Run: 2026-02-18-01KHQ9430J7ME9JYXAY02W5DGV

Run Details

Duration:
43.1s
Cost:
$0.032349
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 curtains. Quinn turned up the collar of her coat and pressed herself against the wet brickwork of a doorway on Dean Street, watching the green neon of The Raven's Nest bleed its colour across the slick pavement below. She'd been outside for forty minutes. Her leather watch told her it was quarter past eleven. The bar closed at midnight, and the man she was watching had gone in just after nine. She had a name. Tomás Herrera, twenty-nine years old, formerly of the NHS, currently of no fixed professional address. Three prior cautions, no convictions. A ghost on paper. In practice, something considerably more interesting. He'd been at the scene of two of the four incidents she was building a case around — "incidents" being the polite word for events that had left her with crime scene photographs she kept in a separate locked drawer in her flat, away from the official files, because she couldn't explain them and she didn't want anyone else trying to until she could. Morris would have known what to call them. Morris was three years dead, and she still heard his voice sometimes, usually at moments like this, standing in the rain outside a bar in Soho, telling her to think carefully before she pushed a door open. *You don't know what's on the other side, Harlow. You never do.* The door opened. Herrera came out at eleven-twenty, coat zipped to his chin, shoulders hunched against the weather. He wasn't running — not yet — but there was a purposeful tightness to his step that she recognised. He knew he was being watched, or suspected he was, which amounted to the same thing. She gave him the corner. Then she followed. He went north on Wardour Street, then cut east, moving quickly through the narrow arteries between Soho and Covent Garden. She kept a full block behind him, letting the pedestrian traffic — thin as it was in the rain — provide whatever cover was available. He didn't look back. People who didn't look back were either oblivious or very good. She'd stopped assuming oblivious about three years ago. He ducked into a cab. She swore under her breath, raised her hand, and by some minor miracle of London meteorology managed to flag the next one within forty seconds. "Follow that cab." She showed her warrant card to the driver's mirror. "Don't lose it." The driver, admirably, didn't say a word. They went north. Up through Holborn, through Bloomsbury still gleaming and deserted in the rain, and then across the Euston Road. The cab ahead of them turned into Camden, and she felt something shift in her attention — a tightening, the way the air felt before something broke open. Herrera's cab stopped on Chalk Farm Road, just past the bridge over the canal. She watched him pay the driver and stand for a moment on the pavement, doing what looked like nothing in particular, which meant he was checking his surroundings. She had her driver stop short and paid him without looking away from the figure in the rain. Herrera moved again. He turned off the main road into a narrow passage that ran along the back of a row of shuttered market stalls, the kind of Camden labyrinth that tourists got lost in by daylight. By night, in the rain, with the market closed and the security lights casting orange pools that left more shadow than light, it felt like a different city entirely. She followed him in. The passage bent twice, and she caught a glimpse of him near the second turn — a flash of dark coat, the gleam of something at his neck. A medallion of some kind. Then he was gone again, and she followed the sound of his footsteps over the wet stone. The footsteps stopped. She stopped too, pressing into shadow, listening. Rain on corrugated metal. Distant traffic. Nothing else. She moved to the place where she'd last heard him and found a section of hoarding — wooden panels covering what had once been a service entrance to the old station. Camden Town had redundant tunnels beneath it; she knew that much. Transport for London occasionally sent engineers down to check on them. She'd done enough years on the Met to know that various other people went down too, for various other reasons. One of the wooden panels was slightly ajar. She crouched and examined it without touching it. The gap was about two feet wide. Darkness beyond. And something else — a smell that didn't belong to rot or concrete or old earth. Something mineral and faintly sweet, like the air near a burning match, if the match had been burning something other than phosphorus. Morris had described a smell like that once. He'd been trying to explain something to her, the week before he died, and she'd told him to get some sleep. She hadn't listened properly. She'd been so sure there was a rational explanation. The sound that came from below was not easily rational. It was a low, uneven murmur, like a crowd but stranger — voices overlapping in rhythms that didn't match, in a space that seemed larger than the ground above could account for. She heard what might have been music. She heard something that was definitely not music. Quinn straightened up and stood in the rain. The sensible thing was to radio in. Request backup. Get a warrant for the tunnels. Do this properly. The sensible thing. The problem was that the last time she'd waited for the sensible thing, she'd been standing over Morris's body trying to explain to paramedics why a man in perfectly good health had died of causes that no post-mortem would adequately name. The coroner had written *cardiac event*. She'd written *insufficient answers* in the notebook she still kept, and she'd been working on it ever since. Her hand rested on the wooden panel. Herrera was down there. And whatever she'd been building a case toward for three years was down there too, she could feel it with the same certainty she felt in her jaw before a storm. She checked her watch. Twenty to midnight. The leather strap was dark with rain. She took out her phone and typed a message to the one number she trusted implicitly — DS Chen, who owed her three favours and asked no questions — with her location, the time, and the words *if you don't hear from me in two hours, come and get me.* She sent it. She put her phone away. Then she pulled the panel back and went down. The air changed immediately. Cooler, still, carrying that strange mineral sweetness more intensely now. A string of bare bulbs had been rigged along the tunnel wall at shoulder height, and in their unsteady yellow light she could see that the floor had been swept clean and that there were marks on the walls — not graffiti, something older-looking, angular shapes pressed into the stone like stamps. She kept moving toward the noise, one hand trailing the wall, the other loose near her hip where her warrant card and her radio both lived, neither of them likely to be much use here. The tunnel opened. She'd been in Grand Central. She'd been in the concourse at St. Pancras. She thought she had a reasonable sense of interior scale. What she was looking at dismantled that sense completely. The abandoned station had been transformed into something that had no name in any category she possessed. Stalls lined the old platform edges and spilled out across the tracks, lit by lanterns of a dozen different colours — amber, violet, deep arterial red. The vaulted ceiling above was strung with chains of light that moved without wind. The crowd was dense and close and wrong in ways she was still cataloguing, because some of them were wrong in ways she couldn't look at directly, couldn't hold in focus, like a word you know but temporarily cannot read. The stalls sold things she had no framework for. She could see glass vessels containing substances with no analogue in any evidence locker she'd ever touched. She could see what looked like documents, but the text moved. She could see a woman — possibly a woman — behind a table of small objects, speaking to a customer in a language that sounded like English but structured differently, like English being spoken by something that had learned it from the outside. She stood at the mouth of the tunnel and no one had spotted her yet. Herrera was fifty feet away. She could see the back of his dark coat, the Saint Christopher medallion catching a lantern's red light at his collar. He was talking to someone. He looked, despite everything, completely at ease — like a man who belonged somewhere, in a way she was beginning to understand she did not. Her jaw was tight. Her heart was doing something measured and controlled, the way it always did when she was about to make a decision she couldn't take back. She thought about Morris. She stepped onto the platform.
created_at"2026-02-18T02:27:14.465+00:00" 2/18/2026, 2:27:14 AM
model"anthropic/claude-4.6-sonnet-20260217"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency672
moderation_latency160
generation_time42851
tokens_prompt914
tokens_completion2261
native_tokens_prompt918
native_tokens_completion1973
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"
usage0.032349
router(null)
provider_responses
0
id"6c407e6f-82dd-431a-bc79-5cf94a207535"
status200
is_byokfalse
latency670
endpoint_id"83d802cb-44f2-45a0-a9d5-cb9bf79777c8"
provider_name"Amazon Bedrock"
model_permaslug"anthropic/claude-4.6-sonnet-20260217"
api_type"completions"
id"gen-1771381591-Bzj8U3eiVHLVkvMK3ngG"
upstream_id"6c407e6f-82dd-431a-bc79-5cf94a207535"
total_cost0.032349
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences3
tagDensity0.667
leniency1
rawRatio0
effectiveRatio0
73.68% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1520
totalAiIsmAdverbs8
found
0
adverb"carefully"
count1
1
adverb"quickly"
count1
2
adverb"very"
count1
3
adverb"slightly"
count1
4
adverb"perfectly"
count1
5
adverb"intensely"
count1
6
adverb"completely"
count2
highlights
0"carefully"
1"quickly"
2"very"
3"slightly"
4"perfectly"
5"intensely"
6"completely"
80.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found
0"Chen"
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
76.97% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1520
totalAiIsms7
found
0
word"gleaming"
count1
1
word"footsteps"
count2
2
word"could feel"
count1
3
word"sense of"
count1
4
word"framework"
count1
5
word"measured"
count1
highlights
0"gleaming"
1"footsteps"
2"could feel"
3"sense of"
4"framework"
5"measured"
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
narrationSentences111
matches(empty)
91.38% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount1
narrationSentences111
filterMatches
0"watch"
1"know"
hedgeMatches
0"managed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences112
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen63
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans4
markdownWords29
totalWords1533
ratio0.019
matches
0"You don't know what's on the other side, Harlow. You never do."
1"cardiac event"
2"insufficient answers"
3"if you don't hear from me in two hours, come and get me."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions44
wordCount1526
uniqueNames28
maxNameDensity0.39
worstName"Herrera"
maxWindowNameDensity1
worstWindowName"Herrera"
discoveredNames
Dean1
Street2
Raven1
Nest1
Herrera6
Soho2
Harlow1
Wardour1
Covent1
Garden1
London2
Holborn1
Bloomsbury1
Euston1
Road2
Camden3
Chalk1
Farm1
Town1
Met1
Morris5
Chen1
Grand1
Central1
St1
English2
Saint1
Christopher1
persons
0"Raven"
1"Nest"
2"Herrera"
3"Morris"
4"Chen"
5"English"
6"Saint"
7"Christopher"
places
0"Dean"
1"Street"
2"Soho"
3"Wardour"
4"Covent"
5"Garden"
6"London"
7"Holborn"
8"Bloomsbury"
9"Euston"
10"Road"
11"Camden"
12"Chalk"
13"Farm"
14"Town"
15"Grand"
16"St"
globalScore1
windowScore1
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences76
glossingSentenceCount5
matches
0"looked like nothing in particular, which"
1"felt like a different city entirely"
2"seemed larger than the ground above could account for"
3"looked like documents, but the text moved"
4"sounded like English but structured differ"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1533
matches(empty)
17.86% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount5
totalSentences112
matches
0"knew that much"
1"know that various"
2"was that the"
3"see that the"
4"dismantled that sense"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs45
mean34.07
std30
cv0.88
sampleLengths
06
170
234
3109
412
53
650
75
83
968
1030
1115
127
133
1446
1560
1666
174
1850
193
2015
2173
228
2355
2442
2510
2647
278
2821
2965
307
3135
3214
3358
349
35101
363
3732
3897
3980
4015
4156
4229
434
445
89.46% Passive voice overuse
Target: ≤2% passive sentences
passiveCount5
totalSentences111
matches
0"got lost"
1"was gone"
2"been rigged"
3"been swept"
4"been transformed"
5"being spoken"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount9
totalVerbs256
matches
0"was watching"
1"was building"
2"wasn't running "
3"was being"
4"was checking"
5"was looking"
6"was talking"
7"was beginning"
8"was doing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount17
semicolonCount1
flaggedSentences14
totalSentences112
ratio0.125
matches
0"He'd been at the scene of two of the four incidents she was building a case around — \"incidents\" being the polite word for events that had left her with crime scene photographs she kept in a separate locked drawer in her flat, away from the official files, because she couldn't explain them and she didn't want anyone else trying to until she could."
1"He wasn't running — not yet — but there was a purposeful tightness to his step that she recognised."
2"She kept a full block behind him, letting the pedestrian traffic — thin as it was in the rain — provide whatever cover was available."
3"The cab ahead of them turned into Camden, and she felt something shift in her attention — a tightening, the way the air felt before something broke open."
4"The passage bent twice, and she caught a glimpse of him near the second turn — a flash of dark coat, the gleam of something at his neck."
5"She moved to the place where she'd last heard him and found a section of hoarding — wooden panels covering what had once been a service entrance to the old station."
6"Camden Town had redundant tunnels beneath it; she knew that much."
7"And something else — a smell that didn't belong to rot or concrete or old earth."
8"It was a low, uneven murmur, like a crowd but stranger — voices overlapping in rhythms that didn't match, in a space that seemed larger than the ground above could account for."
9"She took out her phone and typed a message to the one number she trusted implicitly — DS Chen, who owed her three favours and asked no questions — with her location, the time, and the words *if you don't hear from me in two hours, come and get me.* She sent it."
10"A string of bare bulbs had been rigged along the tunnel wall at shoulder height, and in their unsteady yellow light she could see that the floor had been swept clean and that there were marks on the walls — not graffiti, something older-looking, angular shapes pressed into the stone like stamps."
11"Stalls lined the old platform edges and spilled out across the tracks, lit by lanterns of a dozen different colours — amber, violet, deep arterial red."
12"She could see a woman — possibly a woman — behind a table of small objects, speaking to a customer in a language that sounded like English but structured differently, like English being spoken by something that had learned it from the outside."
13"He looked, despite everything, completely at ease — like a man who belonged somewhere, in a way she was beginning to understand she did not."
91.99% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1513
adjectiveStacks1
stackExamples
0"other loose near her"
adverbCount64
adverbRatio0.04230006609385327
lyAdverbCount27
lyAdverbRatio0.01784534038334435
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences112
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences112
mean13.69
std12.46
cv0.91
sampleLengths
06
137
26
310
417
54
615
75
84
96
1064
118
1237
139
143
153
1615
1719
1816
195
203
2120
2225
234
2411
258
265
2725
2812
293
307
313
3218
3328
3414
3528
3618
373
3834
3929
404
4128
425
4317
443
457
464
472
482
4931
38.99% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats18
diversityRatio0.33035714285714285
totalSentences112
uniqueOpeners37
94.34% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences106
matches
0"Then she followed."
1"Then he was gone again,"
2"Then she pulled the panel"
ratio0.028
27.55% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount51
totalSentences106
matches
0"She'd been outside for forty"
1"Her leather watch told her"
2"She had a name."
3"He'd been at the scene"
4"*You don't know what's on"
5"You never do.*"
6"He wasn't running — not"
7"He knew he was being"
8"She gave him the corner."
9"He went north on Wardour"
10"She kept a full block"
11"He didn't look back."
12"She'd stopped assuming oblivious about"
13"He ducked into a cab."
14"She swore under her breath,"
15"She showed her warrant card"
16"They went north."
17"She watched him pay the"
18"She had her driver stop"
19"He turned off the main"
ratio0.481
40.19% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount89
totalSentences106
matches
0"The rain came down in"
1"Quinn turned up the collar"
2"She'd been outside for forty"
3"Her leather watch told her"
4"The bar closed at midnight,"
5"She had a name."
6"Tomás Herrera, twenty-nine years old,"
7"A ghost on paper."
8"He'd been at the scene"
9"Morris would have known what"
10"Morris was three years dead,"
11"*You don't know what's on"
12"You never do.*"
13"The door opened."
14"Herrera came out at eleven-twenty,"
15"He wasn't running — not"
16"He knew he was being"
17"She gave him the corner."
18"He went north on Wardour"
19"She kept a full block"
ratio0.84
47.17% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences106
matches
0"What she was looking at"
ratio0.009
55.39% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences49
technicalSentenceCount6
matches
0"He turned off the main road into a narrow passage that ran along the back of a row of shuttered market stalls, the kind of Camden labyrinth that tourists got lo…"
1"It was a low, uneven murmur, like a crowd but stranger — voices overlapping in rhythms that didn't match, in a space that seemed larger than the ground above co…"
2"The problem was that the last time she'd waited for the sensible thing, she'd been standing over Morris's body trying to explain to paramedics why a man in perf…"
3"The abandoned station had been transformed into something that had no name in any category she possessed."
4"She could see a woman — possibly a woman — behind a table of small objects, speaking to a customer in a language that sounded like English but structured differ…"
5"He looked, despite everything, completely at ease — like a man who belonged somewhere, in a way she was beginning to understand she did not."
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
74.1655%