elasticsearch
Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Best use case
elasticsearch is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "elasticsearch" skill to help with this workflow task. Context: Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/elasticsearch/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How elasticsearch Compares
| Feature / Agent | elasticsearch | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Guides Elasticsearch usage including index mapping design, query DSL (match, term, bool, aggregations), bulk indexing, cluster management, and performance tuning. Use when the user needs to implement full-text search, design index mappings, write complex search queries, or manage Elasticsearch clusters.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## When to use this skill
Use this skill whenever the user wants to:
- Design index mappings with analyzers and field types
- Write search queries (match, term, bool, multi_match, nested, aggregations)
- Index, update, or bulk-load documents via the REST API
- Manage clusters (shards, replicas, snapshots, upgrades)
- Integrate Elasticsearch with Kibana or Logstash (ELK stack)
## How to use this skill
### Workflow
1. **Design the mapping** - Define field types, analyzers, and index settings
2. **Index documents** - Use PUT/POST or bulk API
3. **Write queries** - Use Query DSL with filters for caching
4. **Monitor and tune** - Check cluster health, slow logs, and shard balance
### Quick-Start Example: Create Index and Search
```json
// Create index with mapping
PUT /products
{
"mappings": {
"properties": {
"name": { "type": "text", "analyzer": "standard" },
"description": { "type": "text" },
"price": { "type": "float" },
"category": { "type": "keyword" },
"created_at": { "type": "date" }
}
}
}
// Index a document
POST /products/_doc
{
"name": "Wireless Mouse",
"description": "Ergonomic wireless mouse with USB-C receiver",
"price": 29.99,
"category": "electronics",
"created_at": "2025-01-15"
}
// Search with bool query and aggregation
GET /products/_search
{
"query": {
"bool": {
"must": [{ "match": { "description": "wireless" } }],
"filter": [{ "range": { "price": { "lte": 50 } } }]
}
},
"aggs": {
"by_category": { "terms": { "field": "category" } }
}
}
```
## Best Practices
1. **Define explicit mappings** - Avoid dynamic mapping in production; set `dynamic: strict` to catch errors
2. **Use filters for exact matches** - Filters are cached and faster than queries for keyword/range conditions
3. **Paginate with search_after** - Avoid deep `from`/`size` pagination; use `search_after` for large result sets
4. **Plan shards carefully** - Target 20-40 GB per shard; avoid too many small shards
5. **Snapshot regularly** - Use snapshot/restore for backups; test restore procedures
## Keywords
elasticsearch, search, index, mapping, query DSL, aggregation, 搜索引擎, 全文检索, 聚合, ELK, Kibana, bulk API, clusterRelated Skills
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