | 8 | | Morris | 3 | | Whitechapel | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Mercer" | | 3 | "Morris" |
| | places | | | globalScore | 0.857 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "as if reaching for something" |
| |
| 49.85% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.502 | | wordCount | 1332 | | matches | | 0 | "not at the body, not at the stalls, but at the far end of the platform" | | 1 | "not at the stalls, but at the far end of the platform" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 28.34 | | std | 23.65 | | cv | 0.835 | | sampleLengths | | 0 | 88 | | 1 | 43 | | 2 | 55 | | 3 | 29 | | 4 | 52 | | 5 | 4 | | 6 | 31 | | 7 | 76 | | 8 | 18 | | 9 | 69 | | 10 | 7 | | 11 | 25 | | 12 | 8 | | 13 | 54 | | 14 | 28 | | 15 | 47 | | 16 | 6 | | 17 | 10 | | 18 | 29 | | 19 | 10 | | 20 | 41 | | 21 | 21 | | 22 | 39 | | 23 | 12 | | 24 | 5 | | 25 | 42 | | 26 | 4 | | 27 | 5 | | 28 | 43 | | 29 | 8 | | 30 | 4 | | 31 | 53 | | 32 | 87 | | 33 | 5 | | 34 | 46 | | 35 | 5 | | 36 | 19 | | 37 | 62 | | 38 | 44 | | 39 | 6 | | 40 | 5 | | 41 | 34 | | 42 | 10 | | 43 | 21 | | 44 | 1 | | 45 | 3 | | 46 | 18 |
| |
| 60.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 11 | | totalSentences | 87 | | matches | | 0 | "was cropped" | | 1 | "been sealed" | | 2 | "been ruled" | | 3 | "was covered" | | 4 | "been photographed" | | 5 | "were covered" | | 6 | "were painted" | | 7 | "were scuffed" | | 8 | "was bunched" | | 9 | "been held" | | 10 | "been reported" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "was looking" | | 1 | "was spinning" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 111 | | ratio | 0.054 | | matches | | 0 | "A bone — carved, polished, the size of a domino." | | 1 | "The heels of his shoes had left faint drag marks in the dust — three feet, maybe less, from the edge of the circles to the centre." | | 2 | "Not body-warm — warmer, like it had been held in a fist for a long time." | | 3 | "It pointed not at the body, not at the stalls, but at the far end of the platform — a bricked-up archway where the tunnel curved into darkness." | | 4 | "The bricks were old, crusted with decades of grime, but the mortar at the edges was fresh — pale grey, not the dark crumble of the surrounding wall." | | 5 | "The chalk circles surrounded him — not a tag, not a warning." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 933 | | adjectiveStacks | 1 | | stackExamples | | 0 | "body-warm — warmer, like" |
| | adverbCount | 12 | | adverbRatio | 0.012861736334405145 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 12 | | std | 9.22 | | cv | 0.768 | | sampleLengths | | 0 | 10 | | 1 | 33 | | 2 | 26 | | 3 | 19 | | 4 | 10 | | 5 | 5 | | 6 | 28 | | 7 | 21 | | 8 | 20 | | 9 | 14 | | 10 | 7 | | 11 | 22 | | 12 | 5 | | 13 | 22 | | 14 | 9 | | 15 | 6 | | 16 | 8 | | 17 | 2 | | 18 | 4 | | 19 | 31 | | 20 | 12 | | 21 | 10 | | 22 | 4 | | 23 | 10 | | 24 | 19 | | 25 | 6 | | 26 | 15 | | 27 | 7 | | 28 | 11 | | 29 | 7 | | 30 | 4 | | 31 | 12 | | 32 | 15 | | 33 | 6 | | 34 | 3 | | 35 | 7 | | 36 | 15 | | 37 | 7 | | 38 | 2 | | 39 | 23 | | 40 | 8 | | 41 | 6 | | 42 | 12 | | 43 | 8 | | 44 | 19 | | 45 | 9 | | 46 | 28 | | 47 | 13 | | 48 | 7 | | 49 | 27 |
| |
| 44.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.36036036036036034 | | totalSentences | 111 | | uniqueOpeners | 40 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | 0 | "Then Morris had died, and" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 76 | | matches | | 0 | "She was tall for a" | | 1 | "Her hair was cropped close," | | 2 | "He was a big man" | | 3 | "he said without looking up" | | 4 | "She pried it loose." | | 5 | "She'd seen bones like this" | | 6 | "She'd seen drawings of them" | | 7 | "They were concentric circles, interlocking" | | 8 | "She'd seen these shapes before" | | 9 | "She stood and walked the" | | 10 | "His jacket was bunched under" | | 11 | "It was warm." | | 12 | "She checked the dead man's" | | 13 | "He looked at the chalk" | | 14 | "She'd bought it from a" | | 15 | "She'd thought it was a" | | 16 | "She watched it quiver, then" | | 17 | "It pointed not at the" | | 18 | "She ran her glove along" | | 19 | "She looked back at the" |
| | ratio | 0.289 | |
| 32.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 76 | | matches | | 0 | "The air down here tasted" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "She was tall for a" | | 3 | "Her hair was cropped close," | | 4 | "Camden's abandoned Tube station had" | | 5 | "The seals had been cut." | | 6 | "The chain-link fence at the" | | 7 | "DS Mercer stood at the" | | 8 | "He was a big man" | | 9 | "he said without looking up" | | 10 | "Quinn crouched beside the body." | | 11 | "The man lay on his" | | 12 | "The needle lay six inches" | | 13 | "The spoon beside it was" | | 14 | "Quinn pulled on a glove" | | 15 | "The fingers were stiff, curled" | | 16 | "She pried it loose." | | 17 | "A bone — carved, polished," | | 18 | "A hole drilled through one" | | 19 | "She'd seen bones like this" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 43.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 5 | | matches | | 0 | "He was a big man with a small notebook, the kind who wrote down everything and understood none of it." | | 1 | "The man lay on his side, knees drawn up, one arm stretched toward the stall's corrugated shutter as if reaching for something." | | 2 | "They were concentric circles, interlocking arcs, symbols that repeated with obsessive precision." | | 3 | "A small brass thing, the casing green with verdigris, the face etched with symbols that matched the chalk on the floor." | | 4 | "She'd bought it from a man in Whitechapel who'd told her it would find what she was looking for, as long as she was looking for the right thing." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 33 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |