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test-optimization
by d-o-hub
A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.
⭐ 3🍴 0📅 Jan 23, 2026
SKILL.md
name: test-optimization description: Advanced test optimization with cargo-nextest, property testing, and performance benchmarking. Use when optimizing test execution speed, implementing property-based tests, or analyzing test performance.
Test Optimization
Advanced test optimization with cargo-nextest, property testing, and benchmarking.
cargo-nextest Integration
# Install
cargo install cargo-nextest --locked
# Run all tests
cargo nextest run
# CI profile (with retries)
cargo nextest run --profile ci
Configuration (.config/nextest.toml)
[profile.default]
slow-timeout = { period = "30s", terminate-after = 3 }
[profile.ci]
retries = 2
fail-fast = false
test-threads = 4
Property-Based Testing (proptest)
proptest! {
#[test]
fn test_episode_id_uniqueness(
tasks in prop::collection::vec(any::<String>(), 1..100)
) {
let rt = tokio::runtime::Runtime::new().unwrap();
rt.block_on(async {
let memory = setup_memory().await;
let mut ids = HashSet::new();
for desc in tasks {
let id = memory.start_episode(desc, ctx, type_).await;
prop_assert!(ids.insert(id));
}
});
}
}
Performance Targets
| Operation | Target | Actual |
|---|---|---|
| Episode Creation | < 50ms | ~2.5 µs |
| Step Logging | < 20ms | ~1.1 µs |
| Pattern Extraction | < 1000ms | ~10.4 µs |
| Memory Retrieval | < 100ms | ~721 µs |
Best Practices
- Use nextest profiles for dev/CI separation
- Implement property tests for edge cases
- Monitor test duration
- Track coverage trends
Score
Total Score
75/100
Based on repository quality metrics
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