Run: 2026-04-25-01KQ2JQHP3H90HHATH5DZ0Q2HG

Run Details

Duration:
1m 16s
Cost:
$0.002373
Provider:
DeepSeek

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
Detective Harlow Quinn stepped off the Tube carriage and into air that tasted of copper and wet limestone. The platform stretched empty in both directions, its tiles shedding decades of soot and neglect. This station had been closed since the Blitz, sealed up and forgotten like a tomb. Yet here she was, her breath misting in the chill, the beam of her torch cutting through the darkness toward a cluster of figures two hundred yards down the tracks. Her shoes, sensible flats with grips that had seen her through eighteen years of crime scenes, made no sound on the concrete. She’d learned to move quietly early in her career, back when she still believed in the straightforward mathematics of evidence. A plus B equals C. The guilty party, the motive, the means. It had taken her three years since Morris’s death to accept that some equations left remainders she couldn’t account for. “Quinn.” DC Patel materialized from behind a support pillar, his face drawn tight. He was young, twenty-seven, with the kind of eagerness that hadn’t yet been ground down by the weight of unsolved cases. He held a tablet in one hand, the screen casting blue shadows across his features. “They’re set up about fifty meters ahead. The tunnel curves east, there’s an old maintenance alcove. That’s where she is.” “She?” “Female. White. Mid-twenties, maybe younger. No identification on the body.” Patel fell into step beside her, his longer stride shortened to match her measured pace. “Called in forty minutes ago. Anonymous tip from a phone box in Camden. Said there was a body in the old Mornington Crescent spur. Uniforms thought it was a prank until they found the access hatch pried open.” Harlow’s torch beam swept across the curved wall of the tunnel, catching the gleam of old ceramic tiles beneath layers of grime. Something had been painted there once, a mural or an advertisement, reduced now to ghostly fragments. She didn’t like tunnels. Too many exits, too few. The air pressed in from all sides. “Who’s on scene?” “Forensics is already working. Singh’s the lead. And, ah.” Patel hesitated, the pause telling her everything she needed to know before he finished the sentence. “There’s a woman. Civilian. Says she’s a researcher with the British Museum. Found her down here when we arrived.” Harlow stopped walking. “You found a civilian at a sealed crime scene, and she’s still present?” “She had credentials. And she knew things.” Patel’s jaw worked, as if he were chewing on words he didn’t quite believe. “Said she’d been tracking unusual energy signatures in the area. Geological anomalies. That’s why she was down here. She claims she arrived before we did.” “Before the anonymous tip?” “Yes.” Harlow resumed walking. Her right hand drifted to her left wrist, her thumb finding the worn leather band of her watch. She pressed it once, twice, a habit she’d developed the year Morris didn’t come back. The leather was soft as skin. It grounded her. The crime scene lights came into view around the next bend, harsh white LEDs mounted on portable stands that threw the tunnel into sharp relief. The victim lay in the maintenance alcove, a narrow recess barely three feet deep set into the tunnel wall. Her body was positioned with deliberate care, arms folded across her chest, legs straight, head tilted slightly to the left as if she were merely sleeping. But the skin had gone waxy and pale, and the dark stain spreading beneath her told a different story. Singh looked up from his kit as Harlow approached. He was a compact man with precise hands and a face that gave away nothing. “Detective. Interesting one, this.” “How interesting?” “No visible wounds. No ligature marks. No signs of struggle.” He gestured at the body with a gloved hand. “But she’s lost approximately three liters of blood. I found a single puncture mark behind her left ear, very small, almost surgical. The blood pooled beneath her, but there’s no spatter, no trail. It’s as if she lay down here and simply bled out without moving.” Harlow crouched at the edge of the alcove, studying the victim’s face. Young, as Patel had said. Freckled complexion. Curly red hair spread across the concrete like a halo. Round glasses had been placed beside her head, folded neatly. A worn leather satchel rested against the alcove wall. Something cold settled in Harlow’s stomach. “The satchel,” she said. “Has it been examined?” “Not yet. We were waiting for you.” “Detective Quinn?” The voice came from behind her, a woman’s voice, slightly breathless. Harlow turned. The civilian was younger than she’d expected, perhaps twenty-six, with the same red hair as the victim and green eyes that looked like they hadn’t slept in days. She clutched a small notebook to her chest, her knuckles white. Freckles dusted her nose and cheeks. A nervous hand rose to tuck a strand of hair behind her left ear. “I’m Eva Kowalski,” she said. “I’m a research assistant at the British Museum. I specialize in occult artifacts and esoteric texts. And I know you have no reason to believe me, but I need you to listen. This isn’t what it looks like.” Harlow rose to her feet, her posture shifting into something she’d learned from years of interrogations. Open but alert. “And what does it look like, Ms. Kowalski?” “A murder. Obviously.” Eva took a step closer, stopped when Patel moved to intercept. “But the method isn’t human. The blood loss without a wound, the positioning of the body, the location — this is a ritual. A transference. Someone used her blood to power something, and they chose this place because it’s a convergence point.” “A convergence point for what?” Eva’s eyes darted toward Patel, then back to Harlow. “I can show you. If you let me.” Harlow studied her for a long moment. The woman was terrified, that much was clear, but there was something else beneath the fear. Something that looked almost like recognition. “You knew the victim,” Harlow said. It wasn’t a question. Eva flinched. “Her name is Aurora. She’s my best friend. We grew up together. She moved to London six months ago to be closer to me.” Her voice cracked on the last word, and for a moment the composure slipped, revealing a grief so raw it looked like a wound. “I’ve been looking for her for three days. She went missing on Tuesday. When I couldn’t find her through normal channels, I used other methods. Methods that led me here.” Patel shifted his weight, his expression caught between skepticism and something else. “You’re saying you tracked your missing friend to an abandoned Tube station using occult methods, and you arrived before our tipster even called?” “Yes.” Eva didn’t blink. “Because the tipster was me.” Silence stretched across the tunnel. Harlow heard water dripping somewhere in the darkness, a slow, rhythmic sound like a metronome. Singh had stopped working, his attention fixed on the conversation. The crime scene lights hummed. “The phone box in Camden,” Harlow said. “That was you.” “I couldn’t explain how I knew she was here. I still can’t, not in a way you’d accept. But I needed someone to find her. To bring her back to the surface. She deserves better than to be left down here.” Eva’s hand rose to her hair again, the nervous gesture automatic. “And I needed to see the scene before it was disturbed. Before your people moved her and erased what was left.” “What was left of what?” Eva reached into her satchel and withdrew a small brass compass. Its casing was mottled with verdigris, and its face was etched with symbols Harlow didn’t recognize. The needle spun lazily counterclockwise, then settled, pointing directly at the alcove where the body lay. “The Veil Compass,” Eva said. “I bought it three months ago at a market that doesn’t exist on any map. It points toward supernatural energy. Rifts, portals, places where the boundary between worlds has worn thin.” She held it out, her hand trembling slightly. “The needle shouldn’t be pointing at her. It points at locations, not people. Unless the person has been in contact with something. Something that left a trace.” Harlow took the compass. The brass was cool against her palm, heavier than it should have been. The needle twitched, resettled, still pointing at the body. At Aurora. Morris’s face flickered through her mind. The way he’d looked at the end, his eyes fixed on something she couldn’t see. The paramedics had said cardiac arrest. They’d been wrong. She’d known it then, and she knew it now. She handed the compass back to Eva. “DC Patel, I want a full background check on Ms. Kowalski. I want Aurora’s full name, address, employment history, everything you can find. I want this tunnel swept for trace evidence from end to end. And I want the satchel examined before we move the body.” “You believe me?” Eva’s voice was barely a whisper. “I believe the evidence doesn’t add up,” Harlow said. “A woman bleeds out without a wound. A friend finds the body before the police do, using a compass that points at things that shouldn’t exist. An anonymous tip from a phone box in Camden, placed by the very person standing in front of me, who somehow gained access to a station that’s been sealed for eighty years.” She looked at Eva, her brown eyes steady. “I believe something is wrong with this picture. And I intend to find out what.” She turned to Singh. “The puncture behind her ear. I want it photographed from every angle. I want the wound measured, swabbed, and catalogued. If there’s something in her bloodstream that shouldn’t be there, I want to know.” “Detective.” Singh hesitated. “There’s one more thing. When I was examining the body, I found this.” He held up a small evidence bag. Inside was a token, roughly the size of a coin, carved from what looked like bone. Its surface was marked with symbols similar to those on Eva’s compass. Eva went pale. “That’s a Veil Market token. It’s how you get in. She must have visited the Market before she died. She must have bought something, or learned something, or—” “Or someone followed her out,” Harlow finished. “Someone who wanted whatever she had.” The tunnel seemed to grow colder, the darkness pressing closer around the pool of artificial light. Harlow looked down at the victim — at Aurora — and saw what she’d missed before. A faint residue on the fingertips, dark and flaking. Not blood. Something else. Something that glinted under the crime scene lights like powdered glass. “Bag her hands,” she said. “Both of them. And I want analysis on that residue before the end of shift.” Eva was still staring at the token, her green eyes fixed on the bone-white surface. “There’s more,” she said. “Things I haven’t told you. About the Market, about what they sell there, about what’s been happening in London for the past six months. Disappearances. Strange deaths. Cases that go nowhere because the evidence doesn’t make sense.” She looked up, meeting Harlow’s gaze. “Cases like this one.” Harlow felt the shift in her chest. The cold certainty that had been building since she’d stepped off the Tube carriage, since she’d seen the body arranged like a sleeping girl in a fairy tale, since she’d held a compass that pointed at things science couldn’t explain. Three years ago, she’d watched her partner die without understanding why. She’d buried him without answers. She’d spent every day since pretending the world still followed rules she recognized. The rules had changed. She was only now beginning to understand how much. “Ms. Kowalski,” she said. “I’m going to need you to tell me everything.”
created_at"2026-04-25T15:04:33.736Z" 4/25/2026, 3:04:33 PM
model"deepseek/deepseek-v4-pro-20260423"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1100
moderation_latency(null)
generation_time76329
tokens_prompt923
tokens_completion3124
native_tokens_prompt857
native_tokens_completion2651
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached768
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"
usage0.002372925
router(null)
provider_responses
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is_byokfalse
latency302
model_permaslug"deepseek/deepseek-v4-pro-20260423"
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
totalTags23
adverbTagCount0
adverbTags(empty)
dialogueSentences60
tagDensity0.383
leniency0.767
rawRatio0
effectiveRatio0
84.74% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1966
totalAiIsmAdverbs6
found
0
adverb"slightly"
count3
1
adverb"very"
count2
2
adverb"lazily"
count1
highlights
0"slightly"
1"very"
2"lazily"
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)
72.02% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1966
totalAiIsms11
found
0
word"chill"
count1
1
word"weight"
count2
2
word"measured"
count2
3
word"stomach"
count1
4
word"silence"
count1
5
word"rhythmic"
count1
6
word"etched"
count1
7
word"flickered"
count1
8
word"whisper"
count1
highlights
0"chill"
1"weight"
2"measured"
3"stomach"
4"silence"
5"rhythmic"
6"etched"
7"flickered"
8"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
emotionTells1
narrationSentences110
matches
0"was terrified"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences110
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)
analyzedSentences145
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen66
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1969
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions20
unquotedAttributions0
matches(empty)
33.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions56
wordCount1123
uniqueNames10
maxNameDensity1.87
worstName"Harlow"
maxWindowNameDensity4
worstWindowName"Eva"
discoveredNames
Harlow21
Quinn1
Tube2
Blitz1
Morris3
Patel8
Eva13
Silence1
Aurora2
Singh4
persons
0"Harlow"
1"Quinn"
2"Blitz"
3"Morris"
4"Patel"
5"Eva"
6"Silence"
7"Singh"
places
0"Aurora"
globalScore0.565
windowScore0.333
16.67% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences75
glossingSentenceCount4
matches
0"quite believe"
1"looked like they hadn’t slept in days"
2"looked like a wound"
3"looked like bone"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1969
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount2
totalSentences145
matches
0"accept that some"
1"terrified, that much"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs55
mean35.8
std26.21
cv0.732
sampleLengths
078
174
269
31
463
554
63
744
816
946
104
111
1245
1389
1428
152
1665
1748
186
198
207
2115
2259
2343
2427
2556
265
2717
2829
2910
3080
3135
329
3335
3410
3573
365
3743
3871
3928
4039
4153
429
4390
4438
4516
4635
4731
4813
4956
82.93% Passive voice overuse
Target: ≤2% passive sentences
passiveCount7
totalSentences110
matches
0"been closed"
1"been ground"
2"been painted"
3"was positioned"
4"been placed"
5"was terrified"
6"was mottled"
7"was etched"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs198
matches
0"were merely sleeping"
1"was still staring"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences1
totalSentences145
ratio0.007
matches
0"Harlow looked down at the victim — at Aurora — and saw what she’d missed before."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1124
adjectiveStacks0
stackExamples(empty)
adverbCount38
adverbRatio0.033807829181494664
lyAdverbCount15
lyAdverbRatio0.013345195729537367
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences145
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences145
mean13.58
std11.81
cv0.87
sampleLengths
018
115
215
330
422
520
65
77
820
913
1021
1115
1220
131
1425
1538
1622
1716
184
195
207
213
2225
2319
243
2513
2621
2725
284
291
303
3118
3215
336
343
3525
3619
3726
3819
399
4015
414
422
4319
4446
4512
465
472
4810
4910
57.47% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.38620689655172413
totalSentences145
uniqueOpeners56
35.84% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences93
matches
0"Too many exits, too few."
ratio0.011
99.57% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences93
matches
0"Her shoes, sensible flats with"
1"She’d learned to move quietly"
2"It had taken her three"
3"He was young, twenty-seven, with"
4"He held a tablet in"
5"She didn’t like tunnels."
6"Her right hand drifted to"
7"She pressed it once, twice,"
8"It grounded her."
9"Her body was positioned with"
10"He was a compact man"
11"He gestured at the body"
12"She clutched a small notebook"
13"It wasn’t a question."
14"Her voice cracked on the"
15"Its casing was mottled with"
16"She held it out, her"
17"They’d been wrong."
18"She’d known it then, and"
19"She handed the compass back"
ratio0.301
8.39% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount84
totalSentences93
matches
0"Detective Harlow Quinn stepped off"
1"The platform stretched empty in"
2"This station had been closed"
3"Her shoes, sensible flats with"
4"She’d learned to move quietly"
5"A plus B equals C."
6"The guilty party, the motive,"
7"It had taken her three"
8"DC Patel materialized from behind"
9"He was young, twenty-seven, with"
10"He held a tablet in"
11"Patel fell into step beside"
12"Harlow’s torch beam swept across"
13"Something had been painted there"
14"She didn’t like tunnels."
15"The air pressed in from"
16"Patel hesitated, the pause telling"
17"Harlow stopped walking."
18"Patel’s jaw worked, as if"
19"Harlow resumed walking."
ratio0.903
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences93
matches(empty)
ratio0
59.80% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences43
technicalSentenceCount5
matches
0"Yet here she was, her breath misting in the chill, the beam of her torch cutting through the darkness toward a cluster of figures two hundred yards down the tra…"
1"He was young, twenty-seven, with the kind of eagerness that hadn’t yet been ground down by the weight of unsolved cases."
2"The crime scene lights came into view around the next bend, harsh white LEDs mounted on portable stands that threw the tunnel into sharp relief."
3"Her body was positioned with deliberate care, arms folded across her chest, legs straight, head tilted slightly to the left as if she were merely sleeping."
4"The civilian was younger than she’d expected, perhaps twenty-six, with the same red hair as the victim and green eyes that looked like they hadn’t slept in days…"
38.04% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags23
uselessAdditionCount4
matches
0"Patel’s jaw worked, as if he were chewing on words he didn’t quite believe"
1"The voice came, a woman’s voice, slightly breathless"
2"She held, her hand trembling slightly"
3"She looked, her brown eyes steady"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags9
fancyCount0
fancyTags(empty)
dialogueSentences60
tagDensity0.15
leniency0.3
rawRatio0
effectiveRatio0
78.9605%