Config Authoring logo

Config Authoring

Organization
adobe
config-authoring

Create, modify, review, and harden configuration for the Adobe Dispatcher Apache HTTP Server module and Apache HTTPD in AEM as a Cloud Service environments only. Use for `.any`, vhost, rewrite, cache, and filter changes.

Overview

Publisheradobe
Repositoryskills
Skill nameconfig-authoring
Stars
298
Forks
92
Bundled files
16
LicenseApache-2.0
Links
  • 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.

  • 16 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 Config Authoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/adobe/skills/tree/main/plugins/aem/cloud-service/skills/dispatcher/config-authoring
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/cloud-service/skills/dispatcher/config-authoring .claude/skills/config-authoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Config Authoring 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 Config Authoring 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 Config Authoring 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 Config Authoring (Cloud)

Design minimal, deterministic changes for the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration in AEMaaCS, then verify with MCP evidence.

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

text
Cloud Config Progress
- [ ] 1) Confirm scope and acceptance criteria; if the repo layout is unclear, normalize it with `repo-layout-workflows.md`
- [ ] 2) Apply cloud guardrails (immutable files, required includes, symlink topology, required wildcard ServerAlias coverage, reserved probe paths, preserved cloud vhost defaults, CDN-vs-Dispatcher boundary)
- [ ] 3) Decompose target URLs (path/selectors/extension/suffix) and use that model for all URL-based rules—filters, cache rules, etc.—using `/path`, `/selectors`, `/extension`, `/suffix` or aligned globs (not raw `/url`) where applicable; then design complete section-level edits
- [ ] 4) Update config with least-privilege defaults (produce final merged section, not isolated rule snippets)
- [ ] 5) Run static checks: validate -> lint (deep/order-aware when filters changed)
- [ ] 6) Run SDK checks: `sdk({"action":"check-files","config_path":"<dispatcher src path>"})`, `sdk({"action":"diff-baseline","config_path":"<dispatcher src path>"})` as needed
- [ ] 7) Run runtime verification in container-backed environment
- [ ] 8) Return diff, evidence table, risk/rollback, and citations

Verification Scope Selection

Use the shared references to select the minimum evidence set:

Output Contract

Always include:

  • files changed + intent
  • exact insertion location and final merged section content for each edited block
  • checks executed + evidence
  • selected test IDs
  • risk/rollback plan
  • residual risks and next checks

Feature cross-boundary (Playbook G)

Permission-sensitive caching (/auth_checker) is end-to-end: it requires both Dispatcher config and an AEM servlet. When implementing Playbook G from scratch, create or verify the auth-check servlet in the project core bundle (path /bin/permissioncheck, HEAD/GET, 200 or 403) and allowlist it on publish; then add the /auth_checker block, filter allow for the endpoint, and /allowAuthorized "1" in /cache. See config-scenario-playbooks.md Playbook G and reference-snippets.md.

Guardrails

  • Do not weaken deny-by-default security posture without explicit user approval.
  • Do not claim runtime verification if container/runtime prerequisites were missing.
  • Keep changes minimal and scoped.
  • Enforce cloud guardrails from cloud-service-aemaacs-guardrails.md before proposing config edits.

References

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 Config Authoring AI skill do?

Create, modify, review, and harden configuration for the Adobe Dispatcher Apache HTTP Server module and Apache HTTPD in AEM as a Cloud Service environments only. Use for `.any`, vhost, rewrite, cache, and filter changes.

Why use Config Authoring on TypingMind?

Because you install it once and use it with any model. Config Authoring 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 Config Authoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adobe/skills/tree/main/plugins/aem/cloud-service/skills/dispatcher/config-authoring. 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 Config Authoring?

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 Config Authoring?

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

Is the Config Authoring 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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