BMAD Performance Optimization Skill
When to Invoke
Trigger this skill when the user:
- Reports latency, throughput, or resource regressions.
- Requests load/performance testing guidance or results interpretation.
- Needs to set or validate performance budgets and SLAs.
- Wants to plan scaling strategies ahead of a launch or marketing event.
- Asks how to tune code, queries, caching, or infrastructure for speed.
If the user only needs to implement a specific optimization already defined, delegate to bmad-development-execution.
Mission
Deliver actionable insights, testing strategies, and prioritized optimizations that keep the product within agreed performance budgets while balancing cost and complexity.
Inputs Required
- Current architecture diagrams and deployment topology.
- Observability data: metrics dashboards, traces, profiling dumps, load test reports.
- Performance requirements (SLAs/SLOs, budgets, target response times).
- Workload assumptions and peak usage scenarios.
Gather missing telemetry by coordinating with bmad-observability-readiness if instrumentation is lacking.
Outputs
- Performance brief summarizing current state, key bottlenecks, and risks.
- Benchmark and load test plan aligning tools, scenarios, and success criteria.
- Optimization backlog ranked by impact vs. effort with owner and verification plan.
- Updated performance budget recommendations or SLO adjustments when necessary.
Process
- Validate inputs and ensure instrumentation coverage. Escalate gaps to observability skill.
- Analyze telemetry to pinpoint hotspots (CPU, memory, I/O, DB, network, frontend paint times).
- Assess architecture decisions for scalability (caching, asynchronous workflows, data partitioning).
- Define performance goals and acceptance thresholds with stakeholders.
- Create load/benchmark plans covering baseline, stress, soak, and spike scenarios.
- Recommend optimizations across code, database, infrastructure, and CDN layers.
- Produce backlog with measurable acceptance criteria and regression safeguards.
Quality Gates
- Recommendations trace back to observed data or projected workloads.
- Each backlog item includes measurement approach (before/after metrics).
- Performance budgets and SLAs updated or reaffirmed.
- Risks communicated when goals require major architectural change.
Error Handling
- If telemetry contradicts assumptions, schedule hypothesis-driven experiments rather than guessing.
- Flag when performance targets are unrealistic within constraints; propose trade-offs.
- When required tooling is unavailable, document blockers and coordinate with observability & dev skills.