| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.476 | | leniency | 0.952 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1682 | | totalAiIsmAdverbs | 2 | | 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) | |
| 88.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1682 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "velvet" | | 1 | "flicked" | | 2 | "profound" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 109 | | matches | (empty) | |
| 77.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 119 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 23 | | totalWords | 1700 | | ratio | 0.014 | | matches | | 0 | "rats" | | 1 | "were" | | 2 | "last Tuesday, who'll give me last Tuesday" | | 3 | "Morris's voice. It had said her name in Morris's voice." | | 4 | "Now." | | 5 | "notices" | | 6 | "Run." | | 7 | "parted" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 1 | | matches | | 0 | "Performance, she told herself." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1526 | | uniqueNames | 17 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Vauxhall | 2 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Dean | 1 | | Street | 2 | | Quinn | 15 | | Camden | 1 | | High | 1 | | Underground | 1 | | Tuesday | 2 | | Morris | 4 | | Saint | 1 | | Christopher | 1 | | Seville | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Herrera" | | 3 | "Quinn" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Vauxhall" | | 2 | "Dean" | | 3 | "Street" | | 4 | "Camden" | | 5 | "High" | | 6 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | 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 | 1700 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 119 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 38.64 | | std | 31.73 | | cv | 0.821 | | sampleLengths | | 0 | 85 | | 1 | 32 | | 2 | 34 | | 3 | 2 | | 4 | 39 | | 5 | 6 | | 6 | 92 | | 7 | 17 | | 8 | 89 | | 9 | 18 | | 10 | 82 | | 11 | 19 | | 12 | 66 | | 13 | 17 | | 14 | 15 | | 15 | 11 | | 16 | 14 | | 17 | 54 | | 18 | 19 | | 19 | 73 | | 20 | 26 | | 21 | 3 | | 22 | 73 | | 23 | 134 | | 24 | 10 | | 25 | 12 | | 26 | 69 | | 27 | 4 | | 28 | 75 | | 29 | 6 | | 30 | 28 | | 31 | 25 | | 32 | 15 | | 33 | 82 | | 34 | 4 | | 35 | 57 | | 36 | 21 | | 37 | 17 | | 38 | 87 | | 39 | 39 | | 40 | 48 | | 41 | 24 | | 42 | 45 | | 43 | 12 |
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| 98.83% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 109 | | matches | | 0 | "being slapped" | | 1 | "was gone" |
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| 94.18% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 252 | | matches | | 0 | "was seeing" | | 1 | "was selling" | | 2 | "was auctioning" | | 3 | "was looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 16 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 119 | | ratio | 0.084 | | matches | | 0 | "She saw him read her the way soldiers read ground — the coat, the stance, the warrant card half-raised in her hand — and then his warm brown eyes went flat and hard, and he ran." | | 1 | "A pursuit, no backup, no log — her DI would have her badge on a plate." | | 2 | "White tiles, old and greened with damp, curving down into the earth — a disused Underground entrance, the roundel above it painted out, the ghost of a name still legible if you knew how to look at old things." | | 3 | "Then he tilted his head, and something moved behind his eyes, and he looked at her — really looked, the way you look at a crack in a wall — for three long seconds." | | 4 | "The sounds reached her before the light did — a murmur like a church service, like a trading floor, like nothing she'd ever heard — and then the stairwell opened onto a platform, and Quinn stopped with her hand on the wall and understood, for the first time in her life, that she had no frame of reference for what she was seeing." | | 5 | "Stalls lined the platform edge where the tracks should have been — the tracks *were* there, she saw, but furred over with something dark and soft that might have been moss and might have been fur." | | 6 | "He was at a stall near the far tunnel, the canvas bag open, handing something over — a vial, dark glass — and the Saint Christopher medallion swung at his throat as he leaned, and his sleeve rode up and she saw the scar along his left forearm, pale against olive skin." | | 7 | "\"This place is the Veil Market, and it is not for you, and every second you stand here it *notices* you more.\" He closed his hand over hers where it gripped his arm — gently, like a medic calming a patient — and leaned in." | | 8 | "The crowd parted for her — that was the worst part, the way they *parted*, amused, interested, a man reaching out almost idly as if to touch her sleeve and being slapped away by the silver-haired woman — and then her shoes hit the stairs and she took them three at a time, up and up through brick and tile into the cold." | | 9 | "Then she felt it — something in her coat pocket that hadn't been there before." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1523 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.023637557452396585 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004596191726854892 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 119 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 119 | | mean | 14.29 | | std | 14.98 | | cv | 1.048 | | sampleLengths | | 0 | 17 | | 1 | 27 | | 2 | 11 | | 3 | 30 | | 4 | 2 | | 5 | 1 | | 6 | 15 | | 7 | 6 | | 8 | 3 | | 9 | 5 | | 10 | 23 | | 11 | 11 | | 12 | 2 | | 13 | 2 | | 14 | 1 | | 15 | 36 | | 16 | 6 | | 17 | 3 | | 18 | 12 | | 19 | 32 | | 20 | 9 | | 21 | 8 | | 22 | 28 | | 23 | 17 | | 24 | 19 | | 25 | 5 | | 26 | 14 | | 27 | 16 | | 28 | 35 | | 29 | 8 | | 30 | 10 | | 31 | 41 | | 32 | 2 | | 33 | 39 | | 34 | 6 | | 35 | 2 | | 36 | 11 | | 37 | 10 | | 38 | 5 | | 39 | 1 | | 40 | 50 | | 41 | 17 | | 42 | 7 | | 43 | 8 | | 44 | 5 | | 45 | 6 | | 46 | 8 | | 47 | 6 | | 48 | 10 | | 49 | 34 |
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| 69.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.46218487394957986 | | totalSentences | 119 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 89 | | matches | | 0 | "Then he tilted his head," | | 1 | "Then she saw Herrera." | | 2 | "Then she felt it —" |
| | ratio | 0.034 | |
| 85.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 89 | | matches | | 0 | "She'd been sitting there since" | | 1 | "She saw him read her" | | 2 | "He was fast." | | 3 | "He cut down an alley" | | 4 | "He burst out onto the" | | 5 | "She didn't call it in." | | 6 | "He went down an alley" | | 7 | "His eyes were the colour" | | 8 | "His voice had gravel in" | | 9 | "She held up the warrant" | | 10 | "He said it the way" | | 11 | "He reached out, unhurried, and" | | 12 | "She went down." | | 13 | "It had said her name" | | 14 | "She looked at her watch." | | 15 | "She said it to herself" | | 16 | "He was at a stall" | | 17 | "He went white." | | 18 | "His eyes flicked past her," | | 19 | "His voice was low, urgent," |
| | ratio | 0.337 | |
| 49.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 89 | | matches | | 0 | "The rain had been falling" | | 1 | "Quinn sat in the unmarked" | | 2 | "She'd been sitting there since" | | 3 | "The clique's medic, her file" | | 4 | "The soft target." | | 5 | "The one who might talk." | | 6 | "The door opened under the" | | 7 | "Quinn was out of the" | | 8 | "She saw him read her" | | 9 | "Quinn went after him" | | 10 | "He was fast." | | 11 | "He cut down an alley" | | 12 | "He burst out onto the" | | 13 | "Quinn made the Vauxhall in" | | 14 | "The cab led her north" | | 15 | "She didn't call it in." | | 16 | "That was the first wrong" | | 17 | "A pursuit, no backup, no" | | 18 | "The cab dropped him on" | | 19 | "Quinn dumped the car on" |
| | ratio | 0.82 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 18.63% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 8 | | matches | | 0 | "He cut down an alley off Dean Street, kicked through a drift of cardboard, and Quinn followed with the rain needling her close-cropped hair and her shoes skatin…" | | 1 | "He went down an alley between a shuttered chicken shop and a pawnbroker's, and she followed him into the dark beyond the streetlights, past bins and a sleeping …" | | 2 | "Quinn stopped at the arch's mouth, breathing hard, rain running down the sharp line of her jaw." | | 3 | "Someone was auctioning a bottle, and the auctioneer kept saying *last Tuesday, who'll give me last Tuesday*, and the crowd laughed, and Quinn's radio hissed in …" | | 4 | "She said it to herself the way you repeat a prayer, and it held for exactly as long as it took a stall-keeper ten feet away to turn his head and look at her wit…" | | 5 | "It spread, stall by stall, lanterns dimming in a slow wave from the far tunnel, until the only light left nearby was the jar-light at the silver-haired woman's …" | | 6 | "The crowd parted for her — that was the worst part, the way they *parted*, amused, interested, a man reaching out almost idly as if to touch her sleeve and bein…" | | 7 | "Then she felt it — something in her coat pocket that hadn't been there before." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.19 | | leniency | 0.381 | | rawRatio | 0 | | effectiveRatio | 0 | |