Run: 2026-02-17-01KHMMV25Y20AWXCAXRHAE9GAT

Run Details

Duration:
2.5s
Cost:
$0.000062

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<codex> <character id="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</backstory> </character> <location id="854" name="The Raven's Nest" aliases="the Nest, Raven, Silas' bar" realm="Earth"> <signColor>distinctive green neon sign above the entrance</signColor> <secretRoom>hidden back room accessible through a bookshelf, used for clandestine meetings</secretRoom> <description>A dimly lit bar in Soho, London. The walls are covered with old maps and black-and-white photographs.</description> </location> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> </codex> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary.

Model response

AI
The neon glow of the Raven’s Nest flickered like a dying ember against the storm’s relentless assault, its green sign bleeding into the rain-slicked pavement. Detective Harlow Quinn’s breath came in shallow bursts, her leather watch—worn and timeworn—ticking against the damp fabric of her coat. The wind howled through the alleyways, carrying the scent of damp earth and something older, something metallic beneath the rain. She’d been chasing Tomás Herrera for hours. The man had vanished into the labyrinth of Soho after leaving the bar, his dark curls damp from the downpour, his olive skin flushed with something more than exertion. Quinn had followed him through the usual routes—back alleys, doorways, the flickering glow of streetlamps—but he’d slipped away like smoke. Until now. Now, he stood at the edge of the Tube station entrance, his back to her, his Saint Christopher medallion catching the dim light of a flickering sign. Quinn moved with military precision, her boots crunching on the wet pavement, her jaw set. She had no choice. Not when the chase had led her this far. The rain hammered against her face, stinging her eyes, but she didn’t slow. Herrera turned, his warm brown eyes locking onto hers for just a second before he vanished into the shadows. Quinn didn’t hesitate. She lunged, her coat flaring behind her as she dove into the chaos of the abandoned station. The air was thick with the scent of rust and something else—something sour, like old blood. The station had been sealed for years, but the damp had carved its way through the concrete, leaving the air heavy with the weight of forgotten things. Quinn’s pulse hammered in her throat, her fingers digging into the cold metal of the platform railing as she pushed forward. Herrera was ahead of her, moving with the quiet efficiency of a man who knew the station’s secrets. Quinn’s boots splashed through puddles, her breath fogging in the cold air as she followed. She could hear him—his quick, sure footsteps, the occasional rustle of his coat. She was gaining. Then, the sound of a door opening. Quinn froze. A low hum filled the air, like the distant roar of a machine. The station’s exit—a narrow door set into the wall, barely visible in the gloom—was ajar. Herrera stepped through first, his silhouette swallowed by the darkness beyond. Quinn didn’t wait. She lunged, her fingers closing around the cold metal of the door handle. The station groaned beneath her as she forced it open, the air outside sharp with the scent of damp stone and something else—something electric, like the hum of a circuit just about to spark. The Veil Market stretched before her, a labyrinth of stalls and shadows. The air was thick with the scent of smoke and something metallic, like old coins and blood. Quinn’s pulse roared in her ears, her breath coming in ragged gasps as she stepped into the chaos. Herrera was already moving, his dark curls damp and wild, his scar running along his left forearm catching the flickering light of a lantern. Quinn’s eyes locked onto him, her mind racing. She had no idea what lay ahead, no idea what dangers lurked in this underground world. But she had no choice. She followed.
total_cost0.0000618

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount552
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
27.54% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount552
totalAiIsms8
found
0
word"flickered"
count1
1
word"weight"
count1
2
word"pulse"
count2
3
word"footsteps"
count1
4
word"gloom"
count1
5
word"electric"
count1
6
word"racing"
count1
highlights
0"flickered"
1"weight"
2"pulse"
3"footsteps"
4"gloom"
5"electric"
6"racing"
66.67% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches2
maxInWindow2
found
0
label"air was thick with"
count2
highlights
0"The air was thick with"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences38
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences38
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences38
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen35
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords543
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
57.92% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions25
wordCount543
uniqueNames12
maxNameDensity1.84
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Raven1
Nest1
Harlow1
Quinn10
Tomás1
Herrera5
Soho1
Tube1
Saint1
Christopher1
Veil1
Market1
persons
0"Raven"
1"Nest"
2"Harlow"
3"Quinn"
4"Tomás"
5"Herrera"
6"Saint"
7"Christopher"
8"Market"
places
0"Soho"
globalScore0.579
windowScore0.833
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences30
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount543
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences38
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs14
mean38.79
std22.06
cv0.569
sampleLengths
065
17
251
355
452
564
649
77
841
950
1047
1148
125
132
96.03% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences38
matches
0"been sealed"
24.56% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs76
matches
0"was gaining"
1"was already moving"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount9
semicolonCount0
flaggedSentences6
totalSentences38
ratio0.158
matches
0"Detective Harlow Quinn’s breath came in shallow bursts, her leather watch—worn and timeworn—ticking against the damp fabric of her coat."
1"Quinn had followed him through the usual routes—back alleys, doorways, the flickering glow of streetlamps—but he’d slipped away like smoke."
2"The air was thick with the scent of rust and something else—something sour, like old blood."
3"She could hear him—his quick, sure footsteps, the occasional rustle of his coat."
4"The station’s exit—a narrow door set into the wall, barely visible in the gloom—was ajar."
5"The station groaned beneath her as she forced it open, the air outside sharp with the scent of damp stone and something else—something electric, like the hum of a circuit just about to spark."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount553
adjectiveStacks0
stackExamples(empty)
adverbCount9
adverbRatio0.0162748643761302
lyAdverbCount1
lyAdverbRatio0.0018083182640144665
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences38
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences38
mean14.29
std8.23
cv0.576
sampleLengths
025
120
220
37
429
520
62
727
815
94
109
1113
1219
133
1417
1516
1627
1721
1818
1915
2013
213
227
232
2413
2515
2611
273
2813
2934
3012
3117
3218
3324
348
3516
365
372
39.47% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.2894736842105263
totalSentences38
uniqueOpeners11
95.24% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences35
matches
0"Then, the sound of a"
ratio0.029
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount7
totalSentences35
matches
0"She’d been chasing Tomás Herrera"
1"She had no choice."
2"She lunged, her coat flaring"
3"She could hear him—his quick,"
4"She was gaining."
5"She lunged, her fingers closing"
6"She had no idea what"
ratio0.2
17.14% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount31
totalSentences35
matches
0"The neon glow of the"
1"Detective Harlow Quinn’s breath came"
2"The wind howled through the"
3"She’d been chasing Tomás Herrera"
4"The man had vanished into"
5"Quinn had followed him through"
6"Quinn moved with military precision,"
7"She had no choice."
8"The rain hammered against her"
9"Herrera turned, his warm brown"
10"Quinn didn’t hesitate."
11"She lunged, her coat flaring"
12"The air was thick with"
13"The station had been sealed"
14"Quinn’s pulse hammered in her"
15"Herrera was ahead of her,"
16"Quinn’s boots splashed through puddles,"
17"She could hear him—his quick,"
18"She was gaining."
19"A low hum filled the"
ratio0.886
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences35
matches
0"Now, he stood at the"
ratio0.029
87.91% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences26
technicalSentenceCount2
matches
0"Quinn’s pulse hammered in her throat, her fingers digging into the cold metal of the platform railing as she pushed forward."
1"Herrera was ahead of her, moving with the quiet efficiency of a man who knew the station’s secrets."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
83.7493%