Run: 2026-02-19-01KHTRJWH8QET90TYJEAAN38G1

Run Details

Duration:
28.8s
Cost:
$0.031110
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
Detective Quinn's boots splashed through puddles as she pursued the hooded figure down Chalk Farm Road. Rain pelted her face, blurring her vision, but she kept her target in sight. The suspect darted between late-night pedestrians, knocking over a rubbish bin that clattered across the wet pavement. Quinn's breath came in controlled bursts as she closed the gap. Her worn leather watch thumped against her wrist with each stride. The figure glanced back – just a flash of pale skin in the darkness – before ducking into a narrow alley. "Metropolitan Police! Stop right there!" Quinn's voice carried over the drum of rain on metal shutters. The alley opened onto a small courtyard behind a row of shops. Quinn's target disappeared behind a maintenance door that led to the old Camden Town station. She drew her torch, beam cutting through sheets of rain as she approached. The door hung open, revealing concrete stairs descending into darkness. Quinn paused at the threshold. The stale underground air carried unfamiliar scents – incense, exotic spices, something metallic that made her nose twitch. Her instincts screamed danger, but three years of investigating her partner's death had taught her to trust those warnings. They usually led to answers. She started down the stairs, keeping close to the wall. Her footsteps echoed in the confined space. The temperature dropped with each step, until her breath fogged in front of her face. At the bottom, fluorescent lights flickered dimly over tiled walls covered in decades of grime. Voices drifted from further along the platform – not the usual echoes of maintenance workers or urban explorers. These spoke in languages she didn't recognize, accompanied by the buzz of commerce and haggling. Quinn reached into her jacket pocket, fingers closing around a small object she'd recovered from Morris's effects after his death. A token carved from what looked like bone, worn smooth from handling. She'd assumed it was evidence from their last case together, but now... The platform opened into the old station concourse. Quinn pressed herself against a wall, taking in the scene before her. Market stalls filled the space, their vendors illuminated by strings of colored lights and floating orbs that definitely weren't standard electrical fixtures. The crowd that moved between them wasn't entirely human. Her suspect weaved through the stalls toward a far exit. Quinn started to follow, then froze as a familiar face appeared through the crowd. Tomás Herrera, the disgraced paramedic whose name had surfaced repeatedly in her investigation. He worked his way between the stalls, medical bag slung over his shoulder, Saint Christopher medallion glinting in the strange light. "You're new here." A gravelly voice spoke from beside her. "Show your token or leave." Quinn's hand tightened around the bone disc in her pocket. Following her suspect meant stepping into whatever this was – an underground network that might have answers about Morris, but could just as easily get her killed. Her military training screamed at her to retreat, call for backup, return with a tactical team. But backup wouldn't understand what they were seeing. They'd shut this place down before she could find what she needed. She pulled out the token, holding it up without taking her eyes off Herrera. The figure beside her grunted in approval. "Welcome to the Veil Market. Mind the rules, or we'll mind you." Quinn stepped into the crowd, letting the press of bodies conceal her from Herrera's view. Her suspect was nearly at the far exit, but now she had two targets to track. The market's lights cast strange shadows across the walls, and somewhere in the distance, a bell tolled.
created_at"2026-02-19T10:54:27.884Z" 2/19/2026, 10:54:27 AM
model"anthropic/claude-3.5-sonnet"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1139
moderation_latency140
generation_time28580
tokens_prompt1140
tokens_completion959
native_tokens_prompt1125
native_tokens_completion812
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"
usage0.03111
router(null)
provider_responses
0
endpoint_id"d4fb79bd-9786-4932-af81-b83040e9f4e4"
id"300ece6d-2355-43f0-9989-4db2f806f2f1"
is_byokfalse
latency1139
model_permaslug"anthropic/claude-3.5-sonnet"
provider_name"Amazon Bedrock"
status200
api_type"completions"
id"gen-1771498467-oaO0D1bbzSvGlXcUD5r8"
upstream_id"300ece6d-2355-43f0-9989-4db2f806f2f1"
total_cost0.03111
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)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount601
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)
41.76% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount601
totalAiIsms7
found
0
word"footsteps"
count1
1
word"echoed"
count1
2
word"flickered"
count1
3
word"echoes"
count1
4
word"standard"
count1
5
word"familiar"
count1
6
word"glinting"
count1
highlights
0"footsteps"
1"echoed"
2"flickered"
3"echoes"
4"standard"
5"familiar"
6"glinting"
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
narrationSentences43
matches(empty)
76.41% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences43
filterMatches
0"watch"
hedgeMatches
0"started to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences45
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen26
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords605
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
63.79% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions22
wordCount580
uniqueNames10
maxNameDensity1.72
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Quinn10
Chalk1
Farm1
Road1
Camden1
Town1
Morris2
Herrera3
Saint1
Christopher1
persons
0"Quinn"
1"Morris"
2"Herrera"
3"Saint"
4"Christopher"
places
0"Chalk"
1"Farm"
2"Road"
3"Camden"
4"Town"
globalScore0.638
windowScore0.833
89.02% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences41
glossingSentenceCount1
matches
0"looked like bone, worn smooth from handli"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount605
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences45
matches(empty)
71.56% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs16
mean37.81
std15.14
cv0.4
sampleLengths
047
143
216
350
447
547
633
744
851
958
1015
1153
1220
1321
1412
1548
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences43
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs106
matches
0"were seeing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount5
semicolonCount0
flaggedSentences4
totalSentences45
ratio0.089
matches
0"The figure glanced back – just a flash of pale skin in the darkness – before ducking into a narrow alley."
1"The stale underground air carried unfamiliar scents – incense, exotic spices, something metallic that made her nose twitch."
2"Voices drifted from further along the platform – not the usual echoes of maintenance workers or urban explorers."
3"Following her suspect meant stepping into whatever this was – an underground network that might have answers about Morris, but could just as easily get her killed."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount576
adjectiveStacks1
stackExamples
0"open, revealing concrete stairs"
adverbCount18
adverbRatio0.03125
lyAdverbCount8
lyAdverbRatio0.013888888888888888
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences45
echoCount0
echoWords(empty)
81.03% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences45
mean13.44
std4.74
cv0.353
sampleLengths
016
114
217
311
411
521
616
712
815
913
1010
115
1218
1319
145
1510
167
1715
1815
1918
2015
2120
2212
2312
248
2512
2622
279
2810
2914
3013
3121
3210
335
3410
3527
3616
378
3812
3914
407
4112
4215
4316
4417
74.07% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats0
diversityRatio0.4444444444444444
totalSentences45
uniqueOpeners20
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences43
matches(empty)
ratio0
99.07% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount13
totalSentences43
matches
0"Her worn leather watch thumped"
1"She drew her torch, beam"
2"Her instincts screamed danger, but"
3"They usually led to answers."
4"She started down the stairs,"
5"Her footsteps echoed in the"
6"She'd assumed it was evidence"
7"Her suspect weaved through the"
8"He worked his way between"
9"Her military training screamed at"
10"They'd shut this place down"
11"She pulled out the token,"
12"Her suspect was nearly at"
ratio0.302
6.51% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences43
matches
0"Detective Quinn's boots splashed through"
1"Rain pelted her face, blurring"
2"The suspect darted between late-night"
3"Quinn's breath came in controlled"
4"Her worn leather watch thumped"
5"The figure glanced back –"
6"Quinn's voice carried over the"
7"The alley opened onto a"
8"Quinn's target disappeared behind a"
9"She drew her torch, beam"
10"The door hung open, revealing"
11"Quinn paused at the threshold."
12"The stale underground air carried"
13"Her instincts screamed danger, but"
14"They usually led to answers."
15"She started down the stairs,"
16"Her footsteps echoed in the"
17"The temperature dropped with each"
18"Voices drifted from further along"
19"These spoke in languages she"
ratio0.907
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences43
matches(empty)
ratio0
79.83% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences34
technicalSentenceCount3
matches
0"The suspect darted between late-night pedestrians, knocking over a rubbish bin that clattered across the wet pavement."
1"Quinn's target disappeared behind a maintenance door that led to the old Camden Town station."
2"Following her suspect meant stepping into whatever this was – an underground network that might have answers about Morris, but could just as easily get her kill…"
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
79.2356%