Run: 2026-07-25-01KYCYNPQW7ZR73QDTCJTD3YDN

Run Details

Duration:
7.5s
Cost:
$0.003498
Provider:
Google AI Studio

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 rain in Soho did not merely fall; it hammered the asphalt, bouncing inches into the air before sweeping into the gutters in greasy, oily streams. Detective Harlow Quinn adjusted the collar of her trench coat, her sharp jaw set, her breath pluming white in the chill autumn air. Eighteen years on the force had taught her many things, but nothing beat the visceral ache of a late-night foot chase in the dead of a Tuesday. Ahead of her, cutting through the pedestrian drizzle with the practiced agility of a man half his age, was the runner. Tomás Herrera. The former paramedic turned back-alley healer was moving fast, his olive skin slicked with rain, a heavy coat flapping behind him like a dark wing. Quinn knew his file inside and out: Seville born, struck from the NHS register after a botched, highly irregular procedure on a patient who supposedly didn't exist, and currently suspected of patching up the very clique Quinn had been hunting for six brutal months. Quinn checked her left wrist, her fingers brushing the worn leather band of her watch. The hands glowed faintly in the downpour. Three minutes past midnight. She lengthened her stride, her military precision keeping her movements efficient despite the slick footing. She didn’t draw her sidearm yet. She wanted answers. She wanted to know what Herrera knew about DS Morris, about the cold, impossible way her partner had burned out from the inside three years ago. Herrera hooked hard to the right, splashing through a wide puddle that reflected the sickly yellow discharge of sodium streetlamps. He vaulted a low iron railing, his boots skidding briefly on wet stone before he caught his balance. Quinn followed, her palms slapping the cold iron to vault the barrier with barely a break in her rhythm. Her breath burned her lungs, raw and metallic, but she closed the gap. Ten yards. Five. She reached out, her gloved fingers grazing the heavy fabric of Herrera's coat as he cut down a narrow alleyway wedged between two derelict warehouses. Hey! Police! Stop right there, Herrera! she shouted, her voice cutting through the hiss of the rain. The runner didn’t look back. Instead, he ducked behind a row of metal commercial bins, scrambling toward the rusted corrugated metal doors of an old lift shaft. Quinn rounded the bins just in time to see Herrera hauling open a heavy, grease-streaked door set deep into the pavement. It revealed a steep, plunging flight of concrete stairs descending into the absolute dark of an abandoned subterranean tunnel. A wave of dead air washed up from the opening, smelling of stale damp, copper, and something sharp and metallic, like scorched wire. Quinn reached the threshold and caught the edge of the steel door before it swung shut. She flicked on her heavy-duty flashlight, sending a searing beam down the stairwell. The light caught the wet, glistening concrete steps spiraling down into the earth, swallowed instantly by the shadows below. Herrera was already halfway down, the rubber soles of his shoes slapping rhythmically against the stone. Quinn stopped at the edge, one foot on the first step. Her stomach tightened into a hard, cold knot. Every instinct honed over two decades of police work screamed at her to pull back. This was outside her jurisdiction. This was deep underground, away from backup, away from radios that worked. She knew what lived in the dark corners of the city—she had seen the autopsy reports of Morris’s case, reports that defied biology and physics alike. Down there, the rules of the Metropolitan Police didn't apply. She flashed her light down again. Painted on the grimy concrete wall near the landing was a crude symbol scratched into the mortar: a crescent moon crossed by a jagged line. The Veil Market. The whispers of the street had named it. A black market operating out of forgotten transit arteries, trading in things that police officers weren't supposed to know existed. To enter required a token, a pass, something she didn't possess. If she went down there without backup, chasing a man who knew every twist and turn of the subterranean warrens, she was walking into a trap. Or worse, a graveyard where no one would ever find her badge. Herrera reached the bottom of the stairs, pausing in the pool of dim amber light cast by an illicitly wired bulb overhead. He looked back up, his warm brown eyes catching the beam of Quinn's flashlight. For a second, she saw the scar running stark and pale along his left forearm, catching the light as he shifted his weight. Beneath his jacket, the glint of a silver Saint Christopher medallion caught her eye. He didn't look triumphant. He looked exhausted, terrified, and entirely desperate. You can't follow me down here, Detective, Herrera’s voice echoed up the stairwell, hollow and strained. You don't know what you're stepping into. Try me, Herrera, Quinn called back, her voice ringing with hard authority. You're harboring criminals. You're tied to the death of a police officer. You aren't going anywhere. You think Morris died of a heart attack? Herrera shouted bitterly, his voice bouncing off the concrete walls. You think it was natural? If you follow me through this door, Quinn, you won't find an arrest. You'll find out why you've been chasing ghosts. He turned on his heel and bolted down the intersecting tunnel, vanishing into the pitch-black maw of the abandoned station. Quinn stood frozen on the threshold. The wind howled above street level, driving the freezing rain against the back of her neck. Her thumb rubbed absently over the worn leather of her watch. Morris's face flashed behind her eyelids—the way he had looked before the end, pale and hollowed out by secrets he refused to share. She could turn back now. Call for a containment perimeter, wait for the tactical units, play it safe. Let the trail go cold in the labyrinth of the underground. Instead, Harlow Quinn drew her service weapon, clicked off the safety, and stepped down into the dark.
created_at"2026-07-25T15:35:33.401Z" 7/25/2026, 3:35:33 PM
model"google/gemini-3.5-flash-lite-20260721"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency310
moderation_latency(null)
generation_time7445
tokens_prompt888
tokens_completion1525
native_tokens_prompt852
native_tokens_completion1297
native_tokens_completion_images0
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"STOP"
service_tier"default"
usage0.0034981
router(null)
provider_responses
0
endpoint_id"6bd8f433-79e1-416c-b407-1772eb796c9a"
id"xddkas_sHbeN_PUPltyv4AE"
is_byokfalse
latency310
model_permaslug"google/gemini-3.5-flash-lite-20260721"
provider_name"Google AI Studio"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784993733-0oMU26L4sTZjCMi3LcVl"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784993733-J4BpQqkOGrleFYuynd4H"
upstream_id"xddkas_sHbeN_PUPltyv4AE"
total_cost0.0034981
cache_discount(null)
upstream_inference_cost0
provider_name"Google AI Studio"
response_cache_source_id(null)
data_region"global"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
95.07% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1014
totalAiIsmAdverbs1
found
0
adverb"very"
count1
highlights
0"very"
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)
45.76% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1014
totalAiIsms11
found
0
word"chill"
count1
1
word"efficient"
count1
2
word"flicked"
count1
3
word"searing"
count1
4
word"glistening"
count1
5
word"stomach"
count1
6
word"stark"
count1
7
word"weight"
count1
8
word"glint"
count1
9
word"echoed"
count1
10
word"maw"
count1
highlights
0"chill"
1"efficient"
2"flicked"
3"searing"
4"glistening"
5"stomach"
6"stark"
7"weight"
8"glint"
9"echoed"
10"maw"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences75
matches(empty)
66.67% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount4
hedgeCount0
narrationSentences75
filterMatches
0"watch"
1"think"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences75
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen44
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1012
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions1
matches
0"Try me, Herrera, Quinn called back, her voice ringing with hard authority."
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions50
wordCount1012
uniqueNames15
maxNameDensity1.28
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"You"
discoveredNames
Soho1
Harlow2
Quinn13
Tuesday1
Herrera11
Seville1
Morris4
Metropolitan1
Police2
Veil1
Market1
Saint1
Christopher1
Detective2
You8
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Morris"
4"Police"
5"Saint"
6"Christopher"
7"You"
places
0"Soho"
1"Seville"
globalScore0.858
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences59
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1012
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences75
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs24
mean42.17
std24.15
cv0.573
sampleLengths
076
192
226
350
473
525
617
767
823
948
1016
1119
1268
1334
1477
1573
1611
1723
1828
1944
2020
2156
2229
2317
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences75
matches
0"tied"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs165
matches
0"was walking"
28.57% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount1
flaggedSentences3
totalSentences75
ratio0.04
matches
0"The rain in Soho did not merely fall; it hammered the asphalt, bouncing inches into the air before sweeping into the gutters in greasy, oily streams."
1"She knew what lived in the dark corners of the city—she had seen the autopsy reports of Morris’s case, reports that defied biology and physics alike."
2"Morris's face flashed behind her eyelids—the way he had looked before the end, pale and hollowed out by secrets he refused to share."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1019
adjectiveStacks1
stackExamples
0"heavy, grease-streaked door"
adverbCount27
adverbRatio0.02649656526005888
lyAdverbCount16
lyAdverbRatio0.015701668302257114
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences75
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences75
mean13.49
std8.37
cv0.62
sampleLengths
026
123
227
321
42
525
644
715
87
94
1015
116
123
1326
1420
1518
1619
1713
182
191
2025
211
221
234
2411
255
2622
2721
2819
2923
3016
3113
3219
3316
3411
358
3615
375
3812
3926
4010
416
4225
433
448
4520
4611
4726
4812
4922
58.67% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats11
diversityRatio0.44
totalSentences75
uniqueOpeners33
95.24% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences70
matches
0"Instead, he ducked behind a"
1"Instead, Harlow Quinn drew her"
ratio0.029
65.71% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences70
matches
0"She lengthened her stride, her"
1"She didn’t draw her sidearm"
2"She wanted answers."
3"She wanted to know what"
4"He vaulted a low iron"
5"Her breath burned her lungs,"
6"She reached out, her gloved"
7"she shouted, her voice cutting"
8"It revealed a steep, plunging"
9"She flicked on her heavy-duty"
10"Her stomach tightened into a"
11"She knew what lived in"
12"She flashed her light down"
13"He looked back up, his"
14"He didn't look triumphant."
15"He looked exhausted, terrified, and"
16"You can't follow me down"
17"You don't know what you're"
18"You're harboring criminals."
19"You're tied to the death"
ratio0.386
81.43% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount53
totalSentences70
matches
0"The rain in Soho did"
1"Detective Harlow Quinn adjusted the"
2"The former paramedic turned back-alley"
3"Quinn knew his file inside"
4"Quinn checked her left wrist,"
5"The hands glowed faintly in"
6"She lengthened her stride, her"
7"She didn’t draw her sidearm"
8"She wanted answers."
9"She wanted to know what"
10"Herrera hooked hard to the"
11"He vaulted a low iron"
12"Quinn followed, her palms slapping"
13"Her breath burned her lungs,"
14"She reached out, her gloved"
15"she shouted, her voice cutting"
16"The runner didn’t look back."
17"Quinn rounded the bins just"
18"It revealed a steep, plunging"
19"A wave of dead air"
ratio0.757
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences70
matches
0"To enter required a token,"
1"If she went down there"
2"If you follow me through"
ratio0.043
98.21% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences48
technicalSentenceCount3
matches
0"Herrera hooked hard to the right, splashing through a wide puddle that reflected the sickly yellow discharge of sodium streetlamps."
1"He vaulted a low iron railing, his boots skidding briefly on wet stone before he caught his balance."
2"A black market operating out of forgotten transit arteries, trading in things that police officers weren't supposed to know existed."
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
88.7554%