| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn knelt again [again]" | | 1 | "Fenn said slowly [slowly]" |
| | dialogueSentences | 55 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.091 | | effectiveRatio | 0.073 | |
| 86.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1491 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "precisely" | | 1 | "perfectly" | | 2 | "slowly" |
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| 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) | |
| 66.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1491 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "traced" | | 2 | "comfortable" | | 3 | "pulse" | | 4 | "perfect" | | 5 | "measured" | | 6 | "restrained" | | 7 | "silence" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1497 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 988 | | uniqueNames | 14 | | maxNameDensity | 1.32 | | worstName | "Fenn" | | maxWindowNameDensity | 3 | | worstWindowName | "Fenn" | | discoveredNames | | Quinn | 10 | | Camden | 2 | | Metropolitan | 1 | | Police | 1 | | Alistair | 1 | | Fenn | 13 | | Twenty-nine | 1 | | Level | 1 | | Rigor | 1 | | Bermondsey | 1 | | Morris | 1 | | Home | 1 | | Office | 1 | | Belgravia | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Alistair" | | 3 | "Fenn" | | 4 | "Twenty-nine" | | 5 | "Rigor" | | 6 | "Morris" | | 7 | "Office" | | 8 | "Belgravia" |
| | places | | | globalScore | 0.842 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | 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 | 1497 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 126 | | matches | | 0 | "let that sit" | | 1 | "leaving that circle" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 27.22 | | std | 23.42 | | cv | 0.86 | | sampleLengths | | 0 | 50 | | 1 | 31 | | 2 | 44 | | 3 | 9 | | 4 | 61 | | 5 | 21 | | 6 | 43 | | 7 | 11 | | 8 | 10 | | 9 | 29 | | 10 | 45 | | 11 | 3 | | 12 | 10 | | 13 | 36 | | 14 | 11 | | 15 | 14 | | 16 | 7 | | 17 | 33 | | 18 | 5 | | 19 | 83 | | 20 | 10 | | 21 | 43 | | 22 | 10 | | 23 | 68 | | 24 | 30 | | 25 | 11 | | 26 | 41 | | 27 | 18 | | 28 | 8 | | 29 | 16 | | 30 | 51 | | 31 | 64 | | 32 | 16 | | 33 | 49 | | 34 | 26 | | 35 | 89 | | 36 | 7 | | 37 | 1 | | 38 | 25 | | 39 | 1 | | 40 | 1 | | 41 | 24 | | 42 | 73 | | 43 | 5 | | 44 | 16 | | 45 | 9 | | 46 | 1 | | 47 | 90 | | 48 | 12 | | 49 | 32 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 93 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 52.15% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 126 | | ratio | 0.032 | | matches | | 0 | "Not recently — the grime had crept back in a fine grey film — but the old cream tiles along the eastern wall showed the ghost-marks of a hard clean, streaks where a brush had swept in long arcs." | | 1 | "The nails were clean — surgically clean, the cuticles pushed back, no crescent of dirt anywhere." | | 2 | "The rails were long gone, sold for scrap; the sleepers had rotted into ridges of black pulp." | | 3 | "Then, as her pupils opened, the ghost-rectangles on the wall came up out of the black — faint, cold, greenish, the residue of something that had hung there and soaked into the glaze." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 990 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.02727272727272727 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.00909090909090909 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 11.88 | | std | 10.64 | | cv | 0.896 | | sampleLengths | | 0 | 19 | | 1 | 31 | | 2 | 1 | | 3 | 15 | | 4 | 15 | | 5 | 18 | | 6 | 8 | | 7 | 18 | | 8 | 3 | | 9 | 6 | | 10 | 5 | | 11 | 39 | | 12 | 4 | | 13 | 1 | | 14 | 12 | | 15 | 5 | | 16 | 16 | | 17 | 4 | | 18 | 20 | | 19 | 12 | | 20 | 4 | | 21 | 3 | | 22 | 2 | | 23 | 3 | | 24 | 6 | | 25 | 3 | | 26 | 7 | | 27 | 9 | | 28 | 15 | | 29 | 5 | | 30 | 8 | | 31 | 37 | | 32 | 3 | | 33 | 5 | | 34 | 5 | | 35 | 10 | | 36 | 4 | | 37 | 15 | | 38 | 7 | | 39 | 11 | | 40 | 12 | | 41 | 2 | | 42 | 4 | | 43 | 3 | | 44 | 28 | | 45 | 5 | | 46 | 5 | | 47 | 36 | | 48 | 47 | | 49 | 10 |
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| 83.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5396825396825397 | | totalSentences | 126 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 78 | | matches | | 0 | "Then the ankles, easing the" | | 1 | "Then at the dust between" | | 2 | "Then, slowly, at the fifteen" | | 3 | "Then, as her pupils opened," | | 4 | "Somewhere down the tunnel, water" | | 5 | "Then Fenn switched his torch" |
| | ratio | 0.077 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 78 | | matches | | 0 | "She ducked under the strip" | | 1 | "She was looking at the" | | 2 | "She followed his light." | | 3 | "His coat was good wool," | | 4 | "Her knees complained." | | 5 | "She pushed the sleeve back" | | 6 | "She let that sit" | | 7 | "She moved to the man's" | | 8 | "She traced the air above" | | 9 | "She lifted the right hand" | | 10 | "She let the hand down" | | 11 | "She measured the band against" | | 12 | "She checked the other wrist." | | 13 | "She stayed there a long" | | 14 | "She had written *unexplained* in" | | 15 | "She had never stopped smelling" | | 16 | "She stood and walked to" | | 17 | "He killed his beam." | | 18 | "She reached over and snapped" | | 19 | "She heard Fenn's breath catch." |
| | ratio | 0.256 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 78 | | matches | | 0 | "The service tunnel smelled of" | | 1 | "She ducked under the strip" | | 2 | "The Metropolitan Police did not" | | 3 | "Quinn didn't answer." | | 4 | "She was looking at the" | | 5 | "Someone had scrubbed this platform." | | 6 | "Dozens of them, in rows," | | 7 | "She followed his light." | | 8 | "The dead man lay on" | | 9 | "His coat was good wool," | | 10 | "Both shoes were on." | | 11 | "Both laces tied." | | 12 | "Her knees complained." | | 13 | "She pushed the sleeve back" | | 14 | "Puncture marks, old ones, a" | | 15 | "Fenn went on" | | 16 | "Fenn's mouth opened, then closed." | | 17 | "Quinn stood and swept her" | | 18 | "A dropped syringe would have" | | 19 | "She let that sit" |
| | ratio | 0.667 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "The service tunnel smelled of wet chalk and something older, something that made the back of Quinn's throat itch." | | 1 | "Then, as her pupils opened, the ghost-rectangles on the wall came up out of the black — faint, cold, greenish, the residue of something that had hung there and …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.164 | | leniency | 0.327 | | rawRatio | 0 | | effectiveRatio | 0 | |