인터페이스는 현지화되어 있으며, Skill 소개와 설명 문서는 선택한 언어로 AI가 자동 번역합니다. 코드·명령어·경로·전문 용어는 원문을 유지하며 원작자 문서가 우선합니다.
Skills 둘러보기
생산성 및 협업 · SKILL.md

performance-tuning

performance-tuning

Optimize performance of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AEMaaCS cloud workflows only, with cloud-specific baseline and runtime verification.

Independent GitHub snapshot · Automated classification · Original documentation governs

Aadobe / skillsApache-2.0

Skill 문서

GitHub

name: performance-tuning description: Optimize performance of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AEMaaCS cloud workflows only, with cloud-specific baseline and runtime verification. license: Apache-2.0 compatibility: Requires Dispatcher MCP configured for cloud variant (AEM_DEPLOYMENT_MODE=cloud). allowed-tools: validate lint sdk trace_request inspect_cache monitor_metrics tail_logs metadata: mcp-tool-contract: core-7-tools

Dispatcher Performance Tuning (Cloud)

Improve cache efficiency, latency, and throughput for cloud deployments that use the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration.

Variant Scope

  • This skill is cloud-service-only.
  • Scope is fixed by this skill directory; do not ask the user to choose deployment variant.

MCP Tool Contract

Use only these Dispatcher MCP tools:

  • validate
  • lint
  • sdk
  • trace_request
  • inspect_cache
  • monitor_metrics
  • tail_logs

Workflow

  1. Capture baseline metrics and cache evidence.
  2. Apply cloud guardrails (immutable/default includes, reserved probe paths, and CDN-vs-Dispatcher ownership) before proposing changes.
  3. Prioritize low-risk/high-impact changes.
  4. Apply minimal edits.
  5. Verify with validate, lint, and sdk.
  6. Compare before/after runtime evidence.

Verification Scope Selection

Use shared references to select optimization evidence depth:

Output Contract

Always return:

  • baseline metrics snapshot
  • prioritized optimization list with impact/risk
  • changed files and intent
  • executed checks + before/after evidence
  • selected test IDs and outcomes
  • rollback plan and open risks

Guardrails

  • Do not claim improvement without measurable comparison.
  • Keep high-risk tuning opt-in unless user explicitly requests it.
  • Keep cloud assumptions explicit for each recommendation batch.
  • Route edge/WAF/CDN-only concerns to CDN layer guidance instead of Dispatcher config changes.

References

저장한 Skills