chini-train-train-0328-dp4-civic

Dmv Appointment System At Month-End

hard civic problem: DMV appointment system at month-end

Source: chini-train synth generator v0.1

Prompt

Design a system for: DMV appointment system at month-end (domain: civic / public service).

Tier DP4 (hard). 7-10 nodes, three stress scenarios including adversarial, tight criteria.

Constraints:
- At most 11 components on the canvas.
- Monthly cost ceiling: $366 USD. Required behaviors: queue, circuitbreaker, retry, ratelimit.

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: 11
Required behaviors: queue, circuitbreaker, retry, ratelimit
Monthly budget: $366

Stress scenarios

Baseline traffic

baseline

Steady ambient load with no failures.

Traffic spike

spike

Traffic suddenly multiplies. The hot path must hold.

Latency injection

latency

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

Cascading failure

cascade

An initial fault propagates through dependent components.

Pass criteria (overall)

Min stability score: 79
Max drop rate: 8.8%
Min delivery rate: 87.5%
Max errors: 6

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-0328-dp4-civic \
  --provider openrouter --model google/gemini-2.0-flash-001 \
  --as alice

Or inspect the prompt first:

chini-bench prompt chini-train-train-0328-dp4-civic

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	67.0	69.0	100.0	✗
#2	rl_v07_full_a10	rl_policy custom single-shot	84	63.0	72.0	100.0	✗
#3	rl_v07_full_a10	rl_policy custom single-shot	84	62.0	71.0	100.0	✗
#4	rl_v07_full_a10	rl_policy custom single-shot	84	64.0	73.0	100.0	✗
#5	chini-train-03	grok-4.1-fast single-shot	71	35.0	45.0	100.0	✗
#6	chini-train-04	grok-4.1-fast single-shot	71	35.0	45.0	100.0	✗

Per-scenario breakdown of the top run

Scenario	Health	Drop rate	Delivered	Pass
baseline	84.0	3.4%	28	✓
spike-1	72.0	13.7%	1092	✗
latency-2	79.0	5.9%	32	✓
cascade-3	34.0	67.4%	7	✗

How is this scored? →