Performance Tuning

Organization

adobe

performance-tuning

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.

Overview

Publisher	adobe
Repository	`skills`
Skill name	`performance-tuning`
Stars	298
Forks	92
Bundled files	14
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
14 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by adobe on GitHub. Read the source before you install it.

Installation

Install the Performance Tuning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/adobe/skills/tree/main/plugins/aem/6.5-lts/skills/dispatcher/performance-tuning
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/adobe/skills/tree/main/plugins/aem/6.5-lts/skills/dispatcher/performance-tuning

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/adobe/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/aem/6.5-lts/skills/dispatcher/performance-tuning .claude/skills/performance-tuning

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Tuning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Performance Tuning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Performance Tuning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Capture baseline metrics and cache evidence.
Apply AMS 6.5 guardrails (tier boundaries, variable-driven config, flush ACL safety) to candidate optimizations.
Prioritize low-risk/high-impact changes.
Apply minimal edits.
Verify with validate, lint, and sdk.
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Performance Tuning AI skill do?

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.

Why use Performance Tuning on TypingMind?

Because you install it once and use it with any model. Performance Tuning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Performance Tuning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adobe/skills/tree/main/plugins/aem/6.5-lts/skills/dispatcher/performance-tuning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Performance Tuning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Performance Tuning?

As many as you like. As long as a model supports skills, you can use Performance Tuning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Performance Tuning AI skill free?

Yes. It is published on GitHub by adobe under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Dispatcher Performance Tuning (AMS)

Variant Scope

MCP Tool Contract

Workflow

Verification Scope Selection

Output Contract

Guardrails

References

Bundled files

Frequently asked questions

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