Run: 2026-10-08-01M4DA22CEA34Q6EWE2ZQ1QYV2

Run Details

Duration:
13.7s
Cost:
$0.001157
Provider:
Claude Platform on AWS

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 came sideways off the Thames, and by the time Harlow Quinn reached Camden High Street it had soaked through her wool coat and found the gap at her collar. Her watch face fogged. She wiped it with her thumb and kept moving, boots slapping the wet paving, eyes fixed on the figure ahead. Tomás Herrera ran like a man who had spent years carrying stretchers and knew exactly how much his lungs could take. He cut left past a shuttered record shop, his dark curls flattened to his skull, a canvas bag bouncing against his hip. The Saint Christopher medallion swung out from under his shirt collar with every stride. She had seen that medallion on the custody photographs. She had seen the scar on his forearm too, a pale ridge that caught the streetlight as he threw an arm out to steady himself on a lamppost. "Herrera!" Her voice cracked off the brick fronts. "Stop running and you'll live longer." He glanced back. Rain ran down his face and he didn't bother with it. "You've been saying that for three streets, Detective." "Then I'm consistent. Stop." He didn't stop. He swung right onto a narrow lane where the lamps had died, and Quinn lost him for two long heartbeats in the dark. She slowed at the corner, pressed her back to the wet brick, and listened. Water dripped from a broken gutter. A bin lid rattled. Somewhere a dog barked and then thought better of it. She stepped out with her torch low and her hand near her hip. The lane was empty except for a stack of crates and a steel shutter pulled halfway down over a doorway that had once been a shop. Someone had spray-painted a crude raven on it, black wings, one red eye. The shutter was not locked. It had been lifted and let fall again, and the fresh scrape in the grime on the pavement said so. She crouched. A smear of dark blood marked the lip of the shutter, bright against the rust. Her jaw tightened. Three years ago she had knelt in a stairwell very like this one beside DS Morris, and the blood on that floor had not belonged to anyone who should have been bleeding. Her hands had been steady then too. Steadiness was what the job demanded, and she had built eighteen years of service on it. "Quinn." The voice came from below, down a flight of stone steps that vanished into the dark. Tomás sounded winded, but the words were clear. "There's a door at the bottom. You can come down or you can go back to your car and pretend you never saw me. I'd prefer the second, honestly." She rose and aimed the torch down the steps. The beam caught curved tiles, a rusted sign reading **CAMDEN TOWN**, and below that the faded roundel that had once meant the Northern line. The station had been closed for decades. Nobody was supposed to be down there. "You've got blood on your sleeve, Herrera," she called. "Whose is it?" A pause. Then his laugh, short and humourless, echoed up the stairwell. "Mine, mostly. The rest belongs to a man who would have bled out on a bench in Euston if I'd listened to the people who wanted me to walk away. You still have your warrant card, Detective? Good. Hold it up. Down here, that card buys you nothing." She heard the sound of metal scraping, then a heavy door sighing on its hinges. Light spilled up the stairs in a warm amber wash, mixed with a smell of wet stone, incense and something sharper underneath, like burnt copper. Quinn checked her watch. Eleven forty-two. Her radio had been dead since Chalk Farm, swallowed by the dead spaces under the arches. Nobody knew she had followed Herrera off the main road. Her inspector would find her car in a few hours and start asking questions she would have to answer in a room with a recording light. She thought about Morris. She thought about the bone-white card she had seen pinned behind the register of the Raven's Nest the night she had finally walked into that bar, the card with the lacquered edge and the small carved sigil, the one the barman had refused to explain. Nobody in the Met had a name for the place beneath Camden. Nobody who had gone looking for it had come back with a report that made sense. She had no bone token. She had no invitation. She had a torch, a pistol she hoped she would not need, and a suspect who had just told her to hold up her warrant card as if it were a joke. Quinn stepped to the top of the stairs. The rain hammered on the pavement behind her, loud and indifferent, and the warm light below pulled at her like a tide. She set her boot on the first worn step, the stone cold through the leather, and started down.
created_at"2026-10-08T08:28:43.8Z" 10/8/2026, 8:28:43 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2948
moderation_latency(null)
generation_time13648
tokens_prompt1104
tokens_completion1402
native_tokens_prompt1576
native_tokens_completion1998
native_tokens_completion_images(null)
native_tokens_reasoning464
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"
service_tier"default"
usage0.0011566
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448123-o7OGu1Pf6WnWo6EdagzV"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448123-FLKaVhGSInXrfRIJP5CQ"
upstream_id"msg_011CfpTLbfSJ1KW3P2o16nP6"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTLbfSJ1KW3P2o16nP6"
is_byokfalse
latency811
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011566
cache_discount(null)
upstream_inference_cost0
provider_name"Claude Platform on AWS"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences9
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
94.03% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount838
totalAiIsmAdverbs1
found
0
adverb"very"
count1
highlights
0"very"
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)
94.03% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount838
totalAiIsms1
found
0
word"echoed"
count1
highlights
0"echoed"
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
narrationSentences54
matches(empty)
89.95% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences54
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences60
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen48
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords2
totalWords838
ratio0.002
matches
0"CAMDEN TOWN"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount731
uniqueNames17
maxNameDensity0.55
worstName"Quinn"
maxWindowNameDensity1
worstWindowName"Quinn"
discoveredNames
Thames1
Harlow1
Quinn4
Camden2
High1
Street1
Herrera2
Saint1
Christopher1
Morris2
Tomás2
Northern1
Chalk1
Farm1
Raven1
Nest1
Met1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Saint"
4"Christopher"
5"Morris"
6"Tomás"
7"Raven"
places
0"Thames"
1"Camden"
2"High"
3"Street"
4"Chalk"
5"Farm"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences40
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount838
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences60
matches
0"seen that medallion"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs18
mean46.56
std23.39
cv0.502
sampleLengths
055
194
214
322
44
560
652
725
875
954
1047
1112
1260
1340
1458
1577
1641
1748
85.77% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences54
matches
0"been lifted"
1"been closed"
2"was supposed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs114
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences60
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount734
adjectiveStacks0
stackExamples(empty)
adverbCount19
adverbRatio0.025885558583106268
lyAdverbCount2
lyAdverbRatio0.0027247956403269754
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences60
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences60
mean13.97
std10.48
cv0.75
sampleLengths
031
14
220
321
422
514
69
728
88
96
103
1111
128
134
143
1523
1614
176
184
1910
2013
2126
2213
235
2420
252
2615
273
2832
297
3016
3117
328
3329
349
3524
367
377
389
393
402
4110
4248
4315
4425
454
462
4716
4810
4926
55.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.4
totalSentences60
uniqueOpeners24
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences50
matches
0"Somewhere a dog barked and"
1"Then his laugh, short and"
ratio0.04
28.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences50
matches
0"Her watch face fogged."
1"She wiped it with her"
2"He cut left past a"
3"She had seen that medallion"
4"She had seen the scar"
5"Her voice cracked off the"
6"He glanced back."
7"He didn't stop."
8"He swung right onto a"
9"She slowed at the corner,"
10"She stepped out with her"
11"It had been lifted and"
12"Her jaw tightened."
13"Her hands had been steady"
14"She rose and aimed the"
15"She heard the sound of"
16"Her radio had been dead"
17"Her inspector would find her"
18"She thought about Morris."
19"She thought about the bone-white"
ratio0.48
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount47
totalSentences50
matches
0"The rain came sideways off"
1"Her watch face fogged."
2"She wiped it with her"
3"Tomás Herrera ran like a"
4"He cut left past a"
5"The Saint Christopher medallion swung"
6"She had seen that medallion"
7"She had seen the scar"
8"Her voice cracked off the"
9"He glanced back."
10"Rain ran down his face"
11"He didn't stop."
12"He swung right onto a"
13"She slowed at the corner,"
14"Water dripped from a broken"
15"A bin lid rattled."
16"She stepped out with her"
17"The lane was empty except"
18"Someone had spray-painted a crude"
19"The shutter was not locked."
ratio0.94
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences50
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences30
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences9
tagDensity0.111
leniency0.222
rawRatio0
effectiveRatio0
88.2261%