Performance Tuning

adobe/skills/plugins/aem/6.5-lts/skills/dispatcher/performance-tuning

by adobe940b8795c0dfApache-2.0197 starsListed Oct 9, 2026Updated Oct 8, 2026Repository updated today

Optimize performance of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AEM 6.5 / AMS workflows only, with AMS-specific baseline and runtime verification.

Instructions onlyDevOps & Cloud
AI-generated overview

Tunes Adobe Dispatcher and HTTPD performance for AEM 6.5 AMS deployments using Dispatcher MCP tools and verification.

What it does
Guides performance tuning of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AMS deployments. It captures baseline metrics and cache evidence, applies AMS 6.5 guardrails, prioritizes low-risk changes, makes minimal edits, and verifies with validate, lint, and sdk. It returns a baseline snapshot, a prioritized optimization list with impact and risk, changed files, before/after evidence, selected test IDs, and a rollback plan.
When to use it
Use when an AMS AEM 6.5 deployment using the Adobe Dispatcher module needs better cache efficiency, latency, or throughput. It fits requests to diagnose and optimize Dispatcher or HTTPD configuration with measurable before/after evidence.
Requirements
Requires Dispatcher MCP for AMS (AEM_DEPLOYMENT_MODE=ams) or the AMS Dispatcher MCP SDK preset to ams, with the tools validate, lint, sdk, trace_request, inspect_cache, monitor_metrics, and tail_logs. Ships no scripts; it is instructions plus reference documents.

Dispatcher Performance Tuning (AMS)

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

Variant Scope

  • This skill is AMS-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 AMS 6.5 guardrails (tier boundaries, variable-driven config, flush ACL safety) to candidate optimizations.
  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:

  • mode-specific-verification-matrix.md
  • test-case-catalog.md

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 AMS assumptions explicit for each recommendation batch.

References

  • optimization-patterns.md
  • performance-scenario-playbooks.md – scenario-driven tuning flows adapted from broader MCP prompt surfaces
  • load-testing-guidance.md
  • performance-monitoring-setup.md
  • quick-start-execution-path.md – fast entry path for optimization requests
  • repo-layout-workflows.md – map performance findings to cache, farm, vhost, rewrite, and variable file families
  • playbook-command-linkage.md – exact MCP command chains for tuning playbooks
  • ams-6-5-guardrails.md
  • mode-specific-verification-matrix.md
  • test-case-catalog.md
  • change-risk-and-rollback-template.md
  • public-docs-index.md
  • public-doc-citation-rules.md
  • core-7-tools-reference.md

Source and attribution

Source:adobe/skillsinplugins/aem/6.5-lts/skills/dispatcher/performance-tuningat commit940b879

License: Apache-2.0

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