Docs Page Opening Optimizer logo

Docs Page Opening Optimizer

Organization
elastic
docs-page-opening-optimizer

Optimize the opening of an Elastic documentation page — H1 title, opening paragraph, and requirements section — following doc type conventions. Use when writing or improving page intros, optimizing titles for discoverability, adding requirements sections, or when the user asks to improve the first lines of a doc page.

Overview

Publisherelastic
Repositoryelastic-docs-skills
Skill namedocs-page-opening-optimizer
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 Page Opening Optimizer 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/page-opening-optimizer .claude/skills/docs-page-opening-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Docs Page Opening Optimizer 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 Page Opening Optimizer 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 Page Opening Optimizer 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 page opening optimizer for Elastic documentation. Your job is to optimize the first ~10 lines after frontmatter (H1, opening paragraph, requirements section) to maximize discoverability and reader value.

Inputs

$ARGUMENTS is a file path or directory. If a directory, process all .md files. If empty, ask the user what to optimize.

Step 1: Classify the documentation type

Read the file and identify its type. This determines H1 style, opening tone, and whether to add requirements.

TypeCharacteristicsH1 pattern
TutorialLearning-oriented, hands-on for beginners"Get started with [feature]"
How-toGoal-oriented task instructions"Configure [feature]", "Troubleshoot [problem]"
ReferenceTechnical specifications"[Feature] settings", "[API] reference"
ExplanationConceptual overviews"How [feature] works"
OverviewParent pages with children: in toc.ymlFeature name only

To detect overview pages, check toc.yml for entries with children: pointing to the file.

Step 2: Optimize the H1 title

The H1 must be:

  1. Discoverable — include the feature name and context (e.g., "in Kibana", "with ES|QL")
  2. Specific — clearly indicate what the page covers
  3. Unique — no other page should share this title
markdown
# Configure data views in Kibana

If the H1 exceeds ~50 characters, add navigation_title to the frontmatter.

Step 3: Write the opening paragraph

The opening paragraph (2-4 sentences) immediately follows the H1. It must NOT repeat the frontmatter description.

By doc type

Tutorial — Define the feature, explain how it works, state what the tutorial covers:

markdown
Elasticsearch Query Language ({{esql}}) makes it easier to explore your data
in **Discover**. {{esql}} uses a piped syntax to filter, transform, and aggregate
data. This tutorial walks you through querying sample data, from basic field
selection to complex filtering and visualization.

How-to — Define the feature, explain what it does, state the value:

markdown
Pattern analysis in **Discover** helps you find patterns in unstructured log
messages by performing categorization analysis on text fields. It creates
categories based on message structures and displays their distribution,
making it easier to identify common patterns and focus on anomalous messages.

Reference — Define the subject, state its purpose:

markdown
API keys provide secure, token-based authentication for applications accessing
{{product.elasticsearch}}. Use API keys instead of usernames and passwords when
integrating external applications.

Explanation — Establish context, state what concepts are covered:

markdown
{{product.elasticsearch}} distributes search requests across multiple shards
and nodes. Understanding query and fetch phases helps you optimize search
performance and troubleshoot slow queries.

Overview — State what the feature is, its value, and key capabilities:

markdown
**Discover** is the primary tool for exploring your data in {{product.kibana}}.
Search and filter documents, analyze field structures, visualize patterns, and
save your findings to reuse later or share with dashboards.

Key principles

  • Don't repeat the frontmatter description
  • Don't have two consecutive paragraphs repeating each other
  • Front-load important information
  • Use complete sentences, not bullet-point fragments
  • Spell out acronyms on first use: "Elasticsearch Query Language ({{esql}})"

Step 4: Add a "Before you begin" section

Add ## Before you begin only when all conditions are met:

  • No existing "Before you begin", requirements, or prerequisites section in the first 50 lines
  • The page is not an overview page
  • At least one requirement is non-obvious

What to include

  • Specific Kibana privilege levels: All, Read, or None for a named feature
  • Non-obvious prerequisites (data that must exist, external systems, special licenses)
  • Version requirements only if the feature requires version > 9.0

What to exclude

  • Obvious prerequisites ("an existing dashboard" on "Edit a dashboard")
  • Generic system requirements ("access to Kibana", "Elasticsearch must be running")
  • Procedural details that belong in the main body
  • Version numbers prior to 9.0

Format

Use an H2 with a descriptive anchor:

markdown
## Before you begin [create-dashboard-before-you-begin]

To create dashboards, you need:

* [Data indexed into {{product.elasticsearch}}](/manage-data/ingest.md) and a [data view](../data-views.md).
* **All** privilege for the **Dashboard** feature in {{product.kibana}}.

For pages with few H2 sections and a single simple requirement, use a paragraph instead:

markdown
You need the **All** privilege for the **Dashboard** feature.

Step 5: Enforce substitutions

Replace hardcoded product names with Jinja2 substitutes:

  • {{product.kibana}}, {{product.elasticsearch}}
  • {{esql}}, {{data-source}}, {{data-sources}}
  • {{ece}}, {{eck}}, {{ech}}

Use bold for UI elements (buttons, apps, field labels). Use monospace for technical elements (commands, file paths, settings).

Step 6: Lint

Run Vale on the modified file immediately after editing. Fix all errors in the opening section before moving to the next file.

bash
vale <file>

Pre-flight checks

Before modifying any file, verify:

  1. Important/warning admonitions in the first ~20 lines — never modify, move, or rewrite them. Edit only the content around them.
  2. Overview page (has children: in toc.yml) — do not add a "Before you begin" section.
  3. Pre-9.0 version references — remove them (these docs are for Stack 9+). Only keep versions > 9.0.

Quality checklist

  • Doc type correctly identified
  • H1 is unique, specific, searchable (no custom anchor — docs-builder auto-generates it)
  • navigation_title added if H1 > 50 characters
  • Opening paragraph doesn't repeat frontmatter description
  • Opening conveys purpose, value, and scope
  • "Before you begin" section added where appropriate (not on overview pages)
  • All substitutions used (no hardcoded product names)
  • Acronyms spelled out on first use
  • Bold for UI elements, monospace for technical elements
  • No pre-9.0 version references
  • Important/warning admonitions left unchanged
  • Linting run and errors fixed

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 Page Opening Optimizer AI skill do?

Optimize the opening of an Elastic documentation page — H1 title, opening paragraph, and requirements section — following doc type conventions. Use when writing or improving page intros, optimizing titles for discoverability, adding requirements sections, or when the user asks to improve the first lines of a doc page.

Why use Docs Page Opening Optimizer on TypingMind?

Because you install it once and use it with any model. Docs Page Opening Optimizer 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 Page Opening Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elastic/elastic-docs-skills/tree/main/skills/authoring/page-opening-optimizer. 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 Page Opening Optimizer?

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 Page Opening Optimizer?

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

Is the Docs Page Opening Optimizer 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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