Run 2026-02-17-01KHMQPVBC0QATR9SE4HNJJ33G

Run Details

Model:: Qwen 3.5 397B A17B
Test:: Bad Writing Habits
Scenario:: Thriller: chase through city streets
Duration:: 1m 14s
Cost:: $0.015293
Provider:: Alibaba

Model input

System

You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue.

You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex.

Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.

User/Human

<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>

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.

Model response

AI

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Evaluation details

Result

Evaluator

Details

Meta Data

100.0000%

Adverbs in dialogue tags

Target: ≤10% dialogue tags with adverbs

totalTags	0
adverbTagCount	0
adverbTags	(empty)

100.0000%

AI-ism adverb frequency

Target: <1% AI-ism adverbs (57 tracked)

wordCount	717
totalAiIsmAdverbs	0
found	(empty)
highlights	(empty)

100.0000%

AI-ism character names

Target: 0 AI-default names (17 tracked, −20% each)

codexExemptions	(empty)
found	(empty)

100.0000%

AI-ism location names

Target: 0 AI-default location names (33 tracked, −20% each)

codexExemptions	(empty)
found	(empty)

100.0000%

AI-ism word frequency

Target: <1% AI-ism words (140 tracked)

wordCount	717
totalAiIsms	0
found	(empty)
highlights	(empty)

100.0000%

Cliche density

Target: ≤1 cliche(s) per 800-word window

totalCliches	0
maxInWindow	0
found	(empty)
highlights	(empty)

100.0000%

Emotion telling (show vs. tell)

Target: ≤3% sentences with emotion telling

emotionTells	0
narrationSentences	97
matches	(empty)

100.0000%

Filter word density

Target: ≤12% sentences with filter/hedge words

filterCount	0
hedgeCount	0
narrationSentences	97
filterMatches	(empty)
hedgeMatches	(empty)

100.0000%

Overuse of "that" (subordinate clause padding)

Target: ≤10% sentences with "that" clauses

thatCount	0
totalSentences	97
matches	(empty)

97.6400%

Paragraph length variance

Target: CV ≥0.5 for paragraph word counts

totalParagraphs

64

mean

11.2

std

5.51

cv

0.492

sampleLengths

0	6
1	6
2	17
3	6
4	17
5	6
6	17
7	6
8	17
9	6
10	17
11	6
12	17
13	6
14	17
15	6
16	17
17	6
18	17
19	6
20	17
21	6
22	17
23	6
24	17
25	6
26	17
27	6
28	17
29	6
30	17
31	12
32	6
33	17
34	6
35	17
36	6
37	17
38	6
39	17
40	6
41	17
42	6
43	17
44	6
45	17
46	6
47	17
48	6
49	17

100.0000%

Passive voice overuse

Target: ≤5% passive sentences

passiveCount	0
totalSentences	97
matches	(empty)

100.0000%

Past progressive (was/were + -ing) overuse

Target: ≤10% past progressive verbs

pastProgressiveCount	0
totalVerbs	186
matches	(empty)

98.3891%

Purple prose (modifier overload)

Target: <4% adverbs, <2% -ly adverbs, no adj stacking

wordCount	717
adjectiveStacks	0
stackExamples	(empty)
adverbCount	30
adverbRatio	0.04184100418410042
lyAdverbCount	0
lyAdverbRatio	0

100.0000%

Repeated phrase echo

Target: ≤20% sentences with echoes (window: 2)

totalSentences	97
echoCount	0
echoWords	(empty)

77.1546%

Sentence length variance

Target: CV ≥0.4 for sentence word counts

totalSentences

97

mean

7.39

std

2.53

cv

0.343

sampleLengths

0	1
1	5
2	6
3	11
4	6
5	6
6	11
7	6
8	6
9	11
10	6
11	6
12	11
13	6
14	6
15	11
16	6
17	6
18	11
19	6
20	6
21	11
22	6
23	6
24	11
25	6
26	6
27	11
28	6
29	6
30	11
31	6
32	6
33	11
34	6
35	6
36	11
37	6
38	6
39	11
40	6
41	6
42	11
43	6
44	6
45	11
46	6
47	6
48	1
49	5

50.0000%

Sentence opener variety

Target: ≥60% unique sentence openers

consecutiveRepeats	0
diversityRatio	0.041237113402061855
totalSentences	97
uniqueOpeners	4

100.0000%

Dialogue tag variety (said vs. fancy)

Target: ≤30% fancy dialogue tags

totalTags	0
fancyCount	0
fancyTags	(empty)

95.4814%

Run: 2026-02-17-01KHMQPVBC0QATR9SE4HNJJ33G

Run Details

Model input

Model response

Evaluation details