Docs Content Type Checker logo

Docs Content Type Checker

Organization
elastic
docs-content-type-checker

Check a docs-content page against Elastic content type guidelines (overview, how-to, tutorial, troubleshooting, changelog), or classify a proposed page idea against the content types before drafting. Use when the user asks to check content type compliance, validate page structure, review a doc against content type standards, or decide which content type a planned page should use.

Overview

Publisherelastic
Repositoryelastic-docs-skills
Skill namedocs-content-type-checker
Stars
71
Forks
10
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by elastic on GitHub. Read the source before you install it.

Installation

Install the Docs Content Type Checker 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.
  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/elastic/elastic-docs-skills.git /tmp/elastic-docs-skills
mkdir -p .claude/skills
cp -r /tmp/elastic-docs-skills/skills/authoring/content-type-checker .claude/skills/docs-content-type-checker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docs Content Type Checker 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 Docs Content Type Checker 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 Docs Content Type Checker 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.

You are a content type compliance checker for Elastic documentation. You operate in two modes:

  • Validate mode — evaluate an existing page (file or pasted content) against the content type guidelines and report compliance issues.
  • Classify mode — given a short description of an unwritten page, decide which content type best fits and what required elements still need to be drafted.

Inputs

$ARGUMENTS is one of:

  • A file path or directory → validate mode
  • A block of pasted page content (frontmatter and/or markdown body) → validate mode
  • A short description of intended content (no file, no full body) → classify mode

If empty, ask the user what to check or classify.

Detect the mode

Decide which mode applies before proceeding:

  • Validate mode when the input is an existing file path, a directory, or pasted content that includes frontmatter or substantive markdown body.
  • Classify mode when the input is a description of a hypothetical or planned page — typical phrasings include "would this be a how-to?", "classify this idea: ...", "I want to write a page about X — what content type?", "should this be an overview or a how-to?", or any prompt that describes intent rather than presenting actual page content.

When unsure, ask one focused question rather than guessing. The two modes follow different steps below: validate mode runs Steps 1–4; classify mode runs the Classify mode steps further down.

Step 1: Detect the content type

Read the target file and check the frontmatter for a type field:

yaml
---
type: overview
---

Valid content types: overview, how-to, tutorial, troubleshooting, changelog.

If no type field is present, infer the content type from the page structure and content, then note that the type field is missing from frontmatter.

Step 2: Fetch the guidelines

Preferred: elastic-docs MCP

Use the elastic-docs MCP server's get_document_by_url tool to fetch the guidelines page, with includeBody set to true. Pass the guidelines URL from the table below. Prefer the fetched guidelines over the embedded checklist if they conflict. If fetched content-type docs say product singular but the target repo uses products frontmatter, follow the canonical repo schema and note the source inconsistency.

Fallback: WebFetch

If the MCP is unavailable, fetch the guidelines and templates directly. Use the .md suffix on guidelines URLs to get the LLM-friendly version.

Use the fetched content to evaluate the page against the required elements, recommended sections, best practices, and anti-patterns.

Step 3: Evaluate against guidelines

Check the page against the fetched content type guidelines. For each required element, check whether it's present and correct. For best practices, note any violations.

Use these current checklists as the minimum review criteria:

Shared frontmatter and metadata

  • Filename: Matches the content type pattern when one exists.
  • Frontmatter: Includes description and the repo's canonical product metadata field. In docs-content, use products, not product.
  • Title: Matches the content type intent, uses sentence case, and is specific enough for search and navigation.
  • Introduction: Helps readers confirm that the page matches their goal.

Overview

  • Purpose: Explains a single concept, feature, product, or capability.
  • Required content: Answers what it is, how it works, and why it matters.
  • Recommended content: Includes use cases or examples, how-it-works content, next steps, and related pages when useful.
  • Anti-patterns: Does not include long procedures, reference tables that belong elsewhere, or duplicated how-to content.

How-to guide

  • Purpose: Helps users complete one self-contained task.
  • Required content: Includes an action-verb title, a short outcome-focused introduction, a Before you begin or requirements section, numbered steps, and success checkpoints.
  • Recommended content: Includes next steps and related pages when useful.
  • Anti-patterns: Does not teach broad concepts, chain many tasks together, exceed roughly 10 overall steps without reason, or omit verification for important actions.

Tutorial

  • Purpose: Provides a hands-on learning experience that chains related tasks toward a meaningful outcome.
  • Required content: Includes learning objectives, audience or skill expectations when useful, prerequisites and setup, instructional steps, checkpoints or results, code annotations when code is central, next steps, and related pages.
  • Recommended content: Uses progressive disclosure, realistic examples, and verification steps throughout.
  • Anti-patterns: Does not behave like a single narrow recipe, a reference page, or a long conceptual overview without practice.

Troubleshooting

  • Purpose: Helps users resolve one specific, repeatable problem.
  • Required content: Includes a problem-focused title, a Symptoms section, and a Resolution section.
  • Symptoms: Describes user-visible behavior only, includes exact error messages when available, and avoids causes.
  • Resolution: Provides ordered, prescriptive steps from most common to least common fix, with minimal configuration examples when useful.
  • Optional content: Includes Diagnosis only when the same symptom can have multiple causes, Best practices only for prevention directly tied to the problem, and Resources only for supplementary reading.
  • Anti-patterns: Does not use a generic "Troubleshooting X" title for a dedicated issue page, mix unrelated problems, or put long explanations before the fix.

Changelog

  • Purpose: Describes one user-facing product change for release notes generation.
  • Required content: Includes title, type, and products.
  • Title: Uses present tense, starts with an action verb, focuses on user impact, and stays under 80 characters.
  • Description: Adds context only when needed, focuses on user value, and stays under 600 characters.
  • Impact and action: Required for breaking changes and recommended for deprecations and known issues.
  • Anti-patterns: Does not focus on implementation details, internal references, vague summaries, or duplicated title text.

When the inferred type differs from the declared type, report the mismatch first, then evaluate against the type that best matches the page's actual purpose.

Step 4: Generate report

## Content type check: <file>

### Detected type: <type>

### Required elements
- ❌ Frontmatter `description`: Missing
- ✅ Title: Present, uses correct pattern
- ...

### Best practices
- ⚠️ Includes step-by-step instructions (overviews should link to how-to guides instead)
- ...

### Summary
X of Y required elements present. Z best practice issues found.

Classify mode steps

Use these steps when the user describes a planned or hypothetical page rather than presenting actual content. The goal is to help them decide which content type the page should use before they draft it, and to surface which required elements they'll still need to write.

Step 1 (classify): Read the proposal

The user describes what they want to write. Examples:

  • "A new step-by-step page about migrating from ECE to ECH"
  • "An overview of cross-cluster search"
  • "A page documenting the symptoms and resolution for index_not_found_exception errors"
  • "A short page documenting the new --reindex-on-startup CLI flag, what it does, and a one-line example"

If the description is too vague to classify (fewer than ~10 informative words, no verbs of intent, no clear domain reference), ask one focused clarifying question rather than guessing.

Step 2 (classify): Match against the content-type definitions

Score the proposal against each content type using these definitions (the same ones Validate mode uses to grade pages — see Step 3 above for the full checklists):

  • Overview — describes what something is, how it works, and why it matters. Concept-led; long procedures and reference tables are anti-patterns.
  • How-to — accomplishes one self-contained task. Action-verb title, "Before you begin" or requirements, numbered steps, success checkpoint. Anti-pattern: chains many tasks or teaches broad concepts.
  • Tutorial — chains related tasks toward a meaningful learning outcome. Has objectives, prerequisites, instructional flow, checkpoints. Anti-pattern: narrow recipe or pure overview.
  • Troubleshooting — resolves one specific repeatable problem. Problem-focused title, Symptoms section, Resolution section. Anti-pattern: generic "Troubleshooting X" wayfinding pages or mixing unrelated problems.
  • Changelog — describes one user-facing product change for release notes. Action-verb title, user-impact focus, present tense. Anti-pattern: implementation-focused or vague summaries.

Pick the type whose definition the proposal description most clearly invokes. If the proposal mentions sequential steps, prefer how-to over overview. If it mentions an error or symptom, prefer troubleshooting over how-to. If it mentions a concept being introduced or explained, prefer overview.

Step 3 (classify): Check confidence and assembly

Assign a confidence level:

  • High — the proposal clearly invokes one type's required content (e.g., explicit steps + prerequisites = how-to; explicit Symptoms + Resolution = troubleshooting).
  • Medium — the proposal fits one type best but is missing some required elements, or fits two types partially. Suggest the user draft an outline and re-classify.
  • Low — no type fits cleanly. The proposal may belong as a section of an existing page rather than a new page. Recommend an assembly check (look for sibling pages on the same topic) before treating this as a new page.

Step 4 (classify): Generate classification report

## Content type classification

### Best fit: <type>
Confidence: <high | medium | low>

### Why
- <reason 1, e.g., "Proposal describes sequential steps and prerequisites">
- <reason 2, e.g., "Title pattern is action-verb">

### Required elements still to draft
- <element from the type's required-content list, e.g., "Before you begin section">
- <element>
- ...

### Alternatives considered
- <type 2>: <one-line reason it's a worse fit>
- <type 3>: <one-line reason>

### Recommendation
- <next step based on confidence — see Step 3 (classify)>

If no type fits cleanly (low confidence on all), say so explicitly and recommend the user check whether the content could be added as a new section of an existing page, rather than starting a new page. Do not invent a fit.

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 Docs Content Type Checker AI skill do?

Check a docs-content page against Elastic content type guidelines (overview, how-to, tutorial, troubleshooting, changelog), or classify a proposed page idea against the content types before drafting. Use when the user asks to check content type compliance, validate page structure, review a doc against content type standards, or decide which content type a planned page should use.

Why use Docs Content Type Checker on TypingMind?

Because you install it once and use it with any model. Docs Content Type Checker 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 Docs Content Type Checker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elastic/elastic-docs-skills/tree/main/skills/authoring/content-type-checker. 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 Docs Content Type Checker?

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 Docs Content Type Checker?

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

Is the Docs Content Type Checker AI skill free?

Yes. It is published on GitHub by elastic 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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