Run: 2026-10-08-01M4DACH5752Y5K612K9PDMC16

Run Details

Duration:
12.6s
Cost:
$0.001053
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="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4415" name="Lucien Moreau" aliases="Lucien, Luc, the Frenchman" pronouns="he/him" species="Half-Demon" storyRole="Ally"> <eyeColor>heterochromatic — one amber, one black</eyeColor> <hair>slicked-back platinum blond</hair> <height>5'11"</height> <age>32</age> <cane>ivory-handled cane that conceals a thin blade</cane> <dress>tailored charcoal suits</dress> <description>An impeccably dressed fixer and information broker in London's supernatural underworld. He speaks four languages fluently.</description> <backstory>Born in Marseille to a human mother and a demon father from the realm of Avaros.</backstory> </character> <location id="858" name="Eva's Flat" aliases="Eva's place, the flat on Brick Lane" realm="Earth"> <cat>tabby cat named Ptolemy</cat> <security>three deadbolts on the front door</security> <description>A cramped one-bedroom flat above a curry house on Brick Lane, East London. Every surface is covered with books, scrolls, and research notes.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the romance genre. Two people who parted on complicated terms are forced together again when one shows up unannounced at the other's flat. There's history between them — attraction, hurt, and things left unsaid. Write the scene from the moment the door opens. 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 knock came at half past eleven, three slow raps that didn't belong to Silas, who hammered the door like he was evicting someone. Rory set down the tea towel, crossed the flat in her socks, and stopped with her palm flat against the wood. She knew the rhythm. She had hated how well she knew it. She opened the door on the chain. Lucien stood on the landing under the bare bulb, one hand resting on the ivory handle of his cane. The charcoal suit was immaculate despite the drizzle that had darkened the shoulders of his coat. His hair had been slicked back by someone with better hands than the rain. Amber on the left, black on the right, and both of them fixed on her face as if she were a contract he needed to read twice. "You could have called," she said. "You changed your number." "Yes. That's generally what the phrase means." She didn't slide the chain off. "How did you find this address?" "Silas." He tilted his head toward the stairwell, where the bar's jukebox thumped faintly through the floorboards. "He was less cooperative than I anticipated. I offered him twenty quid. He asked for forty. I suspect he's already spent it on something terrible." "That's Silas." "Yes." His gaze dropped to the chain, then rose again. "Are you going to let me in, Aurora, or shall we conduct this conversation through a gap the width of a playing card?" She considered slamming the door. She considered the view of him on her landing, which was quite good, and resented that she had considered it at all. She undid the chain. The flat rose around them, small and crooked. Books stacked along the skirting boards. Her delivery jacket hung off the back of a kitchen chair next to a pile of Golden Empress menus she had been meaning to recycle for a month. Lucien stepped inside and paused, and she watched him take it in with the same unhurried attention he gave everything. He noticed the mug on the windowsill. He noticed the second cup on the draining board, unwashed, which she had not bothered to explain to herself. "You've been drinking tea alone," he said. "I've been drinking tea. The alone part is your assumption." "You never drank it alone at Eva's. You always wanted someone to argue with about the milk." "Eva argued with me about the milk. You just stood in the doorway and watched." He set the cane against the wall, carefully, the way a man might set down a loaded thing. "I was watching you. It was not the same." "Wasn't it?" She turned away before he could answer, filled the kettle because her hands needed a job, and heard him stop behind her. The silence stretched long enough that the kettle began to tick as it heated. "Say what you came to say," she said. "I'm not going to pretend I'm not standing here with a tea towel and a grudge." "I came because someone has been asking about you in Whitechapel." His voice had gone flat and careful, the register he used when business was the only safe thing to talk about. "Not the usual sort of asking. Men with questions about where you sleep and who you run with. One of them mentioned Evan's name." The kettle clicked off. Neither of them moved to lift it. Her left wrist throbbed where the small crescent scar sat, a dull memory she had trained herself not to touch. "Evan doesn't know where I live." "Evan has an uncle who owes money to people I used to work for." Lucien's tone didn't soften, but his eyes did something she didn't want to read. "I thought you should hear it from me before you heard it from a man with a knife." She turned around. He had not come closer. He stood by the bookshelf with his hands clasped behind his back, the way he always stood when he was trying not to reach for something that wasn't his. "You left," she said. "Three months ago. You walked out of that bar and didn't look back, and you didn't even bother to tell me why." "I told you I would be gone." "You told me you had business. You didn't say it was a wall between us." "I did not know how to say the rest of it without you hating me for it." He held her gaze, both colours steady. "I still don't. But I would rather you hate me in a room where you are safe than forgive me on a street where you are not." She laughed, short and sharp, and it came out wrong. "Is that supposed to be romantic?" "No," said Lucien. "It's supposed to be true."
created_at"2026-10-08T08:34:26.607Z" 10/8/2026, 8:34:26 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2417
moderation_latency(null)
generation_time12529
tokens_prompt1004
tokens_completion1294
native_tokens_prompt1469
native_tokens_completion1813
native_tokens_completion_images(null)
native_tokens_reasoning401
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"
usage0.0010534
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448466-zLoqRNUsihFouE121ugP"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448466-n8xEBHPcKW7okXNaTQSh"
upstream_id"msg_011CfpTms17z7EHQSGDDm1qU"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTms17z7EHQSGDDm1qU"
is_byokfalse
latency758
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010534
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
totalTags10
adverbTagCount0
adverbTags(empty)
dialogueSentences31
tagDensity0.323
leniency0.645
rawRatio0
effectiveRatio0
93.77% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount802
totalAiIsmAdverbs1
found
0
adverb"carefully"
count1
highlights
0"carefully"
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)
87.53% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount802
totalAiIsms2
found
0
word"silence"
count1
1
word"throbbed"
count1
highlights
0"silence"
1"throbbed"
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
narrationSentences39
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences39
filterMatches(empty)
hedgeMatches
0"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences59
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen32
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords802
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions14
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions7
wordCount474
uniqueNames4
maxNameDensity0.84
worstName"Lucien"
maxWindowNameDensity1
worstWindowName"Lucien"
discoveredNames
Silas1
Golden1
Empress1
Lucien4
persons
0"Silas"
1"Lucien"
places(empty)
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences28
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount802
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences59
matches
0"resented that she"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs31
mean25.87
std21.73
cv0.84
sampleLengths
057
17
276
36
44
519
642
72
833
927
104
1188
127
1310
1417
1515
1627
172
1836
1924
2056
2111
2226
2346
2437
2526
267
2715
2851
2916
308
96.27% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences39
matches
0"been slicked"
50.19% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs89
matches
0"was evicting"
1"was trying"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences59
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount474
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.029535864978902954
lyAdverbCount3
lyAdverbRatio0.006329113924050633
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences59
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences59
mean13.59
std8.45
cv0.622
sampleLengths
024
121
24
38
47
519
616
714
827
96
104
1113
126
1317
1425
152
1610
1723
185
1922
204
218
226
2328
2420
257
2619
277
2810
2917
3015
3118
329
332
3422
3514
368
3716
3832
3924
404
417
4220
436
4428
4518
463
475
4829
494
56.50% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.4406779661016949
totalSentences59
uniqueOpeners26
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences34
matches(empty)
ratio0
0.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount22
totalSentences34
matches
0"She knew the rhythm."
1"She had hated how well"
2"She opened the door on"
3"His hair had been slicked"
4"She didn't slide the chain"
5"He tilted his head toward"
6"His gaze dropped to the"
7"She considered slamming the door."
8"She considered the view of"
9"She undid the chain."
10"Her delivery jacket hung off"
11"He noticed the mug on"
12"He noticed the second cup"
13"He set the cane against"
14"She turned away before he"
15"His voice had gone flat"
16"Her left wrist throbbed where"
17"She turned around."
18"He had not come closer."
19"He stood by the bookshelf"
ratio0.647
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount34
totalSentences34
matches
0"The knock came at half"
1"Rory set down the tea"
2"She knew the rhythm."
3"She had hated how well"
4"She opened the door on"
5"Lucien stood on the landing"
6"The charcoal suit was immaculate"
7"His hair had been slicked"
8"Amber on the left, black"
9"She didn't slide the chain"
10"He tilted his head toward"
11"His gaze dropped to the"
12"She considered slamming the door."
13"She considered the view of"
14"She undid the chain."
15"The flat rose around them,"
16"Books stacked along the skirting"
17"Her delivery jacket hung off"
18"Lucien stepped inside and paused,"
19"He noticed the mug on"
ratio1
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences34
matches(empty)
ratio0
53.57% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences16
technicalSentenceCount2
matches
0"She considered the view of him on her landing, which was quite good, and resented that she had considered it at all."
1"He stood by the bookshelf with his hands clasped behind his back, the way he always stood when he was trying not to reach for something that wasn't his."
75.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags10
uselessAdditionCount1
matches
0"Lucien's tone didn't, but his eyes did something she didn't want to read"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences31
tagDensity0.129
leniency0.258
rawRatio0
effectiveRatio0
80.4273%