| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 128 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1979 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 97.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1979 | | 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 | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 213 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1978 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 39 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 86 | | wordCount | 1073 | | uniqueNames | 16 | | maxNameDensity | 3.45 | | worstName | "Nia" | | maxWindowNameDensity | 7 | | worstWindowName | "Nia" | | discoveredNames | | Golden | 1 | | Empress | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Silas | 7 | | Aurora | 28 | | Nia | 37 | | Bristol | 1 | | Yu-Fei | 1 | | Cheung | 1 | | Rory | 1 | | Evan | 2 | | Mrs | 1 | | Powell | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Aurora" | | 4 | "Nia" | | 5 | "Yu-Fei" | | 6 | "Cheung" | | 7 | "Rory" | | 8 | "Evan" | | 9 | "Mrs" | | 10 | "Powell" |
| | places | | 0 | "London" | | 1 | "Bristol" | | 2 | "Cardiff" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared above a photograph of five people crowded around a restaurant table, their faces raised towards the camera" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.506 | | wordCount | 1978 | | matches | | 0 | "not the same as liking it, but it isn’t nothing" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 213 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 134 | | mean | 14.76 | | std | 16.06 | | cv | 1.088 | | sampleLengths | | 0 | 80 | | 1 | 24 | | 2 | 2 | | 3 | 14 | | 4 | 3 | | 5 | 4 | | 6 | 1 | | 7 | 65 | | 8 | 1 | | 9 | 20 | | 10 | 6 | | 11 | 6 | | 12 | 15 | | 13 | 59 | | 14 | 21 | | 15 | 15 | | 16 | 27 | | 17 | 41 | | 18 | 43 | | 19 | 36 | | 20 | 3 | | 21 | 22 | | 22 | 11 | | 23 | 2 | | 24 | 10 | | 25 | 8 | | 26 | 62 | | 27 | 39 | | 28 | 4 | | 29 | 22 | | 30 | 43 | | 31 | 4 | | 32 | 1 | | 33 | 1 | | 34 | 5 | | 35 | 14 | | 36 | 15 | | 37 | 14 | | 38 | 5 | | 39 | 7 | | 40 | 12 | | 41 | 11 | | 42 | 17 | | 43 | 4 | | 44 | 6 | | 45 | 33 | | 46 | 2 | | 47 | 33 | | 48 | 5 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 110 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 190 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 213 | | ratio | 0.009 | | matches | | 0 | "Two men argued over a crossword beneath an old map of the London docks; beyond them, black-and-white faces watched from Silas’s photographs with the patience of people who had nowhere left to be." | | 1 | "No—there wasn't one." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1080 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.028703703703703703 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000925925925925926 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 213 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 213 | | mean | 9.29 | | std | 7.63 | | cv | 0.821 | | sampleLengths | | 0 | 25 | | 1 | 12 | | 2 | 10 | | 3 | 33 | | 4 | 8 | | 5 | 16 | | 6 | 2 | | 7 | 4 | | 8 | 10 | | 9 | 3 | | 10 | 4 | | 11 | 1 | | 12 | 14 | | 13 | 20 | | 14 | 4 | | 15 | 27 | | 16 | 1 | | 17 | 6 | | 18 | 14 | | 19 | 6 | | 20 | 6 | | 21 | 9 | | 22 | 6 | | 23 | 11 | | 24 | 25 | | 25 | 11 | | 26 | 12 | | 27 | 21 | | 28 | 11 | | 29 | 4 | | 30 | 16 | | 31 | 11 | | 32 | 3 | | 33 | 15 | | 34 | 23 | | 35 | 8 | | 36 | 12 | | 37 | 23 | | 38 | 7 | | 39 | 29 | | 40 | 3 | | 41 | 14 | | 42 | 8 | | 43 | 11 | | 44 | 2 | | 45 | 10 | | 46 | 2 | | 47 | 6 | | 48 | 5 | | 49 | 3 |
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| 42.57% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.3004694835680751 | | totalSentences | 213 | | uniqueOpeners | 64 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 95 | | matches | | 0 | "Once, Rory would have filled" | | 1 | "Somewhere in the walls, a" | | 2 | "Then she folded it over" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 95 | | matches | | 0 | "His grey-streaked beard hid most" | | 1 | "Her suit jacket lay folded" | | 2 | "Her gaze travelled over Aurora’s" | | 3 | "He returned it with a" | | 4 | "They took the table beneath" | | 5 | "She rubbed at a watermark" | | 6 | "She lived in Bristol now." | | 7 | "She wrote letters people put" | | 8 | "She had a room upstairs" | | 9 | "She tried to read one" | | 10 | "She leaned back, away from" | | 11 | "She took a drink, grimaced" | | 12 | "She thought of Nia at" | | 13 | "Her phone lit on the" | | 14 | "She turned the phone face" | | 15 | "He filled it without asking" |
| | ratio | 0.168 | |
| 49.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 95 | | matches | | 0 | "Aurora came in out of" | | 1 | "The green neon above The" | | 2 | "Silas stood drying a glass" | | 3 | "His grey-streaked beard hid most" | | 4 | "Aurora freed the receipt." | | 5 | "The woman on the last" | | 6 | "Her suit jacket lay folded" | | 7 | "Rain jewelled her shoes." | | 8 | "Aurora knew the face, although" | | 9 | "Nia’s fingers tightened on the" | | 10 | "Aurora touched the straight black" | | 11 | "Nia laughed, a single unprepared" | | 12 | "Her gaze travelled over Aurora’s" | | 13 | "Nia nodded towards the bag" | | 14 | "Silas took the bag from" | | 15 | "Aurora introduced them." | | 16 | "Nia offered Silas the careful" | | 17 | "He returned it with a" | | 18 | "They took the table beneath" | | 19 | "Nia sat with her back" |
| | ratio | 0.821 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 95 | | matches | | 0 | "Now she looked capable of" | | 1 | "Now she turned her glass" |
| | ratio | 0.021 | |
| 76.41% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 4 | | matches | | 0 | "Two men argued over a crossword beneath an old map of the London docks; beyond them, black-and-white faces watched from Silas’s photographs with the patience of…" | | 1 | "His grey-streaked beard hid most of his expression, but not the part that concerned her punctuality." | | 2 | "Aurora thought of Mrs Powell’s kitchen: yellow cupboards, a radio with one missing knob, the blue-striped tea towel that had never been clean again." | | 3 | "She thought of Nia at nineteen, leaving Cardiff with her rucksack, and of herself waving from the pavement with Evan’s hand resting at the back of her neck." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 1 | | matches | | 0 | "Silas took, his signet ring knocking against the strap" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 128 | | tagDensity | 0.102 | | leniency | 0.203 | | rawRatio | 0.077 | | effectiveRatio | 0.016 | |