Agent Skills › bacoco/BMad-Skills › bmad-performance-optimization

bmad-performance-optimization

GitHub

诊断系统性能瓶颈，设计优化方案。涵盖延迟/资源问题排查、负载测试指导、SLA验证及扩缩容规划。通过分析遥测数据识别热点，产出性能简报、基准测试计划及优先级优化待办事项，平衡成本与复杂度。

.claude/skills/bmad-performance-optimization/SKILL.md bacoco/BMad-Skills

Trigger Scenarios

报告延迟、吞吐量或资源回归请求负载测试指导或结果解读需要设定或验证性能预算和SLA 询问如何调优代码、查询、缓存或基础设施

Install

npx skills add bacoco/BMad-Skills --skill bmad-performance-optimization -g -y

More Options

Non-standard path

npx skills add https://github.com/bacoco/BMad-Skills/tree/main/.claude/skills/bmad-performance-optimization -g -y

Use without installing

npx skills use bacoco/BMad-Skills@bmad-performance-optimization

指定 Agent (Claude Code)

npx skills add bacoco/BMad-Skills --skill bmad-performance-optimization -a claude-code -g -y

安装 repo 全部 skill

npx skills add bacoco/BMad-Skills --all -g -y

预览 repo 内 skill

npx skills add bacoco/BMad-Skills --list

SKILL.md

Frontmatter

{
    "name": "bmad-performance-optimization",
    "metadata": {
        "outputs": [
            "performance-brief",
            "benchmark-plan",
            "optimization-backlog"
        ],
        "triggers": {
            "keywords": [
                "performance",
                "latency",
                "throughput",
                "optimize",
                "scaling",
                "profiling",
                "benchmarking"
            ],
            "patterns": [
                "performance budget",
                "optimize speed",
                "slow response",
                "profiling results",
                "latency regression",
                "load testing",
                "capacity planning"
            ]
        },
        "auto-invoke": true,
        "capabilities": [
            "performance-audits",
            "load-testing-plans",
            "profiling-analysis",
            "optimization-roadmaps",
            "capacity-planning"
        ],
        "prerequisites": [
            "bmad-architecture-design",
            "bmad-test-strategy"
        ]
    },
    "description": "Diagnoses bottlenecks and designs performance optimization plans.",
    "allowed-tools": [
        "Read",
        "Write",
        "Grep",
        "Bash"
    ]
}

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.