| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "Nayar said slowly [slowly]" | | 1 | "Quinn turned slowly [slowly]" |
| | dialogueSentences | 45 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.111 | | effectiveRatio | 0.089 | |
| 92.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1331 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 96.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1331 | | totalAiIsms | 1 | | 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 | 66 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 66 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1336 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 840 | | uniqueNames | 14 | | maxNameDensity | 1.55 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Nayar" | | discoveredNames | | Transport | 1 | | London | 1 | | Priya | 1 | | Nayar | 11 | | July | 1 | | Quinn | 13 | | Tube | 1 | | Bermondsey | 1 | | Alan | 1 | | Morris | 1 | | Friday | 1 | | Monday | 1 | | Chief | 1 | | Super | 1 |
| | persons | | 0 | "Nayar" | | 1 | "Quinn" | | 2 | "Alan" | | 3 | "Morris" | | 4 | "Super" |
| | places | | 0 | "Transport" | | 1 | "London" | | 2 | "Bermondsey" |
| | globalScore | 0.726 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1336 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 27.27 | | std | 23.45 | | cv | 0.86 | | sampleLengths | | 0 | 59 | | 1 | 7 | | 2 | 50 | | 3 | 4 | | 4 | 78 | | 5 | 6 | | 6 | 29 | | 7 | 4 | | 8 | 4 | | 9 | 3 | | 10 | 51 | | 11 | 5 | | 12 | 24 | | 13 | 21 | | 14 | 41 | | 15 | 6 | | 16 | 70 | | 17 | 28 | | 18 | 12 | | 19 | 15 | | 20 | 22 | | 21 | 33 | | 22 | 4 | | 23 | 36 | | 24 | 8 | | 25 | 53 | | 26 | 48 | | 27 | 12 | | 28 | 24 | | 29 | 9 | | 30 | 41 | | 31 | 60 | | 32 | 7 | | 33 | 73 | | 34 | 17 | | 35 | 16 | | 36 | 57 | | 37 | 86 | | 38 | 1 | | 39 | 15 | | 40 | 37 | | 41 | 2 | | 42 | 31 | | 43 | 3 | | 44 | 8 | | 45 | 43 | | 46 | 60 | | 47 | 10 | | 48 | 3 |
| |
| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 66 | | matches | | 0 | "been screwed" | | 1 | "been disturbed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 19.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 93 | | ratio | 0.043 | | matches | | 0 | "The platform, when they reached it, was wrong the way a lie is wrong — plausible in every particular and false as a whole." | | 1 | "Around him the platform floor wore sixty years of grit — except in a circle, roughly two metres across, where the floor was as clean as if someone had taken a wet cloth to it." | | 2 | "Sigils cut into the face in a script she didn't recognise — angular, deliberate, not decorative." | | 3 | "A line, drawn with a fingertip, running the full length of the platform at exactly the same height — and under it, at intervals of about a metre, small heaps of something white and granular that the photographer's flash had washed out entirely." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 838 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.026252983293556086 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010739856801909307 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 14.37 | | std | 11.51 | | cv | 0.801 | | sampleLengths | | 0 | 29 | | 1 | 13 | | 2 | 17 | | 3 | 7 | | 4 | 25 | | 5 | 25 | | 6 | 4 | | 7 | 19 | | 8 | 27 | | 9 | 12 | | 10 | 20 | | 11 | 6 | | 12 | 23 | | 13 | 6 | | 14 | 4 | | 15 | 4 | | 16 | 3 | | 17 | 51 | | 18 | 3 | | 19 | 2 | | 20 | 24 | | 21 | 21 | | 22 | 7 | | 23 | 34 | | 24 | 6 | | 25 | 24 | | 26 | 18 | | 27 | 8 | | 28 | 20 | | 29 | 17 | | 30 | 8 | | 31 | 3 | | 32 | 12 | | 33 | 7 | | 34 | 8 | | 35 | 18 | | 36 | 4 | | 37 | 5 | | 38 | 2 | | 39 | 26 | | 40 | 4 | | 41 | 29 | | 42 | 7 | | 43 | 8 | | 44 | 18 | | 45 | 35 | | 46 | 6 | | 47 | 42 | | 48 | 12 | | 49 | 24 |
| |
| 84.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5483870967741935 | | totalSentences | 93 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 57 | | matches | | 0 | "Her torch found rails long" | | 1 | "She moved around the body," | | 2 | "She pulled a glove tighter" | | 3 | "It was a slim black" | | 4 | "It swung to the tunnel" | | 5 | "It tracked the tunnel mouth" | | 6 | "She set the compass on" | | 7 | "It turned itself on the" | | 8 | "She went back to the" | | 9 | "She teased it free." | | 10 | "Her thumb went still on" | | 11 | "She had asked about it" | | 12 | "She had stopped asking on" | | 13 | "Her voice came out level" |
| | ratio | 0.246 | |
| 47.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 57 | | matches | | 0 | "The hoarding at the top" | | 1 | "Quinn crouched and put her" | | 2 | "Paint sat in the grooves," | | 3 | "DS Priya Nayar had her" | | 4 | "The shaft breathed cold air" | | 5 | "Quinn went down the ladder" | | 6 | "Her torch found rails long" | | 7 | "Dust lay on everything, fine" | | 8 | "Nayar ducked a hanging festoon" | | 9 | "Quinn stopped walking." | | 10 | "Nayar's torch swung ahead" | | 11 | "Quinn started walking again" | | 12 | "The platform, when they reached" | | 13 | "A tarpaulin lay folded against" | | 14 | "The body sat with its" | | 15 | "Quinn crouched a metre away" | | 16 | "Quinn tilted her head" | | 17 | "The man's fingertips were black." | | 18 | "The nails had gone the" | | 19 | "Nayar said slowly" |
| | ratio | 0.825 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "Portable lamps stood on tripods, throwing hard white light onto tiled walls that had never seen a passenger." | | 1 | "Two uniformed constables stood at the tunnel mouth with the specific stillness of men who wanted to be somewhere else." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 45 | | tagDensity | 0.178 | | leniency | 0.356 | | rawRatio | 0.125 | | effectiveRatio | 0.044 | |