| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 75.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 813 | | totalAiIsmAdverbs | 4 | | 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) | |
| 81.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 813 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulse" | | 1 | "echo" | | 2 | "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 | 62 | | matches | (empty) | |
| 73.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 813 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 679 | | uniqueNames | 14 | | maxNameDensity | 0.74 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Quinn | 5 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Herrera | 4 | | General | 1 | | Medical | 1 | | Council | 1 | | Raven | 1 | | Nest | 1 | | Hackney | 1 | | Dan | 2 | | Morris | 3 | | Tube | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Council" | | 3 | "Raven" | | 4 | "Dan" | | 5 | "Morris" |
| | places | | 0 | "Chalk" | | 1 | "Farm" | | 2 | "Road" | | 3 | "Hackney" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 813 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 29.04 | | std | 25.6 | | cv | 0.882 | | sampleLengths | | 0 | 56 | | 1 | 72 | | 2 | 12 | | 3 | 100 | | 4 | 57 | | 5 | 5 | | 6 | 82 | | 7 | 20 | | 8 | 15 | | 9 | 6 | | 10 | 29 | | 11 | 6 | | 12 | 27 | | 13 | 27 | | 14 | 4 | | 15 | 37 | | 16 | 59 | | 17 | 14 | | 18 | 45 | | 19 | 7 | | 20 | 16 | | 21 | 4 | | 22 | 5 | | 23 | 29 | | 24 | 17 | | 25 | 15 | | 26 | 41 | | 27 | 6 |
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| 82.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 62 | | matches | | 0 | "been open" | | 1 | "been painted" | | 2 | "were furred" | | 3 | "been closed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 114 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 681 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02643171806167401 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0014684287812041115 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 10.84 | | std | 8.47 | | cv | 0.781 | | sampleLengths | | 0 | 23 | | 1 | 3 | | 2 | 30 | | 3 | 2 | | 4 | 30 | | 5 | 4 | | 6 | 8 | | 7 | 28 | | 8 | 6 | | 9 | 6 | | 10 | 20 | | 11 | 13 | | 12 | 4 | | 13 | 36 | | 14 | 9 | | 15 | 10 | | 16 | 8 | | 17 | 14 | | 18 | 4 | | 19 | 18 | | 20 | 21 | | 21 | 5 | | 22 | 9 | | 23 | 20 | | 24 | 8 | | 25 | 19 | | 26 | 12 | | 27 | 14 | | 28 | 14 | | 29 | 6 | | 30 | 15 | | 31 | 6 | | 32 | 2 | | 33 | 6 | | 34 | 21 | | 35 | 5 | | 36 | 1 | | 37 | 10 | | 38 | 14 | | 39 | 3 | | 40 | 8 | | 41 | 19 | | 42 | 4 | | 43 | 27 | | 44 | 10 | | 45 | 4 | | 46 | 12 | | 47 | 3 | | 48 | 4 | | 49 | 3 |
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| 76.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5 | | totalSentences | 74 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 55 | | matches | | 0 | "Then at the dark below." | | 1 | "Then at her watch." | | 2 | "Then his voice, quieter." | | 3 | "Somewhere far below, a train" |
| | ratio | 0.073 | |
| 89.09% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 55 | | matches | | 0 | "She didn't care." | | 1 | "She had watched him leave" | | 2 | "She knew that was a" | | 3 | "She had made a decision" | | 4 | "He cut left under the" | | 5 | "Its hands read 00:14." | | 6 | "Her partner had walked in" | | 7 | "She had never believed a" | | 8 | "She came under the arch" | | 9 | "She drew her warrant card," | | 10 | "Her echo came back to" | | 11 | "He had stopped on the" | | 12 | "She could just make out" | | 13 | "His breath was ragged, the" | | 14 | "She took one step down" | | 15 | "She looked at the bone" | | 16 | "She heard him exhale, a" | | 17 | "She took the first step" |
| | ratio | 0.327 | |
| 96.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 55 | | matches | | 0 | "The rain had been falling" | | 1 | "She didn't care." | | 2 | "The man ahead of her" | | 3 | "Stitches in the dark." | | 4 | "Wounds that should have killed" | | 5 | "She had watched him leave" | | 6 | "She knew that was a" | | 7 | "She had made a decision" | | 8 | "He cut left under the" | | 9 | "Quinn's left wrist ached where" | | 10 | "Its hands read 00:14." | | 11 | "Her partner had walked in" | | 12 | "The file said he had" | | 13 | "She had never believed a" | | 14 | "She came under the arch" | | 15 | "The space was empty." | | 16 | "The lettering had been painted" | | 17 | "Herrera was already halfway down." | | 18 | "Quinn reached the top of" | | 19 | "The stairs went down into" |
| | ratio | 0.727 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 37.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 4 | | matches | | 0 | "Twenty-nine years old, a paramedic until the General Medical Council had taken his license, and now, according to two informants who had not survived the tellin…" | | 1 | "Ahead, a green neon sign buzzed over a stairwell that should not have been open at this hour." | | 2 | "The stairs went down into darkness that smelled of wet stone, old iron, and something sweeter underneath, like burning herbs." | | 3 | "Procedure was the only thing that had ever kept her from becoming the kind of detective who ended up in a stairwell with blood on her gloves." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |