agentmods.dev

performance-tuning

A guide for improving Adobe Dispatcher, an Apache web-server module that caches and serves AEM content, in Adobe Managed Services (AMS) deployments running AEM 6.5.

In plain words
What is it for?
Improving cache use, response time, and request capacity; reviewing HTTPD and Dispatcher settings; and verifying changes with configuration checks and runtime evidence.
Why use it?
It helps find performance changes without guessing, while checking their effect against baseline measurements and AMS-specific safety rules.

Skill for Claude CodeCodex

175 today A scan 39 tokens original Apache-2.0
View the source file adobe/skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/adobe/skills/performance-tuning
Any agent
npx skills add adobe/skills --skill performance-tuning
Clone the repo
git clone --depth 1 https://github.com/adobe/skills

Made for: Claude Code, Codex.

Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 810 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00039 $0.00810
Opus 5 $0.00019 $0.00405
Sonnet 5 $0.00008 $0.00162
Haiku 4.5 $0.00004 $0.00081

Measured 2d ago against content hash db64e11d52af, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/aem/6.5-lts/skills/dispatcher/performance-tuning/SKILL.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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

Read the full file on GitHub · 81 lines

Files

What ships with it

15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 81 lines · 39 tokens per session scan A db64e11d52af

Subscribe to this mod's changes

performance-tuning is a skill published in the GitHub repository adobe/skills (175 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 810 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

adobeai-agentsskills
Related

Other skills, from other repositories

claude-md-improver

Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…

anthropics/claude-plugins-official · 82 tokens

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

twitter-reader

Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…

himself65/finance-skills · 161 tokens

docx-comment-reply

Reply to comments (批注) in Word .docx/.doc files: extract comment context, draft replies, write threaded replies back, and validate OOXML.

foryourhealth111-pixel/Vibe-Skills · 39 tokens

apple-search-ads

When the user wants to set up, optimize, or scale Apple Search Ads (ASA) campaigns — including keyword bidding, match types, campaign structure, Creative Product Sets, CPP routing, and ROAS optimization. Use when the user mentions "Apple Search Ads", "ASA", "Search Ads", "Search tab ads", "Today tab ads", "CPT"…

Eronred/aso-skills · 127 tokens

chenhao-limit-up

Use when evaluating A-share limit-up (涨停板) setups through Chen Hao's sentiment and momentum lens: market emotion cycles, board strength, follow-through, and short-term aggressive momentum trading.

questflowai/investorskills · 44 tokens