Run: 2026-04-25-01KQ2GWNFJQ3KS8WHH2S2FXS9D

Run Details

Duration:
26.6s
Cost:
$0.000537
Provider:
DeepSeek

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="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <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> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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 air in the abandoned Aldwych tube station tasted of rust and disinfectant, a chemical lie spread too thick to cover the rot beneath. Detective Harlow Quinn ducked under the crime scene tape, her worn leather watch catching the glare of the portable floodlights they’d rigged along the platform. The tiled walls sweated with decades of London damp, and the silence had a weight to it, a pressure against the eardrums that spoke of spaces not meant for the living. “Quinn. Over here.” DS Patel stood near the ticket booth, his notepad out, his face the pale green of a man who’d eaten a bad sandwich. He’d been on the job for five years, and he still hadn’t learned to hide his stomach. Quinn appreciated the honesty. “Tell me,” she said, stepping over a scatter of broken glass. The floor was a museum of discarded things: a single trainer, a crushed can of Tizer, a copy of the *Evening Standard* from three weeks ago. “Uniform found him at 0330. A patrol unit heard a scream, traced it to a maintenance access door that was wedged open. They followed the sound down.” Patel gestured toward the far end of the platform, where a body lay half-hidden behind a rusted-out ticket machine. The figure was small, twisted, limbs arranged at angles that defied the body’s natural hinges. Quinn approached, her footsteps echoing in the hollow space. She crouched, studying the victim without touching. A man, mid-thirties, dressed in jeans and a hoodie that had once been navy. His neck was bent too far to the left, a clean, brutal break. No blood. No obvious signs of a struggle. “Cause of death?” “Preliminary says blunt force trauma to the back of the skull, consistent with a fall down the stairs from the street level. The pathologist thinks it’s an accident.” “A fall.” Quinn tilted her head, her eyes tracing the man’s hands. They were still, palms open, fingers slightly curled. No dirt under the nails. No defensive wounds. “He fell down an entire flight of concrete stairs and landed here, behind a ticket machine, without a single scrape on his knuckles?” Patel shifted his weight. “The angle of the fracture—it lines up with the edge of the bottom step. The geometry is right.” “The geometry.” Quinn stood, brushing dust from her knees. “Let me see the stairs.” She walked back toward the entrance, past the floodlights and the constables who pretended not to stare. The staircase was narrow, the steps worn concave by a century of commuters. Quinn stopped at the bottom, turned, and looked up. The angle was steep. The lighting was poor, one bulb flickering at the top, casting jagged shadows. She climbed. Slowly. Counting each step. At the top, she pushed open the heavy door to the street. The cold night air hit her face, sharp and clean. She stood there, breathing, letting the city hum fill her ears. Then she turned and looked back down the stairs. The bulb flickered. For a fraction of a second, the shadows on the wall rearranged themselves, coalescing into a shape that wasn’t quite human. She blinked, and it was gone. She descended again, her mind working. “Patel,” she said, her voice flat. “How did he get in?” “The maintenance access door. It was wedged open with a piece of wood.” “And how did a man who supposedly fell down the stairs end up twenty metres from the bottom of those stairs, behind a ticket machine?” Patel’s brow furrowed. “He could have crawled. In shock. Trying to find help.” “He had a broken neck, Patel. His spinal cord was severed. He didn’t crawl anywhere.” The silence stretched. Patel cleared his throat. “There’s something else. We found a token in his pocket. A small bone, carved with symbols. Not something you see every day.” Quinn’s heart beat a fraction faster. She held out her hand. “Show me.” Patel pulled an evidence bag from his coat. Inside was a disc of ivory, no bigger than a pound coin, etched with concentric rings and runic figures. The edges were smooth, polished to a dull shine. Quinn hadn’t seen a bone token in three years. Not since Morris. Not since the night she’d chased a suspect through Seven Dials and found him standing over a dead man in a locked room, holding a token just like this one. She’d never proven a thing. The case had been ruled a heart attack, and Morris had been reassigned. Two weeks later, he’d vanished from a police car parked outside his own flat. “This isn’t an accident,” she said, her voice low. “Quinn, the physical evidence—” “Forget the physical evidence. Look at the scene. Look at the geometry, as you called it. If he fell down those stairs, his body should be at the bottom of the stairs. It’s basic physics. A body in motion stays in motion until acted upon by an outside force. So what acted upon it? What moved him twenty metres sideways, behind a ticket machine, and left him tucked away like a forgotten newspaper?” Patel opened his mouth, then closed it. “This was staged,” Quinn continued. “Someone broke his neck, then carried him down here, positioned his body, and wedged the door open to make it look like a vagrant had found his way in. They wanted him found. But they wanted it to look like an accident.” “Who breaks someone’s neck without leaving a mark?” “Someone who knows how.” She thought of the figure in the shadows. The flicker of something wrong. “This station—it’s a dead line. It hasn’t been used in decades. No CCTV. No regular foot traffic. The perfect place for a meeting that no one’s supposed to see.” “A meeting?” “Look at his shoes.” Patel bent down. The victim wore trainers, scuffed but clean. The soles were dry. “He didn’t walk through the rain tonight,” Quinn said. “But it’s been pissing down since six. He was brought here by car, or he arrived before the rain started. Either way, he was expected. He was meeting someone.” “A drug deal gone wrong?” “Maybe. But why hide the body? Why not just leave it where it fell?” She crouched again, this time scanning the floor around the body. The dust was uneven, swept in patterns that didn’t match the station’s natural grime. She pulled a penlight from her pocket and shone it at a low angle, casting long shadows. There, near the wall, a single scrape. Not a scuff from a shoe, but a drag mark, thin and deliberate, as if something had been drawn across the concrete. “Get me a forensics team down here with luminol,” she said. “And check the victim for tattoos. Specifically, small symbols on the inside of his wrist.” Patel stared at her. “How do you know about the tattoo?” Quinn’s jaw tightened. “Because I’ve seen this before. Three years ago. DS Morris’s last case.” “Morris? The one who disappeared?” “He didn’t disappear, Patel. He was taken. And I think whoever did it is still out there, and they’re still using this station as their stage.” She stood, pocketing the penlight. “I want a full canvas of the area. Every homeless shelter, every 24-hour shop, every CCTV camera within a two-block radius. And I want the name of every officer who touched this scene before we arrived.” Patel nodded, his earlier doubt replaced by a focused wariness. “You think someone inside the Met is involved?” “I think this was too clean for a random killing. The door wedged open, the body positioned just so, the token left in his pocket. It’s a message. A signature. And I think someone wanted me to find it.” She turned back to the body, her reflection ghosting in the grimy window of the ticket booth. The dead man’s eyes were open, fixed on some distant point she couldn’t see. His mouth was slack, frozen in an expression that wasn’t quite fear, wasn’t quite peace. It was recognition. He’d known his killer. Known him well enough to let him get close. And that made him dangerous in ways Patel couldn’t yet understand.
created_at"2026-04-25T14:32:24.31Z" 4/25/2026, 2:32:24 PM
model"deepseek/deepseek-v4-flash-20260423"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency589
moderation_latency(null)
generation_time26526
tokens_prompt1149
tokens_completion2105
native_tokens_prompt1033
native_tokens_completion1810
native_tokens_completion_images(null)
native_tokens_reasoning0
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"
usage0.000536732
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provider_responses
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is_byokfalse
latency299
model_permaslug"deepseek/deepseek-v4-flash-20260423"
provider_name"DeepSeek"
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request_id"req-1777127544-vZyX3jKvt5MRaEBrn3Q8"
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provider_name"DeepSeek"
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Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags11
adverbTagCount0
adverbTags(empty)
dialogueSentences41
tagDensity0.268
leniency0.537
rawRatio0
effectiveRatio0
92.65% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1361
totalAiIsmAdverbs2
found
0
adverb"slightly"
count1
1
adverb"slowly"
count1
highlights
0"slightly"
1"slowly"
80.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found
0"Patel"
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
41.22% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1361
totalAiIsms16
found
0
word"silence"
count2
1
word"weight"
count2
2
word"stomach"
count1
3
word"standard"
count1
4
word"traced"
count1
5
word"footsteps"
count1
6
word"echoing"
count1
7
word"tracing"
count1
8
word"flickered"
count1
9
word"furrowed"
count1
10
word"etched"
count1
11
word"flicker"
count1
12
word"perfect"
count1
13
word"scanning"
count1
highlights
0"silence"
1"weight"
2"stomach"
3"standard"
4"traced"
5"footsteps"
6"echoing"
7"tracing"
8"flickered"
9"furrowed"
10"etched"
11"flicker"
12"perfect"
13"scanning"
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
narrationSentences79
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences79
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences109
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen73
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords2
totalWords1359
ratio0.001
matches
0"Evening Standard"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions8
unquotedAttributions0
matches(empty)
78.39% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount768
uniqueNames9
maxNameDensity1.43
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Aldwych1
Harlow1
Quinn11
London1
Patel11
Tizer1
Morris2
Seven1
Dials1
persons
0"Harlow"
1"Quinn"
2"Patel"
3"Morris"
places
0"London"
1"Seven"
globalScore0.784
windowScore0.833
50.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences50
glossingSentenceCount2
matches
0"quite human"
1"quite fear, wasn’t quite peace"
2"quite peace"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.736
wordCount1359
matches
0"Not a scuff from a shoe, but a drag mark, thin"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences109
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs49
mean27.73
std21.24
cv0.766
sampleLengths
080
13
244
337
461
551
63
728
851
922
1014
1156
1248
1330
146
1511
1613
1725
1813
1915
2029
2113
2236
2374
249
254
2673
277
2847
298
3046
312
324
3314
3438
355
3614
3742
3829
3926
4011
4115
425
4367
4418
4539
4646
4716
4811
91.94% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences79
matches
0"was gone"
1"been ruled"
2"been reassigned"
3"been drawn"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs137
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences109
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount771
adjectiveStacks0
stackExamples(empty)
adverbCount18
adverbRatio0.023346303501945526
lyAdverbCount2
lyAdverbRatio0.0025940337224383916
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences109
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences109
mean12.47
std11.04
cv0.885
sampleLengths
024
125
231
33
423
517
64
711
826
946
1015
119
127
1314
1413
152
166
173
1828
1912
208
215
223
2323
244
2518
269
275
2817
2913
309
314
3213
332
341
353
3612
3710
3811
399
403
4121
426
436
446
455
4613
4725
483
4910
56.88% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats8
diversityRatio0.3853211009174312
totalSentences109
uniqueOpeners42
45.66% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences73
matches
0"Then she turned and looked"
ratio0.014
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount21
totalSentences73
matches
0"He’d been on the job"
1"she said, stepping over a"
2"She crouched, studying the victim"
3"His neck was bent too"
4"They were still, palms open,"
5"She walked back toward the"
6"She stood there, breathing, letting"
7"She blinked, and it was"
8"She descended again, her mind"
9"she said, her voice flat"
10"She held out her hand."
11"She’d never proven a thing."
12"she said, her voice low"
13"She thought of the figure"
14"She crouched again, this time"
15"She pulled a penlight from"
16"She stood, pocketing the penlight"
17"She turned back to the"
18"His mouth was slack, frozen"
19"It was recognition."
ratio0.288
55.89% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount59
totalSentences73
matches
0"The air in the abandoned"
1"Detective Harlow Quinn ducked under"
2"The tiled walls sweated with"
3"DS Patel stood near the"
4"He’d been on the job"
5"Quinn appreciated the honesty."
6"she said, stepping over a"
7"The floor was a museum"
8"Patel gestured toward the far"
9"The figure was small, twisted,"
10"Quinn approached, her footsteps echoing"
11"She crouched, studying the victim"
12"A man, mid-thirties, dressed in"
13"His neck was bent too"
14"Quinn tilted her head, her"
15"They were still, palms open,"
16"Patel shifted his weight."
17"Quinn stood, brushing dust from"
18"She walked back toward the"
19"The staircase was narrow, the"
ratio0.808
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences73
matches(empty)
ratio0
68.97% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences29
technicalSentenceCount3
matches
0"DS Patel stood near the ticket booth, his notepad out, his face the pale green of a man who’d eaten a bad sandwich."
1"For a fraction of a second, the shadows on the wall rearranged themselves, coalescing into a shape that wasn’t quite human."
2"Not a scuff from a shoe, but a drag mark, thin and deliberate, as if something had been drawn across the concrete."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags11
uselessAdditionCount3
matches
0"Quinn tilted, her eyes tracing the man’s hands"
1"she said, her voice flat"
2"she said, her voice low"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount1
fancyTags
0"Quinn continued (continue)"
dialogueSentences41
tagDensity0.146
leniency0.293
rawRatio0.167
effectiveRatio0.049
82.0532%