Run: 2026-09-03-01M1KC4MTK3M1HZD93WK7EQGSN

Run Details

Duration:
2m 39s
Cost:
$0.031124
Provider:
SiliconFlow

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 lashed the streets of Camden, turning the curb into a black mirror. Detective Harlow Quinn stood in a doorway long enough for the cold to settle into her knuckles. Across the road, Tomás Herrera moved through the yellow glow of a kebab shop sign. He wore a waxed jacket and a heavy canvas satchel, one hand resting on the strap. The Saint Christopher medallion at his throat caught the light each time a car passed. She had first seen that medallion six nights ago outside The Raven’s Nest in Soho, when he had ducked through a side door carrying a duffel that clinked like laboratory glass. Tonight, he was far from Soho, and she was far from her car. She followed him for another block, keeping close to the shuttered storefronts. The rain softened the sound of her boots. Her radio squawked against her hip, a wash of static. She thumbed it silent and kept her eyes on his shoulders. Herrera had the loose, patient stride of a man who believed no one could be behind him. That was his mistake. He turned into an alley beside a dead pub. The sign had lost half its letters. Harlow waited three seconds, then followed. The alley stank of wet cardboard and old grease. Bins lined the brick walls. A motion light snapped on above a fire door, and her shadow stretched ahead of her. Herrera glanced back. The light caught his face, and she saw the change in it—not surprise, not fear. Recognition. Then he ran. Harlow broke into a sprint. Her boots hit puddles and threw water against the walls. Herrera was fast, faster than a paramedic had any right to be. He swung around a stack of pallets and sent an empty bottle skittering into the dark. She vaulted the pallets, landed in a crouch, and pushed off harder. “Police!” Her voice bounced off the brick. “Herrera, stop.” He didn’t. The alley opened onto a lane lined with shuttered market stalls. Canvas awnings sagged under the rain. Herrera veered between two stalls and toppled a crate of rotted fruit. Harlow hurdled the crate, catching the edge with her shin. Pain flared and vanished. She pumped her arms. The gap closed to ten feet, then eight. She could hear his breathing now, ragged at the edges. He threw something over his shoulder. A small glass vial spun through the air and shattered at her feet. White smoke burst upward, thick and chemical, stinging her eyes. She turned her face away and held her breath. The smoke clawed at her throat. She ran through it blind for three paces, one hand grazing a stall post, then burst out the other side. Herrera had reached a chain-link gate at the end of the lane. A padlock hung open, cut clean through. He scrambled up, the satchel catching on the wire. She lunged and grabbed the strap. The canvas burned through her wet fingers. He twisted, kicked backward, and the strap tore free. He dropped to the other side and vanished down a concrete stairwell that bit into the earth. Harlow scaled the gate, dropped, and landed hard. The impact jarred up through her knees. She steadied herself and looked into the dark. The stairwell dropped steeply, the first steps slick with rain, then dry. A green light pulsed somewhere below, faint as a breathing thing. The air from below smelled of wet stone, hot iron, and something sweet that had no place underground. She heard Herrera’s footsteps fading, then stopping. A voice echoed up—not his. “Token.” Harlow pressed herself to the wall and edged down the first few steps. The green light grew brighter. The steps turned, revealing a rusted gate at the bottom. Herrera stood there, one hand inside his shirt. He pulled out a small pale object, curved like a rib, and held it up. A tall figure in a waxed cloak took it, ran a thumb over the bone, and stepped aside. The gate opened without a sound. Beyond it, lanterns hung from the curved ceiling of an abandoned tube platform. Stalls stretched into the dark, crowded with shapes and voices. A hand-painted sign on the wall read: *Veil Market. Token upon entry. No exceptions.* Herrera looked back up the stairs. His chest heaved. The medallion had twisted on its chain. “You followed me all this way.” His voice came hoarse. “You could still turn back.” The gatekeeper waited, one hand on the iron, watching her with eyes that reflected the green light like a cat’s. Harlow’s thumb rested against the slide of her sidearm. The rain above had dimmed to a whisper. Her radio gave one last burst of static and died. No backup. No signal. No one who knew where she had gone. Three years ago, Morris had written two words in the margin of his notebook. She had found them after the funeral. *Veil Market.* He had gone somewhere like this without her. Herrera stepped through the gate. The gatekeeper did not close it. Harlow looked over her shoulder once. The stairwell climbed back into blue-silver rain and orange city light. Then she faced the green lanterns, the open gate, and the murmur of the market below. She started down.
created_at"2026-09-03T10:13:26.542Z" 9/3/2026, 10:13:26 AM
model"deepseek/deepseek-v4-pro-20260813"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1007
moderation_latency(null)
generation_time159027
tokens_prompt1104
tokens_completion8279
native_tokens_prompt1082
native_tokens_completion7499
native_tokens_completion_images(null)
native_tokens_reasoning6358
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(null)
usage0.03112428
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788430406-yRcw9QGSNRO7shpsjdw5"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788430406-bnrArggAQNlDDtRqvq9e"
upstream_id"chatcmpl-baada8c7-8a46-41a6-a563-96cd26f1cf39"
provider_responses
0
endpoint_id"65367950-3b6c-4abd-9cb0-d553a47de6d9"
id"chatcmpl-baada8c7-8a46-41a6-a563-96cd26f1cf39"
is_byokfalse
latency1007
model_permaslug"deepseek/deepseek-v4-pro-20260813"
provider_name"SiliconFlow"
status200
total_cost0.03112428
cache_discount(null)
upstream_inference_cost0
provider_name"SiliconFlow"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences5
tagDensity0.4
leniency0.8
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount877
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)
71.49% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount877
totalAiIsms5
found
0
word"shattered"
count1
1
word"pulsed"
count1
2
word"footsteps"
count1
3
word"echoed"
count1
4
word"whisper"
count1
highlights
0"shattered"
1"pulsed"
2"footsteps"
3"echoed"
4"whisper"
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
narrationSentences91
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences91
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences94
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen31
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords9
totalWords875
ratio0.01
matches
0"Veil Market. Token upon entry. No exceptions."
1"Veil Market."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
91.86% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount860
uniqueNames12
maxNameDensity1.16
worstName"Herrera"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Camden1
Harlow8
Quinn1
Tomás1
Herrera10
Saint1
Christopher1
Raven1
Nest1
Soho2
Market2
Morris1
persons
0"Camden"
1"Harlow"
2"Quinn"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
7"Morris"
places
0"Raven"
1"Soho"
globalScore0.919
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences72
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount875
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences94
matches
0"seen that medallion"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs25
mean35
std26.91
cv0.769
sampleLengths
0107
113
262
322
452
555
69
72
865
964
1067
1146
1218
1312
141
1575
1637
1716
1815
1920
2039
2131
2211
2333
243
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences91
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs150
matches(empty)
82.07% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences94
ratio0.021
matches
0"The light caught his face, and she saw the change in it—not surprise, not fear."
1"A voice echoed up—not his."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount865
adjectiveStacks0
stackExamples(empty)
adverbCount25
adverbRatio0.028901734104046242
lyAdverbCount1
lyAdverbRatio0.0011560693641618498
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences94
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences94
mean9.31
std5.09
cv0.547
sampleLengths
013
117
215
316
415
531
613
712
88
910
1011
1117
124
139
147
156
169
175
1816
193
2015
211
223
235
2410
2512
2616
2712
287
292
302
3111
326
3312
3410
354
364
378
3810
396
4013
4110
429
436
4420
4512
467
479
486
497
44.68% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.2872340425531915
totalSentences94
uniqueOpeners27
77.52% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences86
matches
0"Then he ran."
1"Then she faced the green"
ratio0.023
94.42% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount27
totalSentences86
matches
0"He wore a waxed jacket"
1"She had first seen that"
2"She followed him for another"
3"Her radio squawked against her"
4"She thumbed it silent and"
5"He turned into an alley"
6"Her boots hit puddles and"
7"He swung around a stack"
8"She vaulted the pallets, landed"
9"Her voice bounced off the"
10"She pumped her arms."
11"She could hear his breathing"
12"He threw something over his"
13"She turned her face away"
14"She ran through it blind"
15"He scrambled up, the satchel"
16"She lunged and grabbed the"
17"He twisted, kicked backward, and"
18"He dropped to the other"
19"She steadied herself and looked"
ratio0.314
6.51% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount78
totalSentences86
matches
0"Rain lashed the streets of"
1"Detective Harlow Quinn stood in"
2"He wore a waxed jacket"
3"The Saint Christopher medallion at"
4"She had first seen that"
5"Tonight, he was far from"
6"She followed him for another"
7"The rain softened the sound"
8"Her radio squawked against her"
9"She thumbed it silent and"
10"Herrera had the loose, patient"
11"That was his mistake."
12"He turned into an alley"
13"The sign had lost half"
14"Harlow waited three seconds, then"
15"The alley stank of wet"
16"Bins lined the brick walls."
17"A motion light snapped on"
18"Herrera glanced back."
19"The light caught his face,"
ratio0.907
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences86
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences42
technicalSentenceCount2
matches
0"She had first seen that medallion six nights ago outside The Raven’s Nest in Soho, when he had ducked through a side door carrying a duffel that clinked like la…"
1"The gatekeeper waited, one hand on the iron, watching her with eyes that reflected the green light like a cat’s."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
88.9517%