Run: 2026-04-24-01KPZV8MED2TSYFHT4QFFQNFQG

Run Details

Duration:
26.4s
Cost:
$0.054445
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
Rain came down in sheets, turning the Camden pavement into a black mirror that swallowed the streetlamps and spit them back up sideways. Quinn was running. She had her radio in one hand and the other free for her baton, though she hadn't drawn it yet. The coat was slowing her — sodden at the hem, heavy as chainmail across the shoulders — but she wasn't about to shed it. The coat had the notebook in it. The notebook had the names. Ahead of her, forty yards and closing, Tomás Herrera's dark curls bobbed under the sodium glare of a shuttered kebab shop, then vanished around a corner. He ran like a man who knew where he was going. That was the part that bothered her. Suspects in a proper panic ran in lines that broke on every obstacle, bins and bollards and the wrong end of alleys. Herrera was cutting left where left was right, vaulting the low wall by the bookmaker's without breaking stride, finding the seams in the city like someone reading a map she couldn't see. "Quinn to dispatch." Her voice came out level; she was proud of that. "Foot pursuit, male IC3, late twenties, curly dark hair, navy jacket. Heading north on Kentish Town Road, approaching the overground station. Requesting backup." The radio coughed static. "Say again, Detective, you're breaking up." She wasn't breaking up. The radio was. Three years on the job and she could tell the difference between bad signal and something else — something that crowded the frequencies when she got near certain people, certain places, the way a storm pressed down on the ears before it broke. The same static she'd heard the night Morris had gone through a door that hadn't been there in the morning. She swallowed the thought. Put her head down. Ran faster. Herrera cut across the street, slipped on the painted white of a zebra crossing, caught himself on his palms and was up again before the Uber behind him had finished its horn. Quinn saw the flash of something at his throat — a medal on a chain, silver in the wet light — and then he was gone into the dark mouth of a side road she didn't know the name of. She followed. Brick walls narrowed around her. The rain sounded different here, close-walled, doubled. She could smell hot oil from a vent and cold water from everywhere else. Her boots hit the slick and she corrected with the easy mechanics of eighteen years of pursuit, weight low, hips loose, breath in through the nose. Somewhere ahead he was still running — she could hear his soles slap — and then, abruptly, she couldn't. Quinn pulled up. Listened. The alley ended in a padlocked gate. To the left, bins. To the right, a recessed doorway that shouldn't have been there, because she knew this part of Camden well enough. And beyond the doorway, a set of stairs going down, slick with rain that had followed them in, and on the first step — unmistakable — a dark spatter the size of a ten-pence piece. Blood. She keyed the radio. "Dispatch, suspect entered a premises off—" She looked up for a street sign. There wasn't one. "Off Kentish Town Road, unmarked door, possibly a disused Underground entrance. Camden area. Going in." "Detective, your position is not reading. Hold for—" She clipped the radio to her belt and drew the baton. The stairs went down farther than they should have. Tiled walls, the pale green of every Tube station built before the war. Posters peeling in strips, advertising cigarettes that weren't sold anymore, a Royal Command Performance from 1952. The rain sound thinned to nothing about twenty steps down, and what replaced it was a low hum she felt more than heard, like being inside a cello. At the bottom, a curtain. Heavy, black, weighted at the hem. Quinn stood in front of it and, for the first time in a long while, was afraid. She had been afraid in the usual ways before. Knives. The man in Hackney with the shotgun. The stairwell in Peckham that had taught her always to go up second. Those were fears with shapes. This was the fear she had avoided naming for three years, the thin ugly one that had sat under her ribs every time she'd tried to write Morris's report and found the words wouldn't stay on the page. A bone token lay on the floor at her feet. Small, yellowed, carved with a symbol she didn't recognize. It had not been there a second ago, or if it had, she had missed it, which was worse, because Quinn did not miss things. She crouched. She did not pick it up. Voices, on the other side of the curtain. Not shouting. The low murmur of a crowd. Somewhere in it, a bell rang, clear and small, and the murmur rose for a moment and then fell. She thought about the notebook in her coat. The names in it. Herrera's name, third from the top, because he was the softest — ex-paramedic, nothing to lose left to lose, the kind of man who would talk if you found the right pressure. She thought about the scar she'd seen on his forearm when he'd rolled up his sleeve at the interview two weeks ago, and the Saint Christopher at his throat, patron of travellers, of those moving between places. She thought about Morris. About the static. About the door in Whitechapel that hadn't been there in the morning. Protocol said wait for backup. Protocol said secure the perimeter. Protocol said a lot of things, and Quinn had built a career on the ones she kept and the ones she didn't. She straightened. Adjusted the worn leather of her watchstrap on her left wrist, a small habit, a tell she'd never managed to break. The face read 11:47. The second hand was going backwards. She looked at it for a long moment. Then she reached down, picked up the bone token, closed her fist around it, and pushed through the curtain. The hum swallowed her whole.
created_at"2026-04-24T13:35:58.939Z" 4/24/2026, 1:35:58 PM
model"anthropic/claude-4.7-opus-20260416"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1157
moderation_latency(null)
generation_time26351
tokens_prompt914
tokens_completion1478
native_tokens_prompt1269
native_tokens_completion1924
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)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"end_turn"
usage0.054445
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provider_responses
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is_byokfalse
latency1157
model_permaslug"anthropic/claude-4.7-opus-20260416"
provider_name"Amazon Bedrock"
status200
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request_id"req-1777037758-RV1XWDc3NEJDrPDmk6kz"
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provider_name"Amazon Bedrock"
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Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences6
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1017
totalAiIsmAdverbs0
found(empty)
highlights(empty)
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)
95.08% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1017
totalAiIsms1
found
0
word"weight"
count1
highlights
0"weight"
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
emotionTells2
narrationSentences78
matches
0"was proud"
1"was afraid"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount2
narrationSentences78
filterMatches(empty)
hedgeMatches
0"tried to"
1"managed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences82
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1027
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions29
wordCount966
uniqueNames16
maxNameDensity0.62
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Camden2
Tomás1
Herrera4
Morris3
Uber1
Tube1
Royal1
Command1
Performance1
Hackney1
Peckham1
Quinn6
Saint1
Christopher1
Whitechapel1
Protocol3
persons
0"Tomás"
1"Herrera"
2"Morris"
3"Quinn"
4"Saint"
5"Christopher"
6"Protocol"
places
0"Hackney"
1"Peckham"
2"Whitechapel"
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
wordCount1027
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences82
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs29
mean35.41
std29.22
cv0.825
sampleLengths
026
156
298
336
410
57
663
710
872
92
1071
114
1266
131
1435
158
1611
1766
1811
1917
2073
2144
228
2335
24100
2532
2633
2727
285
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences78
matches
0"was gone"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount6
totalVerbs151
matches
0"was running"
1"was slowing"
2"was going"
3"wasn't breaking"
4"was still running "
5"was going"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount10
semicolonCount1
flaggedSentences7
totalSentences82
ratio0.085
matches
0"The coat was slowing her — sodden at the hem, heavy as chainmail across the shoulders — but she wasn't about to shed it."
1"\"Quinn to dispatch.\" Her voice came out level; she was proud of that."
2"Three years on the job and she could tell the difference between bad signal and something else — something that crowded the frequencies when she got near certain people, certain places, the way a storm pressed down on the ears before it broke."
3"Quinn saw the flash of something at his throat — a medal on a chain, silver in the wet light — and then he was gone into the dark mouth of a side road she didn't know the name of."
4"Somewhere ahead he was still running — she could hear his soles slap — and then, abruptly, she couldn't."
5"And beyond the doorway, a set of stairs going down, slick with rain that had followed them in, and on the first step — unmistakable — a dark spatter the size of a ten-pence piece."
6"Herrera's name, third from the top, because he was the softest — ex-paramedic, nothing to lose left to lose, the kind of man who would talk if you found the right pressure."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount958
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.025052192066805846
lyAdverbCount2
lyAdverbRatio0.0020876826722338203
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences82
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences82
mean12.52
std10.61
cv0.847
sampleLengths
023
13
220
324
47
55
626
711
87
922
1032
1113
1223
134
146
154
163
1743
1820
194
204
212
2232
2340
242
255
267
2714
2826
2919
303
311
327
334
3420
3535
361
374
3813
393
4015
418
4211
439
4413
4516
4628
475
486
4917
71.14% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.5
totalSentences82
uniqueOpeners41
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences70
matches
0"Somewhere ahead he was still"
1"Somewhere in it, a bell"
2"Then she reached down, picked"
ratio0.043
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences70
matches
0"She had her radio in"
1"He ran like a man"
2"Her voice came out level;"
3"She wasn't breaking up."
4"She swallowed the thought."
5"She could smell hot oil"
6"Her boots hit the slick"
7"She keyed the radio."
8"She looked up for a"
9"She clipped the radio to"
10"She had been afraid in"
11"It had not been there"
12"She did not pick it"
13"She thought about the notebook"
14"She thought about the scar"
15"She thought about Morris."
16"She looked at it for"
ratio0.243
95.71% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount51
totalSentences70
matches
0"Rain came down in sheets,"
1"Quinn was running."
2"She had her radio in"
3"The coat was slowing her"
4"The coat had the notebook"
5"The notebook had the names."
6"He ran like a man"
7"That was the part that"
8"Herrera was cutting left where"
9"Her voice came out level;"
10"The radio coughed static."
11"She wasn't breaking up."
12"The radio was."
13"The same static she'd heard"
14"She swallowed the thought."
15"Herrera cut across the street,"
16"Quinn saw the flash of"
17"Brick walls narrowed around her."
18"The rain sounded different here,"
19"She could smell hot oil"
ratio0.729
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences70
matches
0"To the left, bins."
1"To the right, a recessed"
ratio0.029
31.25% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount5
matches
0"Rain came down in sheets, turning the Camden pavement into a black mirror that swallowed the streetlamps and spit them back up sideways."
1"Three years on the job and she could tell the difference between bad signal and something else — something that crowded the frequencies when she got near certai…"
2"And beyond the doorway, a set of stairs going down, slick with rain that had followed them in, and on the first step — unmistakable — a dark spatter the size of…"
3"Posters peeling in strips, advertising cigarettes that weren't sold anymore, a Royal Command Performance from 1952."
4"Herrera's name, third from the top, because he was the softest — ex-paramedic, nothing to lose left to lose, the kind of man who would talk if you found the rig…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
89.7729%