| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1118 | | 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) | |
| 82.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1118 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "electric" | | 1 | "variable" | | 2 | "stomach" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 10 | | totalWords | 1118 | | ratio | 0.009 | | matches | | 0 | "Don't" | | 1 | "You lose him here and you lose him forever" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1043 | | uniqueNames | 21 | | maxNameDensity | 0.67 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Inverness | 1 | | Street | 3 | | Arlington | 1 | | Road | 1 | | Camden | 1 | | High | 1 | | Sainsbury | 1 | | Electric | 1 | | Ballroom | 1 | | Buck | 1 | | Met | 1 | | Underground | 2 | | Jubilee | 1 | | Morris | 2 | | Christmas | 1 | | English | 1 | | Hove | 1 | | Glock | 2 | | Greek | 1 | | Arabic | 1 | | Quinn | 7 |
| | persons | | 0 | "Buck" | | 1 | "Morris" | | 2 | "Glock" | | 3 | "Quinn" |
| | places | | 0 | "Inverness" | | 1 | "Arlington" | | 2 | "Road" | | 3 | "Camden" | | 4 | "High" | | 5 | "Sainsbury" | | 6 | "Christmas" | | 7 | "Hove" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | 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 | 1118 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 109 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 22.36 | | std | 22.54 | | cv | 1.008 | | sampleLengths | | 0 | 24 | | 1 | 3 | | 2 | 57 | | 3 | 5 | | 4 | 6 | | 5 | 96 | | 6 | 13 | | 7 | 1 | | 8 | 29 | | 9 | 8 | | 10 | 13 | | 11 | 2 | | 12 | 5 | | 13 | 3 | | 14 | 6 | | 15 | 66 | | 16 | 11 | | 17 | 13 | | 18 | 3 | | 19 | 45 | | 20 | 13 | | 21 | 51 | | 22 | 12 | | 23 | 8 | | 24 | 69 | | 25 | 26 | | 26 | 7 | | 27 | 21 | | 28 | 26 | | 29 | 41 | | 30 | 5 | | 31 | 11 | | 32 | 4 | | 33 | 2 | | 34 | 16 | | 35 | 38 | | 36 | 9 | | 37 | 68 | | 38 | 55 | | 39 | 15 | | 40 | 54 | | 41 | 4 | | 42 | 19 | | 43 | 4 | | 44 | 25 | | 45 | 3 | | 46 | 50 | | 47 | 41 | | 48 | 7 | | 49 | 5 |
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| 90.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 95 | | matches | | 0 | "been shut" | | 1 | "was meant" | | 2 | "were tiled" | | 3 | "were gone" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 168 | | matches | | 0 | "was working" | | 1 | "was hanging" | | 2 | "was running" | | 3 | "wasn't looking" | | 4 | "wasn't looking" | | 5 | "was looking" | | 6 | "was holding" | | 7 | "wasn't coming" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1046 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.020076481835564052 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004780114722753346 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 10.26 | | std | 11.27 | | cv | 1.099 | | sampleLengths | | 0 | 24 | | 1 | 3 | | 2 | 24 | | 3 | 10 | | 4 | 3 | | 5 | 3 | | 6 | 17 | | 7 | 5 | | 8 | 3 | | 9 | 3 | | 10 | 36 | | 11 | 4 | | 12 | 5 | | 13 | 51 | | 14 | 10 | | 15 | 3 | | 16 | 1 | | 17 | 3 | | 18 | 16 | | 19 | 4 | | 20 | 1 | | 21 | 1 | | 22 | 4 | | 23 | 6 | | 24 | 2 | | 25 | 13 | | 26 | 2 | | 27 | 5 | | 28 | 3 | | 29 | 6 | | 30 | 19 | | 31 | 37 | | 32 | 6 | | 33 | 4 | | 34 | 11 | | 35 | 11 | | 36 | 2 | | 37 | 3 | | 38 | 31 | | 39 | 3 | | 40 | 11 | | 41 | 13 | | 42 | 4 | | 43 | 7 | | 44 | 40 | | 45 | 12 | | 46 | 8 | | 47 | 2 | | 48 | 42 | | 49 | 4 |
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| 63.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.44036697247706424 | | totalSentences | 109 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 22.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 81 | | matches | | 0 | "She didn't fall." | | 1 | "She never fell." | | 2 | "He didn't stop." | | 3 | "They never did." | | 4 | "She had a good look" | | 5 | "He'd come out of the" | | 6 | "She shouldered past." | | 7 | "She clocked the trainers." | | 8 | "He'd dressed for this." | | 9 | "Her radio hissed against her" | | 10 | "She killed the radio and" | | 11 | "He took a left by" | | 12 | "She cut the corner across" | | 13 | "He caught himself with one" | | 14 | "she lied, loud enough to" | | 15 | "He looked back." | | 16 | "They read calculation." | | 17 | "He was working out a" | | 18 | "He ducked right, down the" | | 19 | "She knew those steps." |
| | ratio | 0.494 | |
| 40.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 81 | | matches | | 0 | "The suspect clipped a market" | | 1 | "Quinn vaulted them." | | 2 | "The leather of her watch" | | 3 | "She didn't fall." | | 4 | "She never fell." | | 5 | "He didn't stop." | | 6 | "They never did." | | 7 | "She had a good look" | | 8 | "That was the thing." | | 9 | "That was what she wanted." | | 10 | "He'd come out of the" | | 11 | "Some lad outside the kebab" | | 12 | "She shouldered past." | | 13 | "The suspect was forty yards" | | 14 | "She clocked the trainers." | | 15 | "He'd dressed for this." | | 16 | "Her radio hissed against her" | | 17 | "She killed the radio and" | | 18 | "He took a left by" | | 19 | "She cut the corner across" |
| | ratio | 0.84 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "First time he'd done that, and she saw his face, properly saw it, pale in the rain, young, maybe twenty-five, with eyes that didn't read fear the way faces usua…" | | 1 | "Three years since Morris had gone down a set of steps like these, chasing a girl in a red coat, and come back up in a body bag with no external injuries and a c…" | | 2 | "The part that had put Morris in the ground and kept him there." | | 3 | "A corridor stretched away from her that shouldn't have stretched that far, because she knew the geography above, and this corridor was running under the canal w…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 18 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0.5 | | effectiveRatio | 0.111 | |