Run: 2026-09-03-01M1KRZ0TMA4E7W94SSM5D0HVX

Run Details

Duration:
52.8s
Cost:
$0.003452
Provider:
Phala

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 three deadbolts screeched one after the other. Aurora kept her shoulder against the door until the last bolt cleared, then let it swing in. The hallway light from Eva’s flat spilled onto the landing and caught in Lucien Moreau’s slicked-back platinum hair. He stood in the narrow stairwell with a tailored charcoal suit cut too fine for the curry house smell rising from Brick Lane below. His ivory-handled cane rested against his calf. The amber eye fixed on her, the black one caught the bulb. Ptolemy launched from the bookshelf in the cramped one-bedroom and landed on the door frame, tail high. The tabby hissed. Lucien didn’t move. “You have a cat,” he said. Aurora’s hand found the scar on her left wrist without thinking, thumb brushing the small crescent shaped pale against her skin. “You have a key.” “I don’t.” She stared at him. Bright blue eyes held still. The flat behind her was a mess of paper. Scrolls and research notes covered every surface. A stack of takeaway menus from Yu-Fei Cheung’s Golden Empress teetered by the kettle. “You are trespassing.” “I am asking.” She stepped back. The door didn’t close. The hallway smelled of cumin and damp plaster. Lucien took one step up and the stair creaked under his weight. He was five eleven to her five six. He filled the frame. “You left your number on a dead drop in Whitechapel,” he said. “You told me not to use it.” “I told you not to come here.” His mouth thinned. The cane tapped once against the wood. “Eva isn’t here.” “I know.” “Good.” Silence settled over the narrow space between them. Ptolemy stalked down the doorframe and rubbed against Aurora’s ankle. She didn’t look down. “You took my call last week,” Lucien said. “You asked about the ledger.” “I needed a name.” “You needed me.” She lifted her chin. “I needed information. There is a difference.” He studied the books piled on the floor. A page fluttered. “You still keep everything.” “I like to know what I’m dealing with.” “Do you?” Aurora turned her wrist so the scar caught light. “I’m dealing with you standing in my friend’s hallway.” “You’re dealing with the man who pulled you out of the river.” The words landed hard. She closed her eyes for half a second. “You left me at the docks with a bruised jaw and a story about debts,” she said. “I left you breathing.” “You left me alone.” “I left you because you asked me to.” “I asked you not to touch me.” “You asked me to leave London.” He shifted his weight. The suit didn’t wrinkle. “I stayed in Marseille for six months.” “You came back for her.” His amber eye narrowed. “For you.” Ptolemy yowled and leapt onto the pile of scrolls, scattering them. Aurora moved without thinking, catching a notebook before it hit the floor. Her hair fell forward, straight and shoulder-length, black against the white paper. “You have a job for me,” she said. “No.” The flat felt smaller. The curry house downstairs clattered with plates. Somewhere a train rattled. “Then why are you here?” Lucien lifted the cane slightly. The ivory handle caught the light. “Your ex called me.” Evan. The name sat in her throat. “What did he want?” “He wants the name of the woman who hired you to find his sister.” Aurora’s fingers tightened on the notebook. “He doesn’t have a sister.” “He thinks he does.” “You tell him no.” “I told him I don’t work for humans who hit women.” She laughed once, sharp and without warmth. “That’s a new line.” “It’s an old rule.” She looked at the door, at the three deadbolts, at the narrow stairwell he blocked. “You can’t stay.” “I won’t.” He stepped forward. The air changed. He smelled of cold iron and expensive soap. “You haven’t asked about the scar,” he said. “You left it.” “I left you the option.” Her hand dropped. “I don’t want options.” “You want answers.” “I want you to leave.” He didn’t. He looked past her into the flat, at the books, at the mess, at the life she had built after Cardiff, after Brendan and Jennifer, after Pre-Law and the flat above Silas’ bar and the deliveries she made for Yu-Fei. “You still read,” he said. “I still think.” “You still run.” She moved first. She reached for the door. Her palm hit the wood. He caught her wrist, gentle, his thumb over the crescent scar. “Lucien.” His fingers loosened. “Rory.” She pulled free. The skin burned where he touched. “Don’t use that name here.” “Why?” “Because it’s not yours.” “It was always mine.” Ptolemy jumped down and walked between them, tail flicking against Lucien’s polished shoe. He didn’t flinch. “You’re cold,” he said. “You’re trespassing.” “You’re bleeding,” he said. “On the wrist.” She looked down. A thin line of red beaded where the notebook edge had cut her. She hadn’t felt it. “You see things,” she said. “I see you.” She wiped the blood on her jeans. “I need you to go.” “I need you to listen.” She waited. “The ledger you asked about,” he said. “It’s not a ledger. It’s a list of names. Demons who crossed. Humans who paid. Your ex is on it.” Her breath caught. “He hired someone else. A cutter from Avaros. He wants to erase the debt. He wants the woman who hired you dead.” Aurora’s eyes went flat. “Who hired me?” “A woman in Camden. She pays in cash. She doesn’t want a name.” “Why tell me this?” “Because the cutter is already in London.” The stairwell seemed to tilt. She steadied herself against the doorframe. “When?” “Last night.” She closed her eyes. “Where?” “I don’t know yet.” “Then why tell me?” “Because you won’t stay in a flat with three deadbolts and think it’s enough.” She opened her eyes. “It’s Eva’s flat.” “I know.” She searched his face, the heterochromatic eyes, the perfect hair, the cane he always carried like a threat. “You could have sent a message,” she said. “Messages get intercepted.” “You could have called.” “I did. You didn’t answer.” The cat settled on the pile of books and began to knead. Aurora’s shoulders dropped a fraction. “You left,” she said. “I came back.” She looked at the door, at the hallway, at the man blocking the only way out. “Give me a name,” she said. “One name I can use.” He lifted the cane. The thin blade slid free with a whisper. He held it out, tip down, into the flat. “Not a name,” he said. “A choice.”
created_at"2026-09-03T13:57:33.659Z" 9/3/2026, 1:57:33 PM
model"meta/muse-glimmer-30b-20260810"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency283
moderation_latency(null)
generation_time52729
tokens_prompt1004
tokens_completion3464
native_tokens_prompt953
native_tokens_completion3103
native_tokens_completion_images(null)
native_tokens_reasoning1500
native_tokens_cached952
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"stop"
service_tier(null)
usage0.00345168
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.19.0; linux; x64))"
http_referer(null)
request_id"req-1788443853-V2I2YGYQmgWpqYQoAiKO"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1788443853-MTdZ8tXXCZzk7oxYElca"
upstream_id"req_56b0a00682cbe207fae055b87c9b200c"
provider_responses
0
endpoint_id"6244f10e-1e45-488b-a47f-f575e0edd12b"
id"req_56b0a00682cbe207fae055b87c9b200c"
is_byokfalse
latency239
model_permaslug"meta/muse-glimmer-30b-20260810"
provider_name"Phala"
status200
total_cost0.00345168
cache_discount0.00024752
upstream_inference_cost0
provider_name"Phala"
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
totalTags16
adverbTagCount0
adverbTags(empty)
dialogueSentences92
tagDensity0.174
leniency0.348
rawRatio0
effectiveRatio0
95.48% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1105
totalAiIsmAdverbs1
found
0
adverb"slightly"
count1
highlights
0"slightly"
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)
68.33% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1105
totalAiIsms7
found
0
word"weight"
count2
1
word"silence"
count1
2
word"fluttered"
count1
3
word"warmth"
count1
4
word"perfect"
count1
5
word"whisper"
count1
highlights
0"weight"
1"silence"
2"fluttered"
3"warmth"
4"perfect"
5"whisper"
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
narrationSentences96
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount2
narrationSentences96
filterMatches(empty)
hedgeMatches
0"seemed to"
1"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences173
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen42
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1105
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions30
unquotedAttributions0
matches(empty)
87.79% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount643
uniqueNames15
maxNameDensity1.24
worstName"Aurora"
maxWindowNameDensity2
worstWindowName"Aurora"
discoveredNames
Eva1
Lucien6
Moreau1
Brick1
Lane1
Aurora8
Yu-Fei1
Cheung1
Golden1
Empress1
Cardiff1
Brendan1
Jennifer1
Pre-Law1
Ptolemy4
persons
0"Eva"
1"Lucien"
2"Moreau"
3"Aurora"
4"Yu-Fei"
5"Cheung"
6"Empress"
7"Brendan"
8"Jennifer"
9"Ptolemy"
places
0"Brick"
1"Lane"
2"Cardiff"
globalScore0.878
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences42
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1105
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences173
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs109
mean10.14
std10.66
cv1.051
sampleLengths
08
178
220
33
46
525
62
739
83
93
1039
1119
127
1313
142
151
1622
1713
184
193
2011
2115
228
232
2418
2512
2612
2717
284
294
308
317
326
3315
345
356
3635
378
381
3915
405
4115
427
434
4414
4511
464
474
4811
4911
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences96
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs130
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences173
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount565
adjectiveStacks0
stackExamples(empty)
adverbCount18
adverbRatio0.03185840707964602
lyAdverbCount1
lyAdverbRatio0.0017699115044247787
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences173
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences173
mean6.39
std5.13
cv0.803
sampleLengths
08
117
218
324
47
512
617
73
83
96
1021
114
122
134
145
159
167
1714
183
193
203
214
228
2312
248
254
2612
277
287
293
307
313
322
331
348
3510
364
378
385
394
403
414
427
438
443
454
468
472
489
499
42.49% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats13
diversityRatio0.2254335260115607
totalSentences173
uniqueOpeners39
85.47% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences78
matches
0"Bright blue eyes held still."
1"Somewhere a train rattled."
ratio0.026
25.13% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount38
totalSentences78
matches
0"He stood in the narrow"
1"His ivory-handled cane rested against"
2"She stared at him."
3"She stepped back."
4"He was five eleven to"
5"He filled the frame."
6"His mouth thinned."
7"She didn’t look down."
8"She lifted her chin."
9"He studied the books piled"
10"She closed her eyes for"
11"He shifted his weight."
12"His amber eye narrowed."
13"Her hair fell forward, straight"
14"She laughed once, sharp and"
15"She looked at the door,"
16"He stepped forward."
17"He smelled of cold iron"
18"Her hand dropped."
19"He looked past her into"
ratio0.487
0.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount75
totalSentences78
matches
0"The three deadbolts screeched one"
1"Aurora kept her shoulder against"
2"The hallway light from Eva’s"
3"He stood in the narrow"
4"His ivory-handled cane rested against"
5"The amber eye fixed on"
6"Ptolemy launched from the bookshelf"
7"The tabby hissed."
8"Lucien didn’t move."
9"Aurora’s hand found the scar"
10"She stared at him."
11"The flat behind her was"
12"Scrolls and research notes covered"
13"A stack of takeaway menus"
14"She stepped back."
15"The door didn’t close."
16"The hallway smelled of cumin"
17"Lucien took one step up"
18"He was five eleven to"
19"He filled the frame."
ratio0.962
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences78
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences19
technicalSentenceCount0
matches(empty)
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags16
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags15
fancyCount0
fancyTags(empty)
dialogueSentences92
tagDensity0.163
leniency0.326
rawRatio0
effectiveRatio0
86.8225%