| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow said slowly [slowly]" |
| | dialogueSentences | 55 | | tagDensity | 0.218 | | leniency | 0.436 | | rawRatio | 0.083 | | effectiveRatio | 0.036 | |
| 91.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1216 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | |
| | 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) | |
| 87.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1216 | | totalAiIsms | 3 | | 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 | 70 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 70 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1216 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 671 | | uniqueNames | 11 | | maxNameDensity | 0.89 | | worstName | "Achebe" | | maxWindowNameDensity | 2 | | worstWindowName | "Achebe" | | discoveredNames | | Achebe | 6 | | Harlow | 5 | | Quinn | 1 | | South | 2 | | Morden | 1 | | Tube | 1 | | Morris | 1 | | Chalk | 1 | | Thirty-one | 1 | | Dr | 1 | | Osei | 3 |
| | persons | | 0 | "Achebe" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Chalk" | | 5 | "Thirty-one" | | 6 | "Dr" | | 7 | "Osei" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | 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 | 1216 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 22.11 | | std | 19.19 | | cv | 0.868 | | sampleLengths | | 0 | 21 | | 1 | 40 | | 2 | 9 | | 3 | 33 | | 4 | 3 | | 5 | 4 | | 6 | 65 | | 7 | 38 | | 8 | 3 | | 9 | 19 | | 10 | 6 | | 11 | 7 | | 12 | 31 | | 13 | 8 | | 14 | 47 | | 15 | 16 | | 16 | 63 | | 17 | 1 | | 18 | 43 | | 19 | 2 | | 20 | 42 | | 21 | 61 | | 22 | 13 | | 23 | 5 | | 24 | 5 | | 25 | 47 | | 26 | 41 | | 27 | 2 | | 28 | 16 | | 29 | 6 | | 30 | 11 | | 31 | 66 | | 32 | 5 | | 33 | 18 | | 34 | 23 | | 35 | 15 | | 36 | 1 | | 37 | 32 | | 38 | 3 | | 39 | 21 | | 40 | 26 | | 41 | 30 | | 42 | 3 | | 43 | 14 | | 44 | 27 | | 45 | 55 | | 46 | 2 | | 47 | 3 | | 48 | 18 | | 49 | 40 |
| |
| 85.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 70 | | matches | | 0 | "been swept" | | 1 | "been pried" | | 2 | "been poured" | | 3 | "were carved" | | 4 | "been broken" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 115 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 675 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.03851851851851852 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01037037037037037 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 10.86 | | std | 9.93 | | cv | 0.915 | | sampleLengths | | 0 | 14 | | 1 | 7 | | 2 | 26 | | 3 | 9 | | 4 | 5 | | 5 | 9 | | 6 | 33 | | 7 | 3 | | 8 | 4 | | 9 | 12 | | 10 | 24 | | 11 | 1 | | 12 | 4 | | 13 | 16 | | 14 | 8 | | 15 | 26 | | 16 | 5 | | 17 | 4 | | 18 | 3 | | 19 | 3 | | 20 | 19 | | 21 | 2 | | 22 | 4 | | 23 | 4 | | 24 | 3 | | 25 | 31 | | 26 | 8 | | 27 | 11 | | 28 | 21 | | 29 | 1 | | 30 | 14 | | 31 | 5 | | 32 | 11 | | 33 | 15 | | 34 | 15 | | 35 | 33 | | 36 | 1 | | 37 | 2 | | 38 | 30 | | 39 | 11 | | 40 | 2 | | 41 | 17 | | 42 | 11 | | 43 | 6 | | 44 | 1 | | 45 | 7 | | 46 | 13 | | 47 | 48 | | 48 | 13 | | 49 | 5 |
| |
| 89.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5714285714285714 | | totalSentences | 112 | | uniqueOpeners | 64 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 59 | | matches | | 0 | "Somewhere below, a generator throbbed." | | 1 | "Then she crouched and studied" |
| | ratio | 0.034 | |
| 97.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 59 | | matches | | 0 | "She'd smelled it once before," | | 1 | "She pushed the memory down" | | 2 | "She swept her torch across" | | 3 | "She stood and played the" | | 4 | "She moved along the platform" | | 5 | "She walked the tunnel." | | 6 | "Her torch found the wall's" | | 7 | "They were carved into brick" | | 8 | "She counted to five." | | 9 | "It came out level" | | 10 | "She photographed the words with" | | 11 | "She bagged a tile shard" | | 12 | "He came down the tunnel" | | 13 | "He saw her face and" | | 14 | "He crouched by the compass" | | 15 | "She looked back down the" | | 16 | "They returned at a pace" | | 17 | "She straightened, and her eyes" |
| | ratio | 0.305 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 59 | | matches | | 0 | "DS Achebe held the tape" | | 1 | "Harlow Quinn ducked under the" | | 2 | "Torchlight bounced off tiled walls" | | 3 | "The tunnel opened like a" | | 4 | "Harlow followed the generator noise" | | 5 | "She'd smelled it once before," | | 6 | "She pushed the memory down" | | 7 | "The dead man lay in" | | 8 | "Achebe crouched beside her." | | 9 | "She swept her torch across" | | 10 | "The dust lay undisturbed except" | | 11 | "Achebe exhaled through his teeth." | | 12 | "She stood and played the" | | 13 | "Chalk marks ringed the platform" | | 14 | "She moved along the platform" | | 15 | "The missing tile's edges were" | | 16 | "The pathologist, Dr Osei, appeared" | | 17 | "She walked the tunnel." | | 18 | "Her torch found the wall's" | | 19 | "They were carved into brick" |
| | ratio | 0.695 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "Harlow Quinn ducked under the police cordon and descended the dead escalator into South Morden South, a Tube station that had closed before she was born." | | 1 | "Harlow followed the generator noise and the crime scene lighting into a chamber that smelled of rust, wet chalk, and something else underneath it." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.036 | | leniency | 0.073 | | rawRatio | 0 | | effectiveRatio | 0 | |