Elasticsearch

OrganizationPopular

RightNow-AI

elasticsearch

Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations

Overview

Publisher	RightNow-AI
Repository	`openfang`
Skill name	`elasticsearch`
Stars	18.2K
Forks	2.3K
Bundled files	Instructions only
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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by RightNow-AI on GitHub. Read the source before you install it.

Installation

Install the Elasticsearch 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.
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/RightNow-AI/openfang/tree/main/crates/openfang-skills/bundled/elasticsearch

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/RightNow-AI/openfang.git /tmp/openfang
mkdir -p .claude/skills
cp -r /tmp/openfang/crates/openfang-skills/bundled/elasticsearch .claude/skills/elasticsearch

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

Use it in TypingMind

Enable Elasticsearch 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 Elasticsearch 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 Elasticsearch 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.

Elasticsearch Expert

A search and analytics specialist with deep expertise in Elasticsearch cluster architecture, query DSL, mapping design, and performance optimization. This skill provides production-grade guidance for building search experiences, log analytics pipelines, and time-series data platforms using the Elastic stack.

Key Principles

Design mappings explicitly before indexing data; relying on dynamic mapping leads to field type conflicts and bloated indices
Understand the difference between keyword fields (exact match, aggregations, sorting) and text fields (full-text search with analyzers)
Use index aliases for zero-downtime reindexing, canary deployments, and time-based index rotation
Size shards between 10-50 GB for optimal performance; too many small shards waste overhead, too few large shards limit parallelism
Monitor cluster health (green/yellow/red) continuously and investigate yellow status immediately, as it indicates unassigned replica shards

Techniques

Construct bool queries with must (scored AND), filter (unscored AND), should (OR with minimum_should_match), and must_not (exclusion) clauses
Use match queries for full-text search with analyzer-aware tokenization, and term queries for exact keyword lookups without analysis
Build aggregations: terms for top-N cardinality, date_histogram for time bucketing, nested for sub-document analysis, and pipeline aggs like cumulative_sum
Apply Index Lifecycle Management (ILM) policies with hot/warm/cold/delete phases to automate rollover and data retention
Reindex with POST _reindex using source/dest, applying scripts for field transformations during migration
Check cluster allocation with GET _cluster/allocation/explain to diagnose why shards remain unassigned
Tune search performance with the search profiler API, request caching, and pre-warming for frequently used queries

Common Patterns

Search-as-you-type: Use the search_as_you_type field type or edge_ngram tokenizer with a match_phrase_prefix query for autocomplete experiences
Parent-Child Relationships: Use join field types for one-to-many relationships where child documents update independently, avoiding costly nested reindexing
Cross-cluster Search: Configure remote clusters and use cluster:index syntax to query across multiple Elasticsearch deployments transparently
Snapshot and Restore: Register a snapshot repository (S3, GCS, or filesystem) and schedule regular snapshots for disaster recovery with SLM policies

Pitfalls to Avoid

Do not use wildcard queries on text fields with leading wildcards, as they bypass the inverted index and cause full field scans
Do not index large documents (over 100 MB) without splitting them; they cause memory pressure during indexing and merging
Do not set number_of_replicas to 0 in production; replicas provide both search throughput and data redundancy
Do not update mappings on existing indices for incompatible type changes; create a new index with the correct mapping and reindex the data

Frequently asked questions

What does the Elasticsearch AI skill do?

Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations

Why use Elasticsearch on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RightNow-AI/openfang/tree/main/crates/openfang-skills/bundled/elasticsearch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Elasticsearch?

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 Elasticsearch?

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

Is the Elasticsearch AI skill free?

Yes. It is published on GitHub by RightNow-AI 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

Elasticsearch Expert

Key Principles

Techniques

Common Patterns

Pitfalls to Avoid

Frequently asked questions

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