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.