Run: 2026-02-19-01KHV72HMKP0H1MXRZ2J05XX4R

Run Details

Duration:
33.1s
Cost:
$0.023418
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="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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
The rain had turned Soho into a black mirror. Every puddle threw back the orange smear of streetlights, and the gutters ran fast and cold. Quinn kept her eyes on the figure fifty metres ahead. He'd bolted the moment she'd stepped out of the Raven's Nest, which told her everything she needed to know about his conscience. She'd only wanted to ask him a few questions about a body found near Waterloo three nights ago — a body with wounds she still couldn't explain to her superintendent — but the moment her eyes landed on him across the bar, something in his face had collapsed, and he was through the door before she'd even moved her hand. Now her shoes were soaked through and she was running hard past shuttered restaurants and a supermarket with its lights still blazing, past a group of students who pressed themselves against a wall and stared. The man ahead of her wasn't Tomás Herrera — she'd confirmed that back inside, when she'd put his face against the photograph on her phone and come up short. But he'd been sitting with Herrera's associates. He'd been listening to things she suspected Herrera had told them. That was enough. He took a left onto a side street so narrow the buildings seemed to lean together overhead. Quinn went with him, her stride eating the distance. She was faster on the flat, always had been — eighteen years of early morning runs had made her legs reliable things — but he was lighter and he knew these streets better than she did. He moved with a specific, practised panic that suggested he had a destination in mind. That worried her more than the running. He swung right, then immediately left. A market stall someone had left half-assembled blocked most of the pavement and he vaulted it without breaking stride. Quinn went round it, lost two seconds. He glanced back once, and in the amber quarter-light she caught his face: young, tight with fear, a cut above one eyebrow she hadn't noticed inside. He wasn't running from the law. He was running from being caught *talking* to it. She pulled out her radio and kept moving. "Control, this is DI Quinn, I'm in foot pursuit, suspect heading north on—" She looked up for a street sign and found none. "Camden direction, on foot from Soho. Male, early twenties, dark jacket, grey trainers. Requesting any available units to—" He disappeared. She lowered the radio and ran harder. The street ended in a junction she didn't recognise, a dead-looking stretch with a boarded-up pub on the corner and a bus stop with the timetable torn out. The rain intensified without warning, a sharp lateral sheet of it that hit her in the face and turned everything grey and indistinct. No figure. No sound of footsteps. Quinn stopped. Turned. Read the street the way Morris had taught her — *start with what's wrong, not what's there* — and her eye caught it almost immediately. A corrugated metal fence that ran along the edge of what should have been a pavement had a section pulled back at the base, just wide enough. Beyond it: a staircase going down, concrete steps, ancient tiling visible at the edge of her torch beam. An old station entrance. Camden, she thought. The decommissioned line. She stood in the rain with water running off her jaw and considered it. Eighteen years of instinct said call it in, wait for backup, do not go underground alone pursuing an unidentified suspect into an unlit disused transport tunnel at eleven-forty at night. Eighteen years of something else said she was running out of nights like this one. She'd started pulling at the Herrera thread four months ago, after the Waterloo body. Before that, another one in Bermondsey that the coroner had filed as inconclusive. Before that, DS Morris, three years ago, who had gone into a building in Lambeth following a lead she'd given him and had come out — been brought out — with injuries the hospital had photographed and quietly stopped asking about. Morris, who had been her partner for six years and who had died in a ward in King's College with his hands folded and his face completely still, and who had never once told her what he'd seen in that building. She crouched and went through the gap. The staircase went down further than any disused station she'd seen. The tiles were original — she could tell from the curves and the faded lettering she couldn't quite read — but the lights were new. Not the fluorescents of the Underground. Something lower, amber, almost warm, strung along the walls on cables that vanished around corners. The air smelt of burnt wax and something sharp beneath it, chemical, with an edge of copper. At the bottom of the stairs, a crowd. Quinn stopped with one foot on the last step. Thirty, forty people in a space that had once been a ticket hall. Stalls. Tables covered in cloth, behind which figures stood with their backs half-turned. Objects she couldn't immediately categorise arranged under glass and in cases and hanging from hooks driven into the old tiling. A man nearby had a coat that moved at the hem independent of the air. A woman's face, when she turned under the amber light, was the wrong shape in a way Quinn's brain refused to process directly and filed instead as *probably a trick of the light.* The man she'd been following was twenty feet away, speaking rapidly to someone she couldn't see. Quinn's hand went to her warrant card out of pure reflex, and she stopped herself. She stepped back against the wall. Thought about Morris. Thought about what she'd photographed in Bermondsey, what she'd photographed at Waterloo, what she had on her phone and in three locked notebooks that she'd told no one about, not her super, not the team, no one. She put the warrant card back in her pocket. Then she moved off the step and into the market.
created_at"2026-02-19T15:07:41.341Z" 2/19/2026, 3:07:41 PM
model"anthropic/claude-4.6-sonnet-20260217"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency909
moderation_latency251
generation_time32381
tokens_prompt1140
tokens_completion1505
native_tokens_prompt1126
native_tokens_completion1336
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"
usage0.023418
router(null)
provider_responses
0
endpoint_id"83d802cb-44f2-45a0-a9d5-cb9bf79777c8"
id"60563d6b-5771-4143-959c-896c40b410ef"
is_byokfalse
latency909
model_permaslug"anthropic/claude-4.6-sonnet-20260217"
provider_name"Amazon Bedrock"
status200
api_type"completions"
id"gen-1771513661-DK5DiOoRMzDp0TajWR5g"
upstream_id"60563d6b-5771-4143-959c-896c40b410ef"
total_cost0.023418
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences2
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
95.04% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1008
totalAiIsmAdverbs1
found
0
adverb"completely"
count1
highlights
0"completely"
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)
90.08% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1008
totalAiIsms2
found
0
word"eyebrow"
count1
1
word"footsteps"
count1
highlights
0"eyebrow"
1"footsteps"
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
narrationSentences65
matches
0"t with fear"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences65
filterMatches(empty)
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences66
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen58
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans3
markdownWords14
totalWords1013
ratio0.014
matches
0"talking"
1"start with what's wrong, not what's there"
2"probably a trick of the light."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions10
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions28
wordCount982
uniqueNames13
maxNameDensity0.71
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Soho1
Raven1
Nest1
Waterloo3
Tomás1
Herrera4
Morris4
Bermondsey2
Lambeth1
King1
College1
Underground1
Quinn7
persons
0"Raven"
1"Tomás"
2"Herrera"
3"Morris"
4"Quinn"
places
0"Soho"
1"Waterloo"
2"Bermondsey"
3"Lambeth"
4"King"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences54
glossingSentenceCount1
matches
0"quite read — but the lights were new"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1013
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences66
matches
0"confirmed that back"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs32
mean31.66
std30.28
cv0.957
sampleLengths
025
110
282
335
447
53
677
77
858
96
109
118
1241
132
1458
156
1673
1710
1814
1930
2015
21109
227
2374
248
259
2694
2716
2815
2946
309
3110
89.07% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences65
matches
0"been — eighteen"
1"being caught"
2"been brought"
30.15% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs157
matches
0"was running"
1"wasn't running"
2"was running"
3"was running"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount11
semicolonCount0
flaggedSentences6
totalSentences66
ratio0.091
matches
0"She'd only wanted to ask him a few questions about a body found near Waterloo three nights ago — a body with wounds she still couldn't explain to her superintendent — but the moment her eyes landed on him across the bar, something in his face had collapsed, and he was through the door before she'd even moved her hand."
1"The man ahead of her wasn't Tomás Herrera — she'd confirmed that back inside, when she'd put his face against the photograph on her phone and come up short."
2"She was faster on the flat, always had been — eighteen years of early morning runs had made her legs reliable things — but he was lighter and he knew these streets better than she did."
3"Read the street the way Morris had taught her — *start with what's wrong, not what's there* — and her eye caught it almost immediately."
4"Before that, DS Morris, three years ago, who had gone into a building in Lambeth following a lead she'd given him and had come out — been brought out — with injuries the hospital had photographed and quietly stopped asking about."
5"The tiles were original — she could tell from the curves and the faded lettering she couldn't quite read — but the lights were new."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount977
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.03582395087001024
lyAdverbCount10
lyAdverbRatio0.01023541453428864
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences66
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences66
mean15.35
std11.72
cv0.763
sampleLengths
09
116
210
322
460
535
629
77
811
93
1017
119
1236
1315
147
156
1619
177
1826
196
209
218
2223
2318
242
257
2628
2723
282
294
302
311
3225
3327
3418
354
363
373
3814
3930
4015
4114
4213
4341
4441
457
4611
4725
486
4915
57.07% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.42424242424242425
totalSentences66
uniqueOpeners28
55.56% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences60
matches
0"Then she moved off the"
ratio0.017
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences60
matches
0"He'd bolted the moment she'd"
1"She'd only wanted to ask"
2"He'd been listening to things"
3"He took a left onto"
4"She was faster on the"
5"He moved with a specific,"
6"He swung right, then immediately"
7"He glanced back once, and"
8"He wasn't running from the"
9"He was running from being"
10"She pulled out her radio"
11"She looked up for a"
12"She lowered the radio and"
13"She stood in the rain"
14"She'd started pulling at the"
15"She crouched and went through"
16"She stepped back against the"
17"She put the warrant card"
ratio0.3
85.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount45
totalSentences60
matches
0"The rain had turned Soho"
1"Every puddle threw back the"
2"Quinn kept her eyes on"
3"He'd bolted the moment she'd"
4"She'd only wanted to ask"
5"The man ahead of her"
6"He'd been listening to things"
7"That was enough."
8"He took a left onto"
9"Quinn went with him, her"
10"She was faster on the"
11"He moved with a specific,"
12"That worried her more than"
13"He swung right, then immediately"
14"A market stall someone had"
15"Quinn went round it, lost"
16"He glanced back once, and"
17"He wasn't running from the"
18"He was running from being"
19"She pulled out her radio"
ratio0.75
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount3
totalSentences60
matches
0"Now her shoes were soaked"
1"Before that, another one in"
2"Before that, DS Morris, three"
ratio0.05
84.94% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences37
technicalSentenceCount3
matches
0"He'd bolted the moment she'd stepped out of the Raven's Nest, which told her everything she needed to know about his conscience."
1"Now her shoes were soaked through and she was running hard past shuttered restaurants and a supermarket with its lights still blazing, past a group of students …"
2"The rain intensified without warning, a sharp lateral sheet of it that hit her in the face and turned everything grey and indistinct."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
89.5635%