Run: 2026-02-18-01KHQ9364K4V149AQ0GWTPNZ6H

Run Details

Duration:
39.2s
Cost:
$0.029244
Provider:
Amazon Bedrock

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="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> 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.

Model response

AI
The rain came down in sheets, turning the Soho pavement into a mirror that threw back the red and amber of traffic lights in long, smeared ribbons. Detective Harlow Quinn moved fast, her boots splitting puddles, her eyes fixed on the figure thirty metres ahead who had just made the mistake of looking back. He'd broken when she'd shown her warrant card at the bar. One second he was nursing a whiskey at the counter of the Raven's Nest, the next he was through the side door and into the alley before she'd finished saying his name. "Herrera. Tomás Herrera. Stop — Metropolitan Police." He didn't stop. They never did. Quinn cut right around a cluster of tourists huddled under a restaurant awning, their confused faces a pale blur as she passed. She clocked Herrera at the end of the alley turning north onto a side street, his dark jacket soaked through, the Saint Christopher medallion she'd seen flash under the bar's light now lost to shadow and rain. She'd been watching him for three weeks. She'd built the profile carefully: former paramedic, NHS-struck-off, known associate of at least four people connected to the cluster of incidents she couldn't yet name properly in any official report. Incidents that left no forensic trail. Incidents that left her lying awake at night thinking about Morris. She pushed that thought down and ran harder. Herrera was fit. She'd noted that too. No excess weight, economical movement — he ran like someone who had needed to run before and had learned not to waste energy on panic. But Quinn had eighteen years of footwork on London streets and she knew the maze of Soho the way she knew her own handwriting. She cut through a narrow passage between two restaurants, emerged onto the parallel street, and had closed the gap to fifteen metres before he heard her behind him and broke into a full sprint. They went north. The crowds thinned. The neon advertising gave way to shuttered shop fronts and the occasional sick yellow of a streetlamp. Herrera vaulted a low bollard without breaking stride and Quinn went around it, the worn leather of her watch strap catching on her coat cuff, her left hand automatically pressing it flat — a habit, not a distraction. He was heading for Camden. She could feel it in the geography of his choices, always bearing north-northwest when he had options, never doubling back toward the busier streets where CCTV would be dense and helpful. He knew about cameras. Another note in the profile. Her radio crackled. Backup was six minutes out. Six minutes was nothing and everything. Herrera dropped suddenly, almost disappearing, and it took her half a second to understand he'd taken a staircase — concrete steps leading down into the forecourt of what looked like a derelict entrance, the kind of Victorian mouth the old Underground left scattered across the city like forgotten teeth. There was no active Tube line beneath this stretch. She knew that. She had maps in her head. Quinn hit the top of the stairs without slowing, her hand finding the iron railing, and descended into dark. At the bottom, a corridor. The emergency lighting here was wrong — not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, each one burning amber and casting the kind of light that made depth judgement difficult. She drew her extendable baton. Torch. She wanted her torch. She unclipped it with her left hand and swept the beam ahead. Herrera was twenty metres down the corridor and he'd stopped running. He stood with his back to her, breathing hard, hands at his sides. The corridor opened beyond him into something her torch beam couldn't fully reach. Sound reached her before her eyes made sense of it. Voices — dozens of them, overlapping, speaking languages she could identify and some she couldn't — and under that a smell: woodsmoke, something chemical she associated with hospital dispensaries, and something else, older, mineral, like wet stone and copper. Quinn moved forward slowly, baton extended. "Herrera. Hands where I can see them." He turned. In the torchlight his face was younger than his file suggested and older in the eyes, which were dark and exhausted and not, she noted, afraid of her. He raised both hands to chest height. The scar along his left forearm caught the light. "Detective," he said, his accent blurring the word softly. "You need to stop here." "I need you to turn around and put your hands behind your back." "I can't let you go in there." He nodded over his shoulder at the space she couldn't yet see properly. "Not without preparation. Not without a token." "I'll take my chances." "You won't, though." His voice carried no threat. That was what made her hesitate — the complete absence of threat in it, only a tired, careful certainty. "You'll go in there and you'll see things that will end your career and possibly your mind if you don't have context for them, and then I'll have done you worse harm than anything you think I've done." Quinn kept the torch on his face and listened to the sounds beyond him and felt the hairs on her forearms rise. She thought about the incident reports she'd been reading for three years. The ones she kept in a separate folder, not on the system. The ones that started with DS Morris. "What is that place?" she said. Herrera lowered his hands by a fraction. He seemed to read something in her face and chose his next words as carefully as sutures. "A market. It's underground. It moves — it won't be here by next full moon." He paused. "The people in there are not all people, Detective Quinn. That's not a threat, that's a fact, and I think some part of you already knows that facts like that are possible." The rain was a distant sound above them now. Her backup was four minutes out, maybe three. She thought about calling it in and immediately understood that she wouldn't. Calling it in meant explaining the paper lanterns and the copper smell and a former paramedic who spoke to her like she was a patient being told the diagnosis was serious but survivable. She looked past him into the amber dark, at the shapes moving through it, at the stalls draped in cloth that wasn't quite any colour she had a name for, at the figures bent over things she couldn't make out. "If I go in there," she said, "I want to go in standing up with my eyes open." Herrera was quiet for a moment. Then he reached into his jacket pocket and produced a small object which he held out on his open palm. Bone. Shaped and worn smooth. Small enough to close a fist around. "That's what it takes," he said. Quinn looked at it. She looked at him. She thought about Morris — about the way the file had been quietly closed, the way her superintendent had looked at her when she'd pushed, the way every rational explanation had worn thin and given way to something she'd refused to name for three years because naming it meant she'd been standing on uncertain ground all along. She reached out and took the token. It was warm in her hand in a way that had nothing to do with Herrera's body heat, and she registered that wrongness and filed it precisely, the way she filed everything — methodical, honest, without flinching. "You're still under caution," she said. Tomás Herrera almost smiled. "Understood, Detective." He turned and led her forward. "Stay close. Don't touch anything on the stalls without asking first. And don't accept any drink." Quinn followed him into the market, her torch still lit, her eyes moving the way they'd been trained to move — systematically, missing nothing — and her notebook already open in her mind, filling with things she didn't yet have language for.
created_at"2026-02-18T02:26:41.491689+00:00" 2/18/2026, 2:26:41 AM
model"anthropic/claude-4.6-sonnet-20260217"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency654
moderation_latency205
generation_time38895
tokens_prompt914
tokens_completion1990
native_tokens_prompt918
native_tokens_completion1766
native_tokens_completion_images(null)
native_tokens_reasoning0
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.029244
router(null)
provider_responses
0
id"3ab14011-327f-4dc5-a5a0-e1e1a8f9e83a"
status200
is_byokfalse
latency653
endpoint_id"83d802cb-44f2-45a0-a9d5-cb9bf79777c8"
provider_name"Amazon Bedrock"
model_permaslug"anthropic/claude-4.6-sonnet-20260217"
api_type"completions"
id"gen-1771381561-pNpFJdq2oE0RK58LnLWG"
upstream_id"3ab14011-327f-4dc5-a5a0-e1e1a8f9e83a"
total_cost0.029244
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags9
adverbTagCount0
adverbTags(empty)
dialogueSentences18
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
77.48% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1332
totalAiIsmAdverbs6
found
0
adverb"carefully"
count2
1
adverb"suddenly"
count1
2
adverb"slowly"
count1
3
adverb"softly"
count1
4
adverb"precisely"
count1
highlights
0"carefully"
1"suddenly"
2"slowly"
3"softly"
4"precisely"
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)
84.98% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1332
totalAiIsms4
found
0
word"weight"
count1
1
word"could feel"
count1
2
word"sense of"
count1
3
word"methodical"
count1
highlights
0"weight"
1"could feel"
2"sense of"
3"methodical"
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
narrationSentences83
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences83
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)
analyzedSentences92
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen56
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1342
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions12
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions35
wordCount1157
uniqueNames16
maxNameDensity0.78
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Soho2
Harlow1
Quinn9
Raven1
Nest1
Herrera9
Saint1
Christopher1
Morris3
London1
Camden1
Victorian1
Underground1
Tube1
TfL1
Tomás1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Herrera"
4"Saint"
5"Christopher"
6"Morris"
7"Victorian"
8"Underground"
9"Tomás"
places
0"Soho"
1"London"
globalScore1
windowScore1
59.09% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences55
glossingSentenceCount2
matches
0"looked like a derelict entrance, the kind"
1"quite any colour she had a name for, at the figures bent over things she couldn't make out"
50.97% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches2
per1kWords1.49
wordCount1342
matches
0"not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, e"
1"not municipal anything, but a series of small paper lanterns strung along the ceiling, e"
57.97% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount3
totalSentences92
matches
0"pushed that thought"
1"understood that she"
2"registered that wrongness"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs37
mean36.27
std25.96
cv0.716
sampleLengths
054
143
27
36
4113
58
690
761
845
914
1067
1119
1266
1337
1449
1513
1646
1714
1813
1927
204
2165
2253
236
2424
2549
2662
2740
2818
2938
306
3165
327
3337
346
3528
3642
96.81% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences83
matches
0"being told"
1"been trained"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs215
matches
0"was nursing"
1"was heading"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount11
semicolonCount0
flaggedSentences9
totalSentences92
ratio0.098
matches
0"No excess weight, economical movement — he ran like someone who had needed to run before and had learned not to waste energy on panic."
1"Herrera vaulted a low bollard without breaking stride and Quinn went around it, the worn leather of her watch strap catching on her coat cuff, her left hand automatically pressing it flat — a habit, not a distraction."
2"Herrera dropped suddenly, almost disappearing, and it took her half a second to understand he'd taken a staircase — concrete steps leading down into the forecourt of what looked like a derelict entrance, the kind of Victorian mouth the old Underground left scattered across the city like forgotten teeth."
3"The emergency lighting here was wrong — not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, each one burning amber and casting the kind of light that made depth judgement difficult."
4"Voices — dozens of them, overlapping, speaking languages she could identify and some she couldn't — and under that a smell: woodsmoke, something chemical she associated with hospital dispensaries, and something else, older, mineral, like wet stone and copper."
5"That was what made her hesitate — the complete absence of threat in it, only a tired, careful certainty."
6"She thought about Morris — about the way the file had been quietly closed, the way her superintendent had looked at her when she'd pushed, the way every rational explanation had worn thin and given way to something she'd refused to name for three years because naming it meant she'd been standing on uncertain ground all along."
7"It was warm in her hand in a way that had nothing to do with Herrera's body heat, and she registered that wrongness and filed it precisely, the way she filed everything — methodical, honest, without flinching."
8"Quinn followed him into the market, her torch still lit, her eyes moving the way they'd been trained to move — systematically, missing nothing — and her notebook already open in her mind, filling with things she didn't yet have language for."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1149
adjectiveStacks0
stackExamples(empty)
adverbCount34
adverbRatio0.02959094865100087
lyAdverbCount14
lyAdverbRatio0.012184508268059183
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences92
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences92
mean14.59
std12.7
cv0.871
sampleLengths
027
127
211
332
47
53
63
722
837
97
1030
116
1211
138
143
154
1625
1724
1834
193
203
2117
2238
235
2431
254
265
273
285
296
3049
319
323
336
3419
355
3639
375
381
394
4012
4111
4213
4313
4410
4539
466
477
482
4928
55.07% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats11
diversityRatio0.40217391304347827
totalSentences92
uniqueOpeners37
44.44% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences75
matches
0"Then he reached into his"
ratio0.013
38.67% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount34
totalSentences75
matches
0"He'd broken when she'd shown"
1"He didn't stop."
2"They never did."
3"She clocked Herrera at the"
4"She'd been watching him for"
5"She'd built the profile carefully:"
6"She pushed that thought down"
7"She'd noted that too."
8"She cut through a narrow"
9"They went north."
10"He was heading for Camden."
11"She could feel it in"
12"He knew about cameras."
13"Her radio crackled."
14"She knew that."
15"She had maps in her"
16"She drew her extendable baton."
17"She wanted her torch."
18"She unclipped it with her"
19"He stood with his back"
ratio0.453
26.67% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount65
totalSentences75
matches
0"The rain came down in"
1"Detective Harlow Quinn moved fast,"
2"He'd broken when she'd shown"
3"One second he was nursing"
4"He didn't stop."
5"They never did."
6"Quinn cut right around a"
7"She clocked Herrera at the"
8"She'd been watching him for"
9"She'd built the profile carefully:"
10"Incidents that left no forensic"
11"Incidents that left her lying"
12"She pushed that thought down"
13"Herrera was fit."
14"She'd noted that too."
15"She cut through a narrow"
16"They went north."
17"The crowds thinned."
18"The neon advertising gave way"
19"Herrera vaulted a low bollard"
ratio0.867
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences75
matches(empty)
ratio0
3.97% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences36
technicalSentenceCount7
matches
0"Detective Harlow Quinn moved fast, her boots splitting puddles, her eyes fixed on the figure thirty metres ahead who had just made the mistake of looking back."
1"Incidents that left her lying awake at night thinking about Morris."
2"Quinn hit the top of the stairs without slowing, her hand finding the iron railing, and descended into dark."
3"The emergency lighting here was wrong — not TfL yellow, not municipal anything, but a series of small paper lanterns strung along the ceiling, each one burning …"
4"In the torchlight his face was younger than his file suggested and older in the eyes, which were dark and exhausted and not, she noted, afraid of her."
5"It was warm in her hand in a way that had nothing to do with Herrera's body heat, and she registered that wrongness and filed it precisely, the way she filed ev…"
6"Quinn followed him into the market, her torch still lit, her eyes moving the way they'd been trained to move — systematically, missing nothing — and her noteboo…"
69.44% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags9
uselessAdditionCount1
matches
0"he said, his accent blurring the word softly"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount0
fancyTags(empty)
dialogueSentences18
tagDensity0.278
leniency0.556
rawRatio0
effectiveRatio0
75.5188%