Run: 2026-07-25-01KYD6AC00X89B06WR90VQQV51

Run Details

Duration:
7.0s
Cost:
$0.000183
Provider:
Poolside

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 sheets, hammering the asphalt like someone had turned a fire hose loose on the city. Detective Harlow Quinn’s boots sloshed through puddles as she cut across Oxford Street, her trench coat flapping open behind her. The green neon sign of the Raven’s Nest flickered through the downpour, casting an eerie glow across her face. She was close now—too close for comfort. Tomás Herrera had vanished down an alley behind the bar, his figure swallowed by the darkness just as Quinn had rounded the corner. She skidded to a halt, hand instinctively reaching for her sidearm before she remembered the warrant didn’t cover lethal force for a missing person. Not yet. The alley reeked of wet brick and something else—something she couldn’t name but didn’t like. The air hung thick with the scent of ozone, like after lightning strikes, and the shadows seemed to move with a purpose all their own. Quinn’s hand tightened around the worn leather watch on her wrist, the one Morris had given her before the accident. She pressed it to her temple, feeling its weight against her skin. Old habit. Superstition. Or maybe just desperation. Footsteps echoed behind her—sharp, deliberate. She spun around, gun half-drawn, but only saw the slick reflection of the green sign in a broken window. No—wait.movement. Herrera was back, sprinting now, his olive skin glistening under the streetlight at the end of the alley. He moved like someone who knew every inch of these streets, like he belonged to the darkness rather than the other way around. Quinn fired a single shot into the wall beside him. He flinched, stumbling, but didn’t stop. She followed, her trench coat soaked through, her breath coming in short gasps. The rain had lessened to a drizzle, but the city felt colder somehow, as if the atmosphere itself were holding its breath. When she reached the corner, she expected to see the tube station signs, the familiar blue and pink tiles of Camden’s underground. Instead, a brick wall rose before her, unmarked except for a small, weathered plaque that read *Mornington Crescent*. But she knew better. Quinn shoved her palm against the brick. It gave slightly, almost imperceptibly. She pressed harder, and with a soft grinding sound, a section of the wall swung inward, revealing a narrow passage choked with dust and the smell of damp stone. Her flashlight flickered on, casting a narrow beam ahead. The darkness beyond seemed to swallow the light whole. She stepped through. The tunnel sloped downward, the walls lined with pipes that dripped water into unseen drains. At the bottom, a heavy wooden door stood ajar, and beyond it, the hum of voices and the clink of coin against metal. The Veil Market. Quinn hesitated at the threshold. Her training had taught her to never cross a line without backup. Her experience had taught her that some lines didn’t exist. She needed to see what—or who—was waiting for her in the dark. The market unfolded like a fever dream. Stalls made of salvaged metal and tarpaulin stretched as far as she could see, their wares displayed on overturned crates and makeshift tables. A man with too many eyes offered bottled something that pulsed in the lamplight. A woman whose shadow didn’t match her movements sold vials of what might have been blood. The air buzzed with whispers in languages she didn’t recognize, punctuated by the occasional burst of laughter that sounded too sharp, too knowing. And then she saw him. Herrera stood at a stall run by a figure in a threadbare coat, hands hidden in pockets. The seller held out a small, wrapped package. Herrera took it, paying with a silver coin that caught the light like a blade. Quinn watched as he tore it open, revealing a folded piece of paper. He glanced around, then slipped into a side passage that led behind the stalls. She followed, her hand never straying far from her holster. The passage opened into a narrow corridor lined with more doors—some wooden, some metal, some that seemed to be made of solidified shadow. At the end, a single door stood closed, its surface etched with symbols that made her eyes water just looking at them. Herrera knocked twice. The door creaked open to reveal a room lit by candles that burned green. Shelves lined the walls, packed with books whose titles shifted when she wasn’t looking directly at them. In the center, a table held more packages, more papers, more evidence of things that didn’t belong in the world Quinn had sworn to protect. She pushed the door wide. Herrera looked up, surprised. “Detective Quinn. Shouldn’t you be back at the precinct?” She didn’t answer. Instead, she stepped inside, her boots echoing on the stone floor. The candles flickered, and for a moment, she could’ve sworn the shadows reached for her. “You know what I am,” she said, voice low. “And you know what I found in the package.” Herrera’s medallion caught the candlelight as he touched it. “You don’t understand what you’re dealing with. This isn’t just about Morris.” “It better be,” she said, moving closer. “Because three years, Herrera. Three years I’ve been chasing ghosts, and you hand me a lead only to drag me deeper into whatever hell you’re running from.” The other man’s jaw tightened. “I’m not running from anything.” “Then why are you wearing a Saint Christopher medal in a place where saints go to die?” Behind her, one of the candles snuffed out. The room grew colder. And somewhere in the shadows, something laughed.
created_at"2026-07-25T17:49:10.534Z" 7/25/2026, 5:49:10 PM
model"poolside/laguna-xs-2.1-20260625"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency78
moderation_latency(null)
generation_time5711
tokens_prompt1104
tokens_completion1422
native_tokens_prompt1077
native_tokens_completion1257
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached1072
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)
usage0.0001833
router(null)
provider_responses
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endpoint_id"089ec178-f6dc-4450-aff5-46f68798ce97"
id"chatcmpl-9cd22af9c74b4e53978a8bffa740f906"
is_byokfalse
latency78
model_permaslug"poolside/laguna-xs-2.1-20260625"
provider_name"Poolside"
status200
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api_type"completions"
id"gen-1785001750-Qy2Z94SBMxpopLxz832C"
upstream_id"chatcmpl-9cd22af9c74b4e53978a8bffa740f906"
total_cost0.0001833
cache_discount0.0000536
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provider_name"Poolside"
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
94.68% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount939
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"slightly"
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)
41.43% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount939
totalAiIsms11
found
0
word"flickered"
count3
1
word"weight"
count1
2
word"footsteps"
count1
3
word"echoed"
count1
4
word"glistening"
count1
5
word"familiar"
count1
6
word"pulsed"
count1
7
word"etched"
count1
8
word"echoing"
count1
highlights
0"flickered"
1"weight"
2"footsteps"
3"echoed"
4"glistening"
5"familiar"
6"pulsed"
7"etched"
8"echoing"
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
narrationSentences69
matches(empty)
60.04% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount3
narrationSentences69
filterMatches
0"watch"
hedgeMatches
0"seemed to"
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
maxSentenceWordsSeen29
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords2
totalWords932
ratio0.002
matches
0"Mornington Crescent"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount845
uniqueNames11
maxNameDensity0.95
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Harlow1
Quinn8
Oxford1
Street1
Raven1
Nest1
Herrera7
Morris1
Camden1
Veil1
Market1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Nest"
4"Herrera"
5"Morris"
6"Camden"
7"Market"
places
0"Oxford"
1"Street"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences49
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount932
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
totalParagraphs26
mean35.85
std25.63
cv0.715
sampleLengths
066
149
279
325
457
575
64
759
83
941
1039
1183
125
1367
1455
153
1656
175
1813
1929
2018
2121
2234
2310
2417
2519
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences69
matches(empty)
69.28% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs153
matches
0"were holding"
1"was waiting"
2"wasn’t looking"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount0
flaggedSentences6
totalSentences75
ratio0.08
matches
0"She was close now—too close for comfort."
1"The alley reeked of wet brick and something else—something she couldn’t name but didn’t like."
2"Footsteps echoed behind her—sharp, deliberate."
3"No—wait.movement."
4"She needed to see what—or who—was waiting for her in the dark."
5"The passage opened into a narrow corridor lined with more doors—some wooden, some metal, some that seemed to be made of solidified shadow."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount853
adjectiveStacks0
stackExamples(empty)
adverbCount28
adverbRatio0.032825322391559206
lyAdverbCount5
lyAdverbRatio0.005861664712778429
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
mean12.43
std7.43
cv0.598
sampleLengths
020
120
219
37
423
524
62
715
825
920
1012
112
121
134
145
1519
161
1718
1823
1910
206
2113
2222
2322
2418
254
267
275
2829
299
309
313
3215
3323
343
355
3612
3710
3812
397
4023
4114
4216
4323
445
4517
468
4715
4813
4914
60.44% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.38666666666666666
totalSentences75
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences65
matches
0"Instead, a brick wall rose"
1"Instead, she stepped inside, her"
ratio0.031
96.92% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences65
matches
0"She was close now—too close"
1"She skidded to a halt,"
2"She pressed it to her"
3"She spun around, gun half-drawn,"
4"He moved like someone who"
5"He flinched, stumbling, but didn’t"
6"She followed, her trench coat"
7"It gave slightly, almost imperceptibly."
8"She pressed harder, and with"
9"Her flashlight flickered on, casting"
10"She stepped through."
11"Her training had taught her"
12"Her experience had taught her"
13"She needed to see what—or"
14"He glanced around, then slipped"
15"She followed, her hand never"
16"She pushed the door wide."
17"She didn’t answer."
18"she said, voice low"
19"she said, moving closer"
ratio0.308
44.62% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount54
totalSentences65
matches
0"The rain came down in"
1"Detective Harlow Quinn’s boots sloshed"
2"The green neon sign of"
3"She was close now—too close"
4"Tomás Herrera had vanished down"
5"She skidded to a halt,"
6"The alley reeked of wet"
7"The air hung thick with"
8"Quinn’s hand tightened around the"
9"She pressed it to her"
10"Footsteps echoed behind her—sharp, deliberate."
11"She spun around, gun half-drawn,"
12"Herrera was back, sprinting now,"
13"He moved like someone who"
14"Quinn fired a single shot"
15"He flinched, stumbling, but didn’t"
16"She followed, her trench coat"
17"The rain had lessened to"
18"Quinn shoved her palm against"
19"It gave slightly, almost imperceptibly."
ratio0.831
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences65
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences39
technicalSentenceCount2
matches
0"Instead, a brick wall rose before her, unmarked except for a small, weathered plaque that read *Mornington Crescent*."
1"The air buzzed with whispers in languages she didn’t recognize, punctuated by the occasional burst of laughter that sounded too sharp, too knowing."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount1
matches
0"she said, voice low"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
82.2469%