Run: 2026-04-24-01KQ01P9PWBSDG5WN2YW6A1YCW

Run Details

Duration:
8m 8s
Cost:
$0.049474
Provider:
Inceptron

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 had scrubbed Camden clean of shame. It drummed against awnings and filled gutters to overflowing, turning the high street into a river of neon and grease. Harlow Quinn stood beneath the skeletal remains of a shop canopy, water darkening her cropped salt-and-pepper hair to wet steel. A drop slid from the worn leather watch on her left wrist and vanished into the collar of her coat. Three hours she had watched the derelict textile mill across the road. Three hours of military stillness. Then the door sighed open. Tomás Herrera emerged. Olive skin, dark curls plastered flat by the deluge, the silver Saint Christopher medallion at his throat catching the glare of a passing bus. He clutched a canvas medical bag to his ribs. Harlow's sharp jaw tightened. She pushed off the wall. He saw her. Warm brown eyes locked onto hers across the torrent. For a heartbeat, neither moved. He ran. Harlow's boots struck the pavement. Puddle water erupted in geysers. She cut a diagonal across the road, a horn blaring as a taxi swerved. Tomás bolted left, down a service alley choked with bin bags and rusted bicycles. He moved like a man who had carried stretchers through riots—economical, terrified. Harlow matched him stride for stride. Her breath came steady. Eighteen years of decorated service had carved her into something tireless. He vaulted a chain-link fence. His left forearm snagged on the crown of wire. The scar along it split fresh. Blood ribboned down his wrist and speckled the concrete. He didn't falter. Landing hard, he sprinted toward the railway arches. "Metropolitan Police! Stop!" Her voice shredded the rain. Tomás cast one glance over his shoulder. He kept moving. The green neon of a distant sign—The Raven's Nest—bled into the storm behind her, fading. She had lost sight of her backup. She had lost sight of everything except the medallion flashing at his back. He skidded around a brick arch and vanished. Harlow vaulted the same fence. Wire tore a rent in her coat sleeve. She landed in a crouch, rainwater flooding her collar. Ahead, a metal door sagged open in the brickwork. A service entrance to the old Tube network. Stencil markings read CAMDEN STATION WORKS—DO NOT ENTER. The padlock hung severed, its clasp gleaming with fresh oil. Inside, darkness swallowed the stairs. Three years ago, DS Morris had chased a suspect through a door like this one. They had closed the investigation before the autopsy report cleared. The file held more black ink than text. Harlow stared into the throat of the stairwell. The smell that rose from it was not London—too old, too sweet, like fruit left to rot in a church. She drew her baton. Descended. The stairs spiralled. Her shoes scraped on decades of lime and flaking paint. Halfway down, her heel skidded on something slick. She caught the railing. Her watchband scraped brick. The jolt rattled her teeth. Somewhere below, Tomás's footsteps slapped stone. They stopped. Harlow paused. She checked her watch. The leather band felt slick against her skin. Thirty seconds. She pressed on. The stairs bottomed out into a vaulted tunnel. White ceramic tile lined the walls, cracked and weeping black mould. But the platform ahead was wrong. It should have been empty, abandoned, a grave for the Blitz era. Instead, amber light flickered from iron braziers. Figures moved between stalls draped in velvet and hanging chains. A low murmur of commerce hummed beneath the railway arches, accompanied by a sound like wet clicking and the clink of glass vials. The Veil Market. Harlow stepped onto the platform. Coins clinked against brass. A woman with milk-white eyes weighed something purple and fibrous on a scale that tipped without weights. A man in a top hat whispered to a fox that sat upon his shoulder, its snout smeared red. No one looked at Harlow. They looked through her, or past her, as though she were a ghost that had not yet admitted its death. Tomás stood near a support pillar twenty yards ahead. He was speaking in rapid Spanish to a sentry who blocked the aisle. The man wore a butcher's apron stained the colour of rust. Arms like cable bins. A medallion of bone hung at his own throat—the entry token. Harlow walked toward them. Her boots announced her on the tile. Tomás turned. Fresh blood painted his forearm from elbow to wrist, weaving through the old scar tissue. His medical bag hung heavy at his side, the strap cutting into his shoulder. "Go back, Detective." His voice carried the lilt of Seville, sharp enough to cut through the bazaar drone. "You don't belong here." "You ran." Harlow stopped ten feet from the sentry. She didn't raise the baton yet. Her right hand hovered near the cuff of her coat. "Running makes you belong to me." The butcher shifted. Apron leather creaked. He held out a hand the size of a shovel head. Two fingers were missing, stumps wrapped in dirty gauze that wept yellow. "Token." His voice sounded like grinding gravel. "No bone, no entry. Market moves on the full moon. Strangers bleed on the tracks." "I don't have a token." Harlow's gaze stayed on Tomás. "And I don't bleed easy." Tomás stepped forward. The Saint Christopher medallion swung at his chest, the patron saint of travellers watching his back with blank silver eyes. "Your partner didn't die in that warehouse, Harlow. He died because he walked into a room like this one. He saw past the veil. They don't let you unsee it." Harlow's fingers whitened on the baton. Morris. The funeral had been closed-casket. The coroner had mumbled about cardiac arrest, but Morris was thirty-six and ran marathons. She had seen the crime scene photos. The warehouse walls had bent inward, as though something massive had inhaled the brick. Her thumb brushed the snap of her empty holster. She had left the firearm in the car. A mistake. Or a reflex older than logic. "You know what happened to him." "I know enough to warn you." Tomás gripped his bag with both hands. Scar tissue pulled taut across his left forearm. Blood dripped onto the tile, each drop dark. "There's a line. Cross it, and you're not a detective anymore. You're meat. Or currency." The butcher grinned. Teeth filed to points. "One road goes back up, officer. The surface still wants you. Down here, we don't. Down here, we trade." Harlow looked past them. The stalls curved deeper into the abandoned station. Shadows there moved with too many legs. A child laughed from a vent, high and cold. If she let Tomás vanish into that dark, the trail went cold. Eighteen years meant nothing if she balked at a threshold. But Morris had stood at a threshold too. He had crossed it. Something had crossed back. Rainwater dripped from her hair onto the tile. The droplets spread like ink. She rolled her shoulders. The baton snapped up into a ready position. "Then I'll see what he saw." Harlow stepped forward. The butcher reached. She twisted inside his reach, drove her elbow into his sternum. Bone crunched. He grunted, stumbled back against the pillar. A jar of eyeballs shattered on the stone. She didn't break stride. Tomás's eyes widened. He turned and plunged deeper into the aisle. Harlow followed him into the dark.
created_at"2026-04-24T15:28:18.154Z" 4/24/2026, 3:28:18 PM
model"moonshotai/kimi-k2.6-20260420"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency502
moderation_latency(null)
generation_time488033
tokens_prompt1140
tokens_completion15214
native_tokens_prompt1000
native_tokens_completion13907
native_tokens_completion_images(null)
native_tokens_reasoning13349
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"
usage0.0494745
router(null)
provider_responses
0
endpoint_id"9bdd7d54-5cfa-4fa8-988b-345ef4b9fd68"
id"chatcmpl-1fec5f9f181e38951c5afb6a1e61e8e0"
is_byokfalse
latency364
model_permaslug"moonshotai/kimi-k2.6-20260420"
provider_name"Inceptron"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777044498-JI7WPboJVVywlF3DYTE9"
session_id(null)
api_type"completions"
id"gen-1777044498-rKIav8EcANf3iXW26rYn"
upstream_id"chatcmpl-1fec5f9f181e38951c5afb6a1e61e8e0"
total_cost0.0494745
cache_discount(null)
upstream_inference_cost0
provider_name"Inceptron"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags5
adverbTagCount0
adverbTags(empty)
dialogueSentences15
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1232
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)
71.59% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1232
totalAiIsms7
found
0
word"gleaming"
count1
1
word"footsteps"
count1
2
word"grave"
count1
3
word"flickered"
count1
4
word"velvet"
count1
5
word"lilt"
count1
6
word"shattered"
count1
highlights
0"gleaming"
1"footsteps"
2"grave"
3"flickered"
4"velvet"
5"lilt"
6"shattered"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes widened"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences139
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences139
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)
analyzedSentences149
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen30
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1226
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
77.27% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions51
wordCount1100
uniqueNames19
maxNameDensity1.45
worstName"Harlow"
maxWindowNameDensity2
worstWindowName"Tomás"
discoveredNames
Camden1
Quinn1
Herrera1
Saint2
Christopher2
Raven1
Nest1
Tube1
Morris4
London1
Tomás11
Blitz1
Veil1
Market1
Harlow16
Spanish1
Seville1
Scar1
Three3
persons
0"Camden"
1"Quinn"
2"Herrera"
3"Saint"
4"Christopher"
5"Raven"
6"Nest"
7"Morris"
8"Tomás"
9"Harlow"
places
0"London"
1"Seville"
globalScore0.773
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences85
glossingSentenceCount1
matches
0"sounded like grinding gravel"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1226
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences149
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs36
mean34.06
std24.74
cv0.727
sampleLengths
084
15
245
319
471
540
63
715
835
98
1062
1161
125
1361
1477
153
1670
1748
1842
1922
2031
2129
2222
2315
2453
2572
266
2744
2826
2966
3013
3112
326
3338
3411
356
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences139
matches
0"been closed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs199
matches
0"was speaking"
46.98% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount6
semicolonCount0
flaggedSentences5
totalSentences149
ratio0.034
matches
0"He moved like a man who had carried stretchers through riots—economical, terrified."
1"The green neon of a distant sign—The Raven's Nest—bled into the storm behind her, fading."
2"Stencil markings read CAMDEN STATION WORKS—DO NOT ENTER."
3"The smell that rose from it was not London—too old, too sweet, like fruit left to rot in a church."
4"A medallion of bone hung at his own throat—the entry token."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1112
adjectiveStacks0
stackExamples(empty)
adverbCount15
adverbRatio0.013489208633093525
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences149
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences149
mean8.23
std5.24
cv0.637
sampleLengths
07
120
220
320
412
55
65
73
824
99
104
115
123
139
145
152
165
175
1814
1914
2012
216
224
2311
245
259
266
279
283
298
303
315
327
333
3415
357
3613
378
385
398
409
419
428
438
4410
455
4615
4710
488
498
60.40% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.3825503355704698
totalSentences149
uniqueOpeners57
76.92% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences130
matches
0"Then the door sighed open."
1"Somewhere below, Tomás's footsteps slapped"
2"Instead, amber light flickered from"
ratio0.023
90.77% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount42
totalSentences130
matches
0"It drummed against awnings and"
1"He clutched a canvas medical"
2"She pushed off the wall."
3"He saw her."
4"She cut a diagonal across"
5"He moved like a man"
6"Her breath came steady."
7"He vaulted a chain-link fence."
8"His left forearm snagged on"
9"He didn't falter."
10"Her voice shredded the rain."
11"He kept moving."
12"She had lost sight of"
13"She had lost sight of"
14"He skidded around a brick"
15"She landed in a crouch,"
16"They had closed the investigation"
17"She drew her baton."
18"Her shoes scraped on decades"
19"She caught the railing."
ratio0.323
44.62% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount108
totalSentences130
matches
0"Rain had scrubbed Camden clean"
1"It drummed against awnings and"
2"Harlow Quinn stood beneath the"
3"A drop slid from the"
4"Tomás Herrera emerged."
5"Olive skin, dark curls plastered"
6"He clutched a canvas medical"
7"Harlow's sharp jaw tightened."
8"She pushed off the wall."
9"He saw her."
10"Harlow's boots struck the pavement."
11"Puddle water erupted in geysers."
12"She cut a diagonal across"
13"Tomás bolted left, down a"
14"He moved like a man"
15"Harlow matched him stride for"
16"Her breath came steady."
17"He vaulted a chain-link fence."
18"His left forearm snagged on"
19"The scar along it split"
ratio0.831
38.46% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences130
matches
0"If she let Tomás vanish"
ratio0.008
65.64% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences37
technicalSentenceCount4
matches
0"He moved like a man who had carried stretchers through riots—economical, terrified."
1"A woman with milk-white eyes weighed something purple and fibrous on a scale that tipped without weights."
2"A man in a top hat whispered to a fox that sat upon his shoulder, its snout smeared red."
3"The warehouse walls had bent inward, as though something massive had inhaled the brick."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
89.0884%