chini-train-train-0277-dp3-personal

Exercise Habit Loop With Travel Disruption

moderate personal problem: exercise habit loop with travel disruption

Source: chini-train synth generator v0.1

Prompt

Design a system for: exercise habit loop with travel disruption (domain: personal systems / habits).

Tier DP3 (moderate). 6-9 nodes, two stress scenarios (spike + one of cascade/outage), realistic criteria. Sweet spot.

Constraints:
- At most 10 components on the canvas.
- Monthly cost ceiling: $459 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: 10
Required behaviors: queue, circuitbreaker, retry
Monthly budget: $459

Stress scenarios

Baseline traffic

baseline

Steady ambient load with no failures.

Cascading failure

cascade

An initial fault propagates through dependent components.

Latency injection

latency

Extra latency injected into a critical component. Tests degradation behavior under slow downstreams.

Pass criteria (overall)

Min stability score: 73
Max drop rate: 11.2%
Min delivery rate: 83.7%
Max errors: 8

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-0277-dp3-personal \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice

Or inspect the prompt first:

chini-bench prompt chini-train-train-0277-dp3-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	85	66.0	75.0	100.0	✗
#2	rl_v07_full_a10	rl_policy custom single-shot	85	66.0	75.0	100.0	✗
#3	rl_v07_full_a10	rl_policy custom single-shot	85	66.0	75.0	100.0	✗
#4	rl_v07_full_a10	rl_policy custom single-shot	85	66.0	75.0	100.0	✗
#5	chini-train-03	grok-4.1-fast single-shot	81	61.0	64.0	100.0	✗

Per-scenario breakdown of the top run

Scenario	Health	Drop rate	Delivered	Pass
baseline	81.0	1.7%	29	✓
cascade-1	36.0	53.2%	11	✗
latency-2	80.0	1.4%	35	✓

How is this scored? →