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chini-train-train-0162-dp5-personal

Study Schedule With Willpower Drain

brutal personal problem: study schedule with willpower drain

Source: chini-train synth generator v0.1

Prompt

Design a system for: study schedule with willpower drain (domain: personal systems / habits).

Tier DP5 (brutal). 9-12 nodes, four scenarios with high intensity, brutal criteria. Failing examples.

Constraints:
- At most 12 components on the canvas.
- Monthly cost ceiling: $332 USD. Required behaviors: queue, circuitbreaker, retry.

Return a Chinilla CanvasState that handles the listed scenarios. Include trigger components for each entry point and at least one terminal storage / sink so the simulator can score delivery.

Constraints

Max components
12
Required behaviors
queue, circuitbreaker, retry
Monthly budget
$332

Stress scenarios

Baseline traffic

baseline

Steady ambient load with no failures.

Cascading failure

cascade

An initial fault propagates through dependent components.

Traffic spike

spike

Traffic suddenly multiplies. The hot path must hold.

Adversarial burst

adversarial

Hostile packets injected on top of clean traffic. Defenses must block them without dropping good requests.

Pass criteria (overall)

Min stability score
78
Max drop rate
7.3%
Min delivery rate
88.0%
Max errors
5

Submit your run

Submissions go through the chini-bench CLI. It calls your model with your key, scores the result locally, and posts to the leaderboard. Nothing leaves your machine except the canvas it produces.

End-to-end:
pip install git+https://github.com/collapseindex/chini-bench-cli.git
export OPENROUTER_API_KEY=...

chini-bench run chini-train-train-0162-dp5-personal \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice
Or inspect the prompt first:
chini-bench prompt chini-train-train-0162-dp5-personal
Providers: openai · anthropic · google · openrouter · ollama

Leaderboard

Rank Submitter Model Score Stability Delivery Design Pass
#1 rl_v07_full_a10
rl_policy
custom single-shot
78 56.0 73.0 100.0
#2 rl_v07_full_a10
rl_policy
custom single-shot
78 56.0 73.0 100.0
#3 rl_v07_full_a10
rl_policy
custom single-shot
77 54.0 71.0 100.0
#4 rl_v07_full_a10
rl_policy
custom single-shot
73 57.0 72.0 75.0
#5 chini-train-03
grok-4.1-fast
single-shot
63 29.0 53.0 100.0
#6 chini-train-04
grok-4.1-fast
single-shot
63 29.0 53.0 100.0
Per-scenario breakdown of the top run
Scenario Health Drop rate Delivered Pass
baseline 81.0 1.7% 29
cascade-1 28.0 67.4% 7
spike-2 64.0 14.1% 1266
adversarial-3 49.0 85.7% 112