| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1512 | | 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) | |
| 80.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1512 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "silk" | | 1 | "unspoken" | | 2 | "shattered" | | 3 | "reminder" | | 4 | "traced" | | 5 | "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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1512 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 48.95% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1138 | | uniqueNames | 18 | | maxNameDensity | 2.02 | | worstName | "Sian" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Blackwood | 1 | | Carter | 1 | | Golden | 1 | | Empress | 1 | | Rory | 17 | | Sian | 23 | | Vaughan | 2 | | Cardiff | 1 | | Taf | 1 | | Silas | 6 | | Swansea | 1 | | City | 1 | | Eva | 1 | | London | 1 | | Manchester | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Blackwood" | | 3 | "Carter" | | 4 | "Rory" | | 5 | "Sian" | | 6 | "Taf" | | 7 | "Silas" | | 8 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "Cardiff" | | 3 | "Swansea" | | 4 | "London" | | 5 | "Manchester" |
| | globalScore | 0.489 | | windowScore | 0.5 | |
| 73.08% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite dead" | | 1 | "seemed longer" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1512 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 123 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 20.43 | | std | 20.24 | | cv | 0.99 | | sampleLengths | | 0 | 101 | | 1 | 58 | | 2 | 62 | | 3 | 2 | | 4 | 60 | | 5 | 1 | | 6 | 33 | | 7 | 13 | | 8 | 6 | | 9 | 4 | | 10 | 36 | | 11 | 7 | | 12 | 30 | | 13 | 4 | | 14 | 6 | | 15 | 30 | | 16 | 39 | | 17 | 2 | | 18 | 21 | | 19 | 49 | | 20 | 4 | | 21 | 3 | | 22 | 9 | | 23 | 9 | | 24 | 9 | | 25 | 28 | | 26 | 16 | | 27 | 10 | | 28 | 27 | | 29 | 7 | | 30 | 10 | | 31 | 7 | | 32 | 3 | | 33 | 25 | | 34 | 9 | | 35 | 5 | | 36 | 10 | | 37 | 4 | | 38 | 4 | | 39 | 52 | | 40 | 31 | | 41 | 17 | | 42 | 16 | | 43 | 12 | | 44 | 11 | | 45 | 57 | | 46 | 41 | | 47 | 15 | | 48 | 7 | | 49 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 198 | | matches | | |
| 96.40% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 123 | | ratio | 0.016 | | matches | | 0 | "Foam marked her lip; she wiped it with the back of her hand, a leftover gesture from cheaper nights." | | 1 | "Some things the conversation could carry; others it only circled." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1143 | | adjectiveStacks | 1 | | stackExamples | | 0 | "safer present tense: hotels" |
| | adverbCount | 33 | | adverbRatio | 0.028871391076115485 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005249343832020997 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 12.29 | | std | 9.56 | | cv | 0.777 | | sampleLengths | | 0 | 19 | | 1 | 13 | | 2 | 22 | | 3 | 19 | | 4 | 10 | | 5 | 18 | | 6 | 23 | | 7 | 6 | | 8 | 16 | | 9 | 13 | | 10 | 3 | | 11 | 20 | | 12 | 18 | | 13 | 13 | | 14 | 8 | | 15 | 2 | | 16 | 4 | | 17 | 32 | | 18 | 24 | | 19 | 1 | | 20 | 4 | | 21 | 23 | | 22 | 6 | | 23 | 13 | | 24 | 6 | | 25 | 4 | | 26 | 2 | | 27 | 3 | | 28 | 6 | | 29 | 25 | | 30 | 7 | | 31 | 30 | | 32 | 4 | | 33 | 6 | | 34 | 30 | | 35 | 33 | | 36 | 6 | | 37 | 2 | | 38 | 2 | | 39 | 19 | | 40 | 49 | | 41 | 4 | | 42 | 3 | | 43 | 9 | | 44 | 9 | | 45 | 9 | | 46 | 28 | | 47 | 16 | | 48 | 10 | | 49 | 12 |
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| 49.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3089430894308943 | | totalSentences | 123 | | uniqueOpeners | 38 | |
| 37.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 89 | | matches | | 0 | "Only the shared motion from" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 89 | | matches | | 0 | "His left knee made him" | | 1 | "Her shoulder-length black hair hung" | | 2 | "She had not touched the" | | 3 | "She wore a dark wool" | | 4 | "Her hair sat in a" | | 5 | "Her heels struck the boards" | | 6 | "She stopped beside the empty" | | 7 | "He set them down and" | | 8 | "She offered nothing more." | | 9 | "She made them for deliveries," | | 10 | "They spoke of safer present" | | 11 | "They had located each other" | | 12 | "It was not." | | 13 | "They sat on." |
| | ratio | 0.157 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 89 | | matches | | 0 | "Rain streaked the windows of" | | 1 | "Maps of cities that no" | | 2 | "Silas Blackwood stood behind the" | | 3 | "The silver signet ring caught" | | 4 | "His left knee made him" | | 5 | "Aurora Carter occupied the stool" | | 6 | "Her shoulder-length black hair hung" | | 7 | "The crescent scar on her" | | 8 | "She had not touched the" | | 9 | "The door opened." | | 10 | "She wore a dark wool" | | 11 | "Her hair sat in a" | | 12 | "Rory lifted her head." | | 13 | "The woman in the doorway" | | 14 | "This Sian carried new lines" | | 15 | "Sian crossed the room." | | 16 | "Her heels struck the boards" | | 17 | "She stopped beside the empty" | | 18 | "Rory moved her glass an" | | 19 | "The stool complained." |
| | ratio | 0.933 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 6 | | matches | | 0 | "Black-and-white photographs filled the gaps: men on bridges, women on platforms, all of them caught in weather that had passed decades ago." | | 1 | "The photographs watched as they had watched other conversations that circled the same unspoken country." | | 2 | "Fragments surfaced: the lecturer who slept through his own class, the night they missed the last bus and walked five miles singing off-key, the certainty that t…" | | 3 | "Sian mentioned her parents in Swansea, proud of the City job and the marriage that looked complete on paper." | | 4 | "They had located each other in this unexpected corner, and the locating itself carried every year that had stood between." | | 5 | "Small proofs that time had moved for everyone, yet the centre remained: they had been young together, and now they were not, and the not was a country they had …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |