Run: 2026-03-11-01KKFH7ANQC556QVEPTF58FMKD

Run Details

Duration:
1m 54s
Cost:
$0.010210
Provider:
Seed

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
Rain slanted into Harlow Quinn’s face, hard as thrown gravel, turning London’s streets into a slip of black asphalt and warped neon reflection. She ran, her boots slapping through puddles that swallowed the sound of her stride, the green neon glow of the Raven’s Nest fading behind her. The bar’s secret back exit had spilled her into the alley an hour earlier, after she’d flashed her badge at the hooded kid slipping out with a canvas bag strapped to his chest. He’d run before she could cuff him, and she’d given chase, her military-honed stride eating up the distance between them even as he twisted through Soho’s back alleys. Salt-and-pepper hair, cropped tight to her skull, stuck to the curve of her sharp jaw. The worn leather watch on her left wrist dug into her palm as she clenched her fist, the cracked strap a holdover from her army days, the only thing she’d refused to replace in 20 years. For three weeks she’d staked out the Raven’s Nest, the dim Soho bar plastered with old maps and frayed black-and-white photographs, the front for a clique she’d linked to six missing people. All six had last been spotted within a block of its green neon sign. All six had left no trace except a single polished animal bone, discarded like trash, carved with a raven’s head. She’d found the first in Morris’s pocket, three years ago, when they’d fished his body out of the Thames. The kid darted past a line of idling black cabs, and Harlow broke into a sprint, dodging a group of drunk tourists who spilled out of a Camden pub, their shouts swallowed by the rain. Camden’s high street bled into a stretch of boarded-up warehouses, the streetlights spaced far enough apart to swallow whole blocks in shadow. She knew this stretch. She’d stood here once before, three years prior, the same night Morris had died. The kid skidded to a stop 30 yards ahead of her, in front of a bricked-up entrance to an abandoned Tube station, the same entrance Morris had slipped into the night he died. Harlow ducked behind a overflowing dumpster, her breath coming in short, sharp bursts, and watched the kid yank three rotting pine boards loose from the entrance’s frame. The wood crashed into a puddle, and he fumbled in his jacket pocket, pulling out a small, smooth bone. He held it up to the dark mouth of the station, hesitated, then slipped inside. A second bone clattered out of his pocket as he went, rolling across the asphalt to stop at Harlow’s boots. She stepped out from behind the dumpster, her service weapon drawn and steady in her right hand. She bent to pick up the bone, and its warmth seeped into her palm even through the rain’s chill. It was carved with the same raven’s head she’d found in Morris’s pocket. The entry token for the Veil Market, the underground supernatural black market she’d spent three years digging up information on. It moved locations every full moon, and the clouds above parted for half a second, revealing the full moon hanging fat and silver over Camden’s rooflines. Tonight was the night. The Market was here. The kid hadn’t run from her by accident. He’d led her straight to it. A shadow stepped out from behind the remaining boards of the Tube entrance, and Harlow snapped her gun up, training it on the man’s chest. He kept his hands raised, level with his shoulders, his short curly dark hair plastered to his head. The sleeve of his rainjacket slid down his left forearm as he adjusted his grip, revealing a pale, raised scar running the length of the skin. A Saint Christopher medallion peeked out from the collar of his shirt, glinting in the faint glow of a streetlamp a block away. Tomás Herrera, the former NHS paramedic who’d lost his license three years prior for treating unauthorized patients, the man she’d seen slipping in and out of the Raven’s Nest a dozen times in her three weeks of stakeouts. You don’t want to go in there. Harlow’s finger tightened on the trigger of her gun. The leather watch on her wrist clicked as she shifted her weight, her boots splashing in a puddle that pooled around her ankles. Who are you. Tomás lowered his hands an inch, his warm brown eyes fixed on the bone token in her left palm. That only works if you’re invited. The wards tear apart anyone who walks in there without a claim. You’ll be dead before you hit the first turnstile. The kid who just ran in. He’s part of the clique that runs the Raven’s Nest. I have questions for him. Tomás snorted, wiping rain off his jaw with the back of his hand. That kid’s a runner. He doesn’t know nothing beyond how to drop off product and pick up payments. You want questions, you talk to Silas, and Silas doesn’t talk to cops. Especially not you. He’s known you were staking out his bar since day one. That kid wasn’t running from you. he was luring you. It’s a trap. The same trap they set for Morris. Harlow’s grip on her gun slackened, just a fraction. No one but her had ever linked Morris’s death to the clique. The department had written his death off as a drug overdose, ruled he’d fallen into the Thames while high. She’d never told anyone what she’d seen that night, the shadow with teeth that had dragged Morris into the dark of this same Tube entrance, a shadow no one else had been able to see. I was the first paramedic on scene when they pulled Morris out of the water. Tomás took a step forward, his medallion swinging against his chest. I saw the marks on his neck. I saw his eyes, solid black, no irises, no pupils. The coroner lied. The department lied. He didn’t die from drowning. A shade killed him. The same kind of shade the clique keeps locked in the Market to dispose of people who get too close. I lost my license because I treated a dozen people who escaped those shades, people the NHS would have locked up as delusional rather than admit what they’d seen was real. I moved to London from Seville to help people, not turn them away because their wounds came from something that doesn’t exist in a textbook. Faint sound drifted up from the Tube entrance, the hum of voices, the clink of glass, the low thrum of a guitar. The Veil Market was open, bustling, right beneath her feet. She heard the kid’s laugh echo up the tunnel, sharp and knowing, waiting for her to step over the threshold. Silas had been waiting for her. He’d known she’d come, that her grief over Morris would drive her straight into the trap he’d laid. Harlow shifted her weight, the bone token warm in her palm, the gun still heavy in her hand. She could turn around. She could call it in, say she’d lost the suspect, go back to her empty flat, drink the bad whiskey she kept under her sink, and pretend she hadn’t come this close to the answers she’d spent three years chasing. Or she could step into the dark, into the place that had killed her partner, into the market full of enchanted goods and alchemical substances and things she could never explain, and chase the kid who had led her there. The rain picked up again, slamming into her coat, and she stared into the black mouth of the Tube station, weighing her choice, the weight of Morris’s memory pressing down on her shoulders.
created_at"2026-03-11T22:45:34.022Z" 3/11/2026, 10:45:34 PM
model"bytedance-seed/seed-2.0-lite-20260309"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency603
moderation_latency(null)
generation_time114190
tokens_prompt1140
tokens_completion5389
native_tokens_prompt1104
native_tokens_completion4967
native_tokens_completion_images(null)
native_tokens_reasoning3342
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.01021
router(null)
provider_responses
0
endpoint_id"bf1a3473-7a0a-46d3-aaa5-adb8f876a697"
id"021773269134139378c141953f92ec627da9d675fe03be0c1f3d8"
is_byokfalse
latency603
model_permaslug"bytedance-seed/seed-2.0-lite-20260309"
provider_name"Seed"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.0; linux; x64))"
http_referer(null)
api_type"completions"
id"gen-1773269134-uKNwF57hxAhCjnXl4WJv"
upstream_id"021773269134139378c141953f92ec627da9d675fe03be0c1f3d8"
total_cost0.01021
cache_discount(null)
upstream_inference_cost0
provider_name"Seed"

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)
wordCount1288
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)
72.83% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1288
totalAiIsms7
found
0
word"warmth"
count1
1
word"chill"
count1
2
word"glinting"
count1
3
word"weight"
count3
4
word"echo"
count1
highlights
0"warmth"
1"chill"
2"glinting"
3"weight"
4"echo"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"clenched jaw/fists"
count1
highlights
0"clenched her fist"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences79
matches(empty)
88.61% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences79
filterMatches
0"watch"
hedgeMatches
0"try to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences79
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1288
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions61
wordCount1288
uniqueNames19
maxNameDensity0.78
worstName"Morris"
maxWindowNameDensity2
worstWindowName"Morris"
discoveredNames
Harlow8
Quinn1
London2
Raven4
Nest4
Soho2
Morris10
Thames2
Camden3
Tube5
Veil2
Market4
Saint1
Christopher1
Herrera1
Silas3
Seville1
Tomás4
You3
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Morris"
4"Camden"
5"Saint"
6"Christopher"
7"Herrera"
8"Silas"
9"Tomás"
10"You"
places
0"London"
1"Soho"
2"Thames"
3"Tube"
4"Market"
5"Seville"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences65
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.776
wordCount1288
matches
0"No one but"
82.28% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences79
matches
0"seen that night"
1"come, that her"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs16
mean80.5
std45.56
cv0.566
sampleLengths
0109
1136
275
3114
4117
5130
67
732
83
946
1021
1178
1275
13134
1476
15135
96.38% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences79
matches
0"been spotted"
1"was carved"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs206
matches
0"were staking"
1"wasn’t running"
2"was luring"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences79
ratio0
matches(empty)
82.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1295
adjectiveStacks3
stackExamples
0"single polished animal bone,"
1"short curly dark hair"
2"open, bustling, right beneath her"
adverbCount22
adverbRatio0.01698841698841699
lyAdverbCount4
lyAdverbRatio0.003088803088803089
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences79
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences79
mean16.3
std10.34
cv0.634
sampleLengths
023
125
233
328
415
536
632
714
820
919
1035
1122
124
1314
1433
1527
1619
1715
1820
1917
2019
2113
2220
2326
244
254
268
276
2825
2918
3026
3123
3238
337
349
3523
363
3719
386
3912
409
416
4210
435
4413
454
4614
4713
483
4911
39.87% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.27848101265822783
totalSentences79
uniqueOpeners22
42.19% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences79
matches
0"Especially not you."
ratio0.013
57.97% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount32
totalSentences79
matches
0"She ran, her boots slapping"
1"He’d run before she could"
2"She’d found the first in"
3"She knew this stretch."
4"She’d stood here once before,"
5"He held it up to"
6"She stepped out from behind"
7"She bent to pick up"
8"It was carved with the"
9"It moved locations every full"
10"He’d led her straight to"
11"He kept his hands raised,"
12"You don’t want to go"
13"You’ll be dead before you"
14"He’s part of the clique"
15"I have questions for him."
16"He doesn’t know nothing beyond"
17"You want questions, you talk"
18"He’s known you were staking"
19"he was luring you."
ratio0.405
10.63% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount71
totalSentences79
matches
0"Rain slanted into Harlow Quinn’s"
1"She ran, her boots slapping"
2"The bar’s secret back exit"
3"He’d run before she could"
4"Salt-and-pepper hair, cropped tight to"
5"The worn leather watch on"
6"She’d found the first in"
7"The kid darted past a"
8"Camden’s high street bled into"
9"She knew this stretch."
10"She’d stood here once before,"
11"The kid skidded to a"
12"Harlow ducked behind a overflowing"
13"The wood crashed into a"
14"He held it up to"
15"A second bone clattered out"
16"She stepped out from behind"
17"She bent to pick up"
18"It was carved with the"
19"The entry token for the"
ratio0.899
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences79
matches(empty)
ratio0
35.04% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences53
technicalSentenceCount8
matches
0"She ran, her boots slapping through puddles that swallowed the sound of her stride, the green neon glow of the Raven’s Nest fading behind her."
1"The kid darted past a line of idling black cabs, and Harlow broke into a sprint, dodging a group of drunk tourists who spilled out of a Camden pub, their shouts…"
2"Harlow ducked behind a overflowing dumpster, her breath coming in short, sharp bursts, and watched the kid yank three rotting pine boards loose from the entranc…"
3"The sleeve of his rainjacket slid down his left forearm as he adjusted his grip, revealing a pale, raised scar running the length of the skin."
4"The leather watch on her wrist clicked as she shifted her weight, her boots splashing in a puddle that pooled around her ankles."
5"He’s part of the clique that runs the Raven’s Nest."
6"Or she could step into the dark, into the place that had killed her partner, into the market full of enchanted goods and alchemical substances and things she co…"
7"The rain picked up again, slamming into her coat, and she stared into the black mouth of the Tube station, weighing her choice, the weight of Morris’s memory pr…"
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
83.5936%