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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.

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설치 방법 보기

포함된 파일(17)

  • SKILL.md3.8 KB
  • references/dispatcher-foundation/change-risk-and-rollback-template.md1016 B
  • references/dispatcher-foundation/cloud-service-aemaacs-guardrails.md10.3 KB
  • references/dispatcher-foundation/core-7-tools-reference.md4.4 KB
  • references/dispatcher-foundation/local-sdk-execution.md7.2 KB
  • references/dispatcher-foundation/mode-specific-verification-matrix.md4.1 KB
  • references/dispatcher-foundation/playbook-command-linkage.md7.9 KB
  • references/dispatcher-foundation/public-doc-citation-rules.md2.0 KB
  • references/dispatcher-foundation/public-docs-index.md8.0 KB
  • references/dispatcher-foundation/quick-start-execution-path.md3.4 KB
  • references/dispatcher-foundation/README.md483 B
  • references/dispatcher-foundation/repo-layout-workflows.md3.7 KB
  • references/dispatcher-foundation/test-case-catalog.md4.1 KB
  • references/performance-tuning/load-testing-guidance.md5.3 KB
  • references/performance-tuning/optimization-patterns.md9.1 KB
  • references/performance-tuning/performance-monitoring-setup.md7.2 KB
  • references/performance-tuning/performance-scenario-playbooks.md3.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

리뷰

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