Run: 2026-07-25-01KYCR7CZWX3WPER5JQX841NR9

Run Details

Duration:
56.9s
Cost:
$0.084625
Provider:
Amazon Bedrock

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 stairwell smelled of iron and cold stone, and Quinn counted the steps down out of habit. Forty-two. Camden Town's disused southbound platform sat sixty feet under the pavement, sealed since 1924, and the fire door at the bottom had been cut open with an angle grinder sometime in the last twelve hours. The burrs on the metal still had a shine. "Mind the rail, ma'am." DC Femi Adeyemi held the door with his shoulder, torch tucked under his arm. "Uniform's got a chain of custody sorted but the place is a nightmare. No lighting. Generator's on its way." "Who found him?" "Urban explorer. Twenty-two-year-old with a GoPro and a hobby. He's upstairs being sick into a evidence bag, which I've asked him three times not to do." Quinn stepped through onto the platform and stopped. Her torch beam swept tile the colour of old teeth, the pale ghost of a Nestlé advert from another century, and dust. That was the first thing. The dust lay in a skin across the platform, an even grey felt that had been settling for a hundred years, and it was wrong. Not the dust itself. The pattern in it. "Body's down here," Femi said, moving off. "Wait." He waited. Quinn crouched, elbows on her knees, and angled the light low across the floor so the beam skimmed it. Shadows raked out long. In the dust, running the length of the platform, lay two parallel lines of small rectangular voids — clean spots, sharp-edged, each about the size of a paperback. They marched in rows. Dozens of them. Between the rows, the dust had been scuffed into a broad path by feet. Many feet. "Tables," she said. "Sorry?" "Trestle legs. Two rows, facing each other, with an aisle between." She stood, and her knees complained about it. "Somebody had a car boot sale down here." Femi laughed, then stopped laughing when she didn't. "Squatters? Rave? We get raves in the deep stations. Kids run cables off the maintenance supply." "A rave leaves cans. Nitrous canisters. Fag ends." She turned a slow circle. "Look at the floor and tell me what you see." He looked. "Nothing." "Nothing," she agreed. "Somebody swept up. Somebody who set out ninety-odd tables in a sealed tunnel and then went round afterwards collecting their rubbish." The body lay eighteen metres along, at the mouth of a cross-passage, and the first uniform on scene had done the sensible thing and stood well back with a clipboard. Quinn snapped fresh gloves and crouched. Male, fifties, thin, dressed in a wool coat that had cost money in about 1988. Face down, left cheek against the tile, arms drawn up under him. "So," Femi said, and she heard him settle into it, the way young detectives did when they'd already written the report in their head. "Working theory. He's part of it — the market, whatever it is. Deal goes wrong. Someone puts a blade in him, everyone scatters, they clear the stalls to strip the scene. That's why it's swept. It's not tidiness, it's cleaning." "Where's the blade wound?" "Under him, I'd guess. There's a lot of blood." Quinn moved her torch. There was a lot of blood. It had spread from beneath the body in a broad, dark lake, and the edges had dried to a lacquer, and that was the second thing that was wrong. "Femi. Come here. Get down." He got down. "Which way does the platform slope?" He hesitated. "Towards the tunnel mouth. There's a drainage fall, they all have it." "So." She traced the air an inch above the surface. "He's lying on the high side. Blood runs downhill. Water does, wine does, blood does. This ran uphill." Her finger followed the lake to its far margin, where the dark tongue reached three feet up the slope and stopped in a crescent, as if it had lapped against something and been turned back. "And then it stopped. Not soaked in, not spread thin. Stopped. There's a line." Femi put his torch beam on it. The crescent held. Beyond it, clean tile. "Surface tension," he tried. "On unglazed hundred-year-old tile with a groove in it every four inches." She stood and walked the crescent, following it out from the body in a curve. It kept curving. It closed on itself. A circle, near enough seven feet across, with the dead man inside it, and the blood pushed up against the inner edge all the way round like a tide against a harbour wall. "That's—" Femi said, and stopped. "Say it." "That's a containment. That's a circle. Cult stuff. Chalk, salt, whatever, and they've lifted it after, and the blood's set into the—" He was talking faster. "There'd be residue. Ma'am, there'd be residue, we can swab the line—" "Swab it." She was already moving. "And Femi. Look at his shoes." The dead man's shoes were brown brogues, resoled, laced tight. The uppers were clean. The soles were clean. Femi crouched, tipped his head, and she watched him get there. "There's no dust on him." "Forty-two steps and eighteen metres of century-old filth," Quinn said. "I've got a tidemark on my trousers to the knee. Yours are worse. His are pressed." "So he was carried." "By whom? Show me the drag marks. Show me two sets of prints coming in heavy and going out light." She swung the torch back across the floor around the body. The scuffed path of feet ran up the aisle and past — and stopped, ten feet short, as if the crowd had flowed around the circle without ever crossing it. Inside the circle the dust was undisturbed except where the blood had gone. No prints. Not one. Not even his. Femi was quiet for a while. The generator coughed somewhere far above, failed, coughed again. "Ma'am, there's a version of this where I don't have to write down what I'm looking at." "Give me it." "He came down here on his own. Something in his coat, drugs, whatever, seized him, he went over, he bled, and the airflow off the tunnel — there's a draught, feel it — the airflow dried the blood in a pattern that reads like a circle because we're standing here at four in the morning wanting it to read like something. The stalls are a rave. The urban explorer contaminated the floor. And I put it to the coroner as unexplained and go home." He rubbed his face. "That version's got a lot going for it." "It's got everything going for it except the floor." Quinn took her torch off the body and put it on the tile just outside the circle's northern arc, where a delicate white bloom spread across the ceramic in feathers and needles. She held her bare palm above it. The air bit. "That's frost," Femi said. "It's the eleventh of June." Something clicked under the man's chest when she eased his shoulder, and she stopped, and photographed, and eased again. His right hand had closed hard in death around two objects. The first was a flat disc of yellowed bone, drilled once at the edge, worn smooth as a tumbled pebble. The second was a brass compass, small enough to hide in a fist, its casing furred green with verdigris, its face cut all over with marks that were not letters and not numbers. The needle was turning. Slowly, steadily, without stopping, like a thing looking for something it couldn't find. Quinn held very still and watched it go round, and round, and round, and thought about a night three years ago on a canal towpath in Hackney, and about DS Morris, and about the eleven minutes of her life she had never been able to account for. "Femi." "Yeah." "Don't log that."
created_at"2026-07-25T13:42:53.188Z" 7/25/2026, 1:42:53 PM
model"anthropic/claude-opus-5-20260723"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency4790
moderation_latency(null)
generation_time56775
tokens_prompt1112
tokens_completion2181
native_tokens_prompt1595
native_tokens_completion3066
native_tokens_completion_images(null)
native_tokens_reasoning323
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"end_turn"
service_tier"default"
usage0.084625
router(null)
provider_responses
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endpoint_id"76cb4608-f48c-483d-8da8-9957fb44244e"
id"msg_011CdNsdQLHZ91koyJCNCwZs"
is_byokfalse
latency1709
model_permaslug"anthropic/claude-opus-5-20260723"
provider_name"Amazon Bedrock"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.18.0; linux; x64))"
http_referer(null)
request_id"req-1784986973-0tdZetb9BQq64xdFJA7Y"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1784986973-9aUDk8ON7IkP2TIuzwyB"
upstream_id"msg_011CdNsdQLHZ91koyJCNCwZs"
total_cost0.084625
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"
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
totalTags17
adverbTagCount0
adverbTags(empty)
dialogueSentences49
tagDensity0.347
leniency0.694
rawRatio0
effectiveRatio0
92.27% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1294
totalAiIsmAdverbs2
found
0
adverb"slowly"
count1
1
adverb"very"
count1
highlights
0"slowly"
1"very"
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)
92.27% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1294
totalAiIsms2
found
0
word"traced"
count1
1
word"tension"
count1
highlights
0"traced"
1"tension"
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
narrationSentences74
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences74
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences104
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen86
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1299
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions15
unquotedAttributions0
matches(empty)
94.44% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount810
uniqueNames7
maxNameDensity1.11
worstName"Femi"
maxWindowNameDensity2
worstWindowName"Femi"
discoveredNames
Quinn8
Town1
Femi9
Adeyemi1
Nestlé1
Hackney1
Morris1
persons
0"Quinn"
1"Femi"
2"Adeyemi"
3"Morris"
places
0"Town"
1"Hackney"
globalScore0.944
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1299
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences104
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs52
mean24.98
std25.63
cv1.026
sampleLengths
062
137
23
326
48
560
67
71
876
93
101
1127
1224
1323
143
1524
1663
1764
184
199
2039
215
223
236
2414
2577
2614
274
2867
295
302
3138
3212
3329
345
3526
364
3781
3815
3917
403
4196
4251
434
445
4550
4633
4717
4847
491
95.78% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences74
matches
0"been scuffed"
1"been turned"
52.94% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs136
matches
0"was talking"
1"was already moving"
2"was turning"
87.91% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount2
semicolonCount0
flaggedSentences2
totalSentences104
ratio0.019
matches
0"In the dust, running the length of the platform, lay two parallel lines of small rectangular voids — clean spots, sharp-edged, each about the size of a paperback."
1"The scuffed path of feet ran up the aisle and past — and stopped, ten feet short, as if the crowd had flowed around the circle without ever crossing it."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount811
adjectiveStacks0
stackExamples(empty)
adverbCount27
adverbRatio0.03329223181257707
lyAdverbCount2
lyAdverbRatio0.002466091245376079
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences104
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences104
mean12.49
std13.38
cv1.071
sampleLengths
017
11
235
39
418
519
63
726
88
922
105
1125
124
134
147
151
162
1719
184
1928
204
213
2214
232
243
251
2619
278
288
2916
3013
3110
322
331
343
3521
3630
376
3815
3912
4024
4140
424
439
444
456
4629
475
483
496
84.62% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.5480769230769231
totalSentences104
uniqueOpeners57
53.76% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences62
matches
0"Slowly, steadily, without stopping, like"
ratio0.016
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences62
matches
0"Her torch beam swept tile"
1"They marched in rows."
2"She stood, and her knees"
3"She turned a slow circle"
4"It had spread from beneath"
5"He got down."
6"She traced the air an"
7"Her finger followed the lake"
8"She stood and walked the"
9"It kept curving."
10"It closed on itself."
11"He was talking faster"
12"She was already moving"
13"She swung the torch back"
14"He rubbed his face"
15"She held her bare palm"
16"His right hand had closed"
ratio0.274
40.65% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount52
totalSentences62
matches
0"The stairwell smelled of iron"
1"Camden Town's disused southbound platform"
2"The burrs on the metal"
3"DC Femi Adeyemi held the"
4"Quinn stepped through onto the"
5"Her torch beam swept tile"
6"That was the first thing."
7"The dust lay in a"
8"The pattern in it."
9"Femi said, moving off"
10"Quinn crouched, elbows on her"
11"Shadows raked out long."
12"They marched in rows."
13"Dozens of them."
14"She stood, and her knees"
15"Femi laughed, then stopped laughing"
16"She turned a slow circle"
17"The body lay eighteen metres"
18"Quinn snapped fresh gloves and"
19"Femi said, and she heard"
ratio0.839
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences62
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences22
technicalSentenceCount1
matches
0"It had spread from beneath the body in a broad, dark lake, and the edges had dried to a lacquer, and that was the second thing that was wrong."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags17
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags7
fancyCount1
fancyTags
0"she agreed (agree)"
dialogueSentences49
tagDensity0.143
leniency0.286
rawRatio0.143
effectiveRatio0.041
89.8215%