菜单
Compose deterministic DSDS simulation scenarios from resilience hypotheses, load-test goals, or failure drills. Use when defining workload plus fault plus invariant bundles, built-in presets, and chaos experiments with reproducible seeds and explicit pass/fail criteria.
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基于 SOC 职业分类
| name | scenario-composer |
| description | Compose deterministic DSDS simulation scenarios from resilience hypotheses, load-test goals, or failure drills. Use when defining workload plus fault plus invariant bundles, built-in presets, and chaos experiments with reproducible seeds and explicit pass/fail criteria. |
Design reproducible scenarios that test one clear system behavior at a time. Package each scenario with deterministic timing, explicit hypotheses, and measurable success criteria.
p99 latency, error rate, recovery time, or cost).seed, simulationDuration, and warmupDuration.constant, poisson, spike, diurnal, bursty, sawtooth, replay).deterministic, probabilistic, conditional) and fault duration.references/scenario-patterns.md for templates, built-in mappings, and anti-pattern guardrails.canonical-catalogue/DSDS Canonical Catalogue - scenarios to ship.csvcanonical-catalogue/DSDS Canonical Catalogue - Failure modes & propagation semantics.csvcanonical-catalogue/DSDS Canonical Catalogue - Patterns for scenario generation.csvplanning/IMPLEMENTATION_PLAN.md (Phase 7)Design DSDS chaos experiments with deterministic setup, explicit hypotheses, and measurable outcomes. Use when planning failure drills, resilience validation, pre-production risk exercises, and repeatable chaos scenarios for regression testing.
Map architecture requirements to valid DSDS component categories and component types. Use when converting system requirements into simulator node selections, resolving ambiguous type choices, and ensuring required attributes are captured before topology authoring.
Map DSDS architectures across AWS, GCP, and Azure equivalents and estimate cost-impact tradeoffs. Use when planning provider portability, comparing cloud options, and explaining cost/performance implications of design choices.
Convert DSDS UI specs, mock HTML screens, and system docs into concrete frontend component implementation tasks. Use when turning design references into React components, hooks, state contracts, and acceptance criteria.
Analyze likely cascade paths and propagation semantics in DSDS architectures. Use when estimating blast radius, diagnosing multi-hop degradation, validating containment strategies, or explaining how a localized failure can become systemic.
Define and evaluate deterministic invariants and policy checks for DSDS simulations. Use when encoding correctness rules, post-run validation gates, compliance constraints, and safety checks that must hold across scenarios.