bmad-performance-optimization

bacoco's avatarfrom bacoco

Diagnoses bottlenecks and designs performance optimization plans.

⭐1stars🔀0forks📁View on GitHub🕐Updated Nov 4, 2025⬇️1downloads

When & Why to Use This Skill

This Claude skill is a specialized performance engineering agent designed to diagnose system bottlenecks and architect comprehensive optimization plans. It leverages telemetry data, profiling results, and load testing metrics to identify latency regressions and throughput constraints. By analyzing architecture and resource utilization, it provides actionable roadmaps, performance budgets, and prioritized backlogs to ensure applications remain scalable, responsive, and cost-effective.

Use Cases

  • Latency & Bottleneck Diagnosis: Analyzing profiling dumps and telemetry to pinpoint the root cause of slow response times or resource regressions in production environments.
  • Load Testing & Capacity Planning: Designing detailed stress and soak test plans to validate system stability and determine infrastructure requirements for upcoming high-traffic events.
  • Performance Budgeting & SLO Management: Establishing and validating performance budgets and Service Level Objectives (SLOs) to ensure long-term system reliability and user satisfaction.
  • Architectural Scalability Audits: Reviewing current deployment topologies and codebases to recommend optimizations in caching, asynchronous workflows, and database partitioning.
namebmad-performance-optimization
descriptionDiagnoses bottlenecks and designs performance optimization plans.
allowed-tools["Read", "Write", "Grep", "Bash"]
auto-invoketrue

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

  1. Validate inputs and ensure instrumentation coverage. Escalate gaps to observability skill.
  2. Analyze telemetry to pinpoint hotspots (CPU, memory, I/O, DB, network, frontend paint times).
  3. Assess architecture decisions for scalability (caching, asynchronous workflows, data partitioning).
  4. Define performance goals and acceptance thresholds with stakeholders.
  5. Create load/benchmark plans covering baseline, stress, soak, and spike scenarios.
  6. Recommend optimizations across code, database, infrastructure, and CDN layers.
  7. 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.