Run: 2026-09-03-01M1KCK2TDWT0SS23S29J0D791

Run Details

Duration:
2m 19s
Cost:
$0.024192
Provider:
Phala

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
The rain came down in nails, slanting through the orange glow of the streetlamps and turning Camden High Street into a river of black glass. Detective Harlow Quinn kept her eyes fixed on the suspect’s back as he darted through the late crowd. He wore a waxed jacket with the hood thrown back, and he ran like a man who had done this before—shoulders low, arms tight, feet finding the cracks between puddles. “Stop! Police!” she shouted. He didn’t turn. A bus hissed past, throwing a sheet of water across her legs. Harlow vaulted the curb, slipped on a grate, and caught herself against a bollard. The leather strap of her watch had gone soft with rain. It twisted around her wrist. She pulled it tight and pushed on. The suspect cut down a side street, past shuttered shopfronts and a launderette leaking steam into the night. Harlow gained a stride. Her lungs burned. The heavy fabric of her coat slapped against her thighs. She had been tailing him for six blocks, ever since he stepped out of a building on Arlington Road with one hand pressed flat over something tucked inside his jacket. Now he led her toward the disused Tube station. He vanished through a gap in the hoarding around the old entrance. A sheet of corrugated metal swung back and clanged against the brick. Harlow reached the opening a heartbeat later. Cold air spilled out from under the ground, thick with wet clay and old candle wax. She drew her weapon and ducked through. A stairwell dropped away into darkness. The rain stopped at the first landing, but the cold deepened. The walls sweated. Somewhere below, the suspect’s boots rang on iron steps, then on concrete, then on tile. Harlow took the stairs two at a time, one hand trailing the wall. The floral smell hit her before she saw the platform—jasmine, or something like jasmine, rising through the rot. She emerged onto a platform that time had abandoned. Old advertising boards curled away from the walls. A ticket machine gaped open, its wires pulled out like entrails. A single bulb hummed above a booth made of wood and brass. Behind the glass, a woman sat knitting with needles that looked sharp enough to sew leather. The suspect ran straight to the booth and slapped a pale disc onto the counter. Harlow sprinted forward. “Police! Don’t move!” The suspect didn’t look back. The woman’s hand closed over the disc—a bone token, small and yellowed against her grey palm. She pulled a lever built into the side of the booth. The far wall split open. Light poured through the seam, warm and green and wrong for underground. A sound like a hundred conversations, a market’s pulse, rolled out with it. The suspect lunged through the widening gap and disappeared. Harlow reached the booth as the panel began to slide back. She raised her gun. “Open that door.” The woman’s eyes lifted. They were pale, almost colourless. “Token.” Harlow dug her badge from her belt. “Metropolitan Police. I don’t need a token. Keep it open.” The panel kept moving. The woman’s needles clicked, steady as a pulse. “Down there you need different currency, detective. No token, no trade.” Harlow glanced past her into the impossible space beyond the door. Stalls crowded around pylons of black brick. Lamplight flickered in jars along the walkway. A man led a creature that looked like a deer on a lead, except its antlers moved too slowly, as if they were made of smoke. The suspect was already halfway down the first row, his grey hood merging with the crowd. She had four seconds before the door sealed. Her radio had died three streets back, sputtering in the rain. No backup. No warrant. No map. Just an open maw in the earth where her suspect had vanished and a gatekeeper who looked at her like she was a stray dog at a butcher’s counter. Harlow moved. She slammed her left boot between the sliding panel and the jamb. The wood bit into her ankle. She wedged her shoulder into the gap and forced it back a few more inches. The gatekeeper didn’t bother to stop her. The clicking of the needles went on. “You’ll come out different,” the woman said. Harlow met her eyes. “I’m already different.” She turned sideways and pushed through. The panel scraped across her coat, tore a button from her sleeve, and closed behind her with a soft, final thud. The sound of rain vanished. In its place rose the noise of the Veil Market—haggling in languages that felt older than English, the chime of brass scales, a wheeze from somewhere deep in the dark. The air tasted like pennies and jasmine. Harlow kept her weapon low against her thigh and walked forward. The suspect’s wet footprints led into the crowd. She followed them past a stall where jars of blue fire hung from hooks like lanterns. A vendor with brass teeth called out in a language she didn’t know. A child with too many fingers offered her a map that drew itself as she watched. She ignored them. Her focus stayed on the grey hood, on the hunched shoulders, on the way the crowd parted around him like water around a stone. A man brushed past her, a bag slung over his shoulder dripping something dark onto the platform. She caught a glimpse of a Saint Christopher medallion at his throat, spinning as he moved. Then he was gone into the stalls. Harlow tracked the suspect instead. He turned left between a cart piled with bottles of murky glass and a cage full of ravens that didn’t croak. They watched her with unnervingly human eyes. She pressed on. The suspect glanced back. For the first time, she saw his face clearly—sharp, pale, afraid. He stumbled over a crate and knocked over a stack of wooden boxes. Something inside shattered. A vendor shrieked. The crowd surged, and the suspect threw himself into a narrow passage behind a curtain of iron beads. Harlow reached the passage as the beads swung back into place. The darkness beyond was complete. The sound of the market fell away, replaced by a low hum that vibrated through the floor and up into her jaw. She could still hear his footsteps, fast and wet, fading into the black. Her fingers tightened on the grip of her pistol. She had lost people on stairwells like this. She had lost Morris to something that never fit an incident report. The staircase had been cold that night too, slick with rain, reeking of jasmine and copper. The tunnel waited. Harlow pulled a small torch from her belt and clicked it on. The beam cut through dust and shadow, revealing a corridor that sloped deeper into the earth. The walls were old brick, glistening with moisture. Somewhere below, a door slammed. She stepped forward and descended into the dark.
created_at"2026-09-03T10:21:19.584Z" 9/3/2026, 10:21:19 AM
model"deepseek/deepseek-v4-pro-20260813"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1068
moderation_latency(null)
generation_time138476
tokens_prompt1104
tokens_completion6273
native_tokens_prompt1082
native_tokens_completion5494
native_tokens_completion_images(null)
native_tokens_reasoning4017
native_tokens_cached1024
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)
usage0.02419154
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788430879-D76MWHeQBrzelZ3WwgIZ"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788430879-t3t6ZE1qrz8tgXxtISdj"
upstream_id"req_400b6d94905ec94194c5c296c82e74b2"
provider_responses
0
endpoint_id"bb1fb528-2000-460c-a65e-b82dc347c019"
id"req_400b6d94905ec94194c5c296c82e74b2"
is_byokfalse
latency392
model_permaslug"deepseek/deepseek-v4-pro-20260813"
provider_name"Phala"
status200
total_cost0.02419154
cache_discount0.0013312
upstream_inference_cost0
provider_name"Phala"
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)
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
95.70% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1164
totalAiIsmAdverbs1
found
0
adverb"slowly"
count1
highlights
0"slowly"
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)
65.64% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1164
totalAiIsms8
found
0
word"pulse"
count2
1
word"flickered"
count1
2
word"maw"
count1
3
word"shattered"
count1
4
word"vibrated"
count1
5
word"footsteps"
count1
6
word"glistening"
count1
highlights
0"pulse"
1"flickered"
2"maw"
3"shattered"
4"vibrated"
5"footsteps"
6"glistening"
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
narrationSentences106
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences106
filterMatches
0"watch"
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences112
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen31
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1159
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
83.16% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions28
wordCount1122
uniqueNames14
maxNameDensity1.34
worstName"Harlow"
maxWindowNameDensity2
worstWindowName"Harlow"
discoveredNames
Camden1
High1
Street1
Harlow15
Quinn1
Arlington1
Road1
Tube1
Veil1
Market1
English1
Saint1
Christopher1
Morris1
persons
0"Harlow"
1"Quinn"
2"Market"
3"Saint"
4"Christopher"
5"Morris"
places
0"Camden"
1"High"
2"Street"
3"Arlington"
4"Road"
globalScore0.832
windowScore1
52.60% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences77
glossingSentenceCount3
matches
0"something like jasmine, rising through the r"
1"looked like a deer on a lead, except its"
2"tasted like pennies and jasmine"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1159
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences112
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean41.39
std27.74
cv0.67
sampleLengths
073
14
252
374
454
566
671
76
871
918
1010
1117
1223
1367
1454
1549
167
177
1869
1991
2073
213
2252
2351
2445
253
2641
278
98.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences106
matches
0"were made"
1"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs181
matches(empty)
15.31% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences5
totalSentences112
ratio0.045
matches
0"He wore a waxed jacket with the hood thrown back, and he ran like a man who had done this before—shoulders low, arms tight, feet finding the cracks between puddles."
1"The floral smell hit her before she saw the platform—jasmine, or something like jasmine, rising through the rot."
2"The woman’s hand closed over the disc—a bone token, small and yellowed against her grey palm."
3"In its place rose the noise of the Veil Market—haggling in languages that felt older than English, the chime of brass scales, a wheeze from somewhere deep in the dark."
4"For the first time, she saw his face clearly—sharp, pale, afraid."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1127
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.031055900621118012
lyAdverbCount3
lyAdverbRatio0.0026619343389529724
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences112
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences112
mean10.35
std6.67
cv0.645
sampleLengths
025
118
230
34
43
512
614
711
85
97
1018
114
123
1310
1430
159
1612
1712
187
1916
207
216
2211
233
2415
2513
2618
279
288
2911
3012
3116
3215
333
343
355
3616
3711
385
3912
4013
419
4211
434
443
454
465
471
487
4910
38.39% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats13
diversityRatio0.2767857142857143
totalSentences112
uniqueOpeners31
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences101
matches
0"Somewhere below, the suspect’s boots"
1"Just an open maw in"
2"Then he was gone into"
3"Somewhere below, a door slammed."
ratio0.04
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount30
totalSentences101
matches
0"He wore a waxed jacket"
1"He didn’t turn."
2"It twisted around her wrist."
3"She pulled it tight and"
4"Her lungs burned."
5"She had been tailing him"
6"He vanished through a gap"
7"She drew her weapon and"
8"She emerged onto a platform"
9"She pulled a lever built"
10"She raised her gun."
11"They were pale, almost colourless."
12"She had four seconds before"
13"Her radio had died three"
14"She slammed her left boot"
15"She wedged her shoulder into"
16"She turned sideways and pushed"
17"She followed them past a"
18"She ignored them."
19"Her focus stayed on the"
ratio0.297
9.50% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount91
totalSentences101
matches
0"The rain came down in"
1"Detective Harlow Quinn kept her"
2"He wore a waxed jacket"
3"He didn’t turn."
4"A bus hissed past, throwing"
5"Harlow vaulted the curb, slipped"
6"The leather strap of her"
7"It twisted around her wrist."
8"She pulled it tight and"
9"The suspect cut down a"
10"Harlow gained a stride."
11"Her lungs burned."
12"The heavy fabric of her"
13"She had been tailing him"
14"He vanished through a gap"
15"A sheet of corrugated metal"
16"Harlow reached the opening a"
17"She drew her weapon and"
18"A stairwell dropped away into"
19"The rain stopped at the"
ratio0.901
49.50% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences101
matches
0"Now he led her toward"
ratio0.01
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences52
technicalSentenceCount3
matches
0"He wore a waxed jacket with the hood thrown back, and he ran like a man who had done this before—shoulders low, arms tight, feet finding the cracks between pudd…"
1"Just an open maw in the earth where her suspect had vanished and a gatekeeper who looked at her like she was a stray dog at a butcher’s counter."
2"In its place rose the noise of the Veil Market—haggling in languages that felt older than English, the chime of brass scales, a wheeze from somewhere deep in th…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0.5
effectiveRatio0.25
84.4481%