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adobe/aem-cloud-service

All AEM as a Cloud Service skills: component development, Workflow model design, development, triggering, launchers, debugging, and triaging, Dispatcher config authoring, advisory, incident response, performance tuning, and security hardening, Rapid Development Environment (RDE) deployment, log inspection, snapshots, and troubleshooting via `aio aem rde`, Jackrabbit Oak query/index tuning (property, Lucene index definitions), and extracting/inventorying JCR queries (XPath, SQL2, QueryBuilder) across a codebase.

Quality

85%

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SecuritybySnyk

Low

Low-risk findings worth noting

Overview
Quality
Evals
Security
Files

SKILL.mdskills/dispatcher/performance-tuning/

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:

  • 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 cloud assumptions explicit for each recommendation batch.
  • Route edge/WAF/CDN-only concerns to CDN layer guidance instead of Dispatcher config changes.

References

skills

dispatcher

README.md

SKILL.md

README.md

tile.json