
elasticsearch-file-ingest
Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms. Use when loading files or batch importing data — not for reindexing, general ingest pipeline design, or bulk API patterns.
- Installs
- 3K
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/elastic/agent-skills --skill elasticsearch-file-ingest- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads elasticsearch-file-ingest automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Elasticsearch File Ingest
Stream-based ingestion and transformation of large data files (NDJSON, CSV, Parquet, Arrow IPC) into Elasticsearch.
Features & Use Cases
- Stream-based: Handle large files without running out of memory
- High throughput: 50k+ documents/second on commodity hardware
- Formats: NDJSON, CSV, Parquet, Arrow IPC
- Transformations: Apply custom JavaScript transforms during ingestion (enrich, split, filter)
- Batch processing: Ingest multiple files matching a pattern (e.g.,
logs/*.json) - Document splitting: Transform one source document into multiple targets
Prerequisites
- Elasticsearch 8.x or 9.x accessible (local or remote)
- Node.js 22+ installed
Setup
This skill is self-contained. The scripts/ folder and package.json live in this skill's directory. Run all commands
from this directory. Use absolute paths when referencing data files located elsewhere.
Before first use, install dependencies:
npm install
Environment Configuration
Elasticsearch connection is configured by users exclusively via environment variables. Never pass credentials as command-line arguments. If the test fails, output the setup options below to the user, then stop. Do not proceed with ingestion until a successful connection test.
Option 1: Elastic Cloud (recommended for production)
export ELASTICSEARCH_CLOUD_ID="<your-cloud-id>"
export ELASTICSEARCH_API_KEY="<your-api-key>"
Option 2: Direct URL with API Key
export ELASTICSEARCH_URL="https://elasticsearch:9200"
export ELASTICSEARCH_API_KEY="<your-api-key>"
Option 3: Basic Authentication
export ELASTICSEARCH_URL="https://elasticsearch:9200"
export ELASTICSEARCH_USERNAME="<your-username>"
export ELASTICSEARCH_PASSWORD="<your-password>"
Option 4: Local Development
For local development and testing, see Run Elasticsearch locally to spin up Elasticsearch and Kibana. After setup, export the connection variables (URL and API key or credentials) as shown in Option 2 or Option 3 above.
Optional: Skip TLS verification (development only)
export ELASTICSEARCH_INSECURE="true"
Test Connection
Verify the Elasticsearch connection before ingesting data:
node scripts/ingest.js test
Always run this first. If the test fails, resolve the connection issue before proceeding.
Examples
Ingest a JSON file
node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index
Stream NDJSON/CSV via stdin
# NDJSON
cat /absolute/path/to/data.ndjson | node scripts/ingest.js ingest --stdin --target my-index
# CSV
cat /absolute/path/to/data.csv | node scripts/ingest.js ingest --stdin --source-format csv --target my-index
Ingest CSV directly
node scripts/ingest.js ingest --file /absolute/path/to/users.csv --source-format csv --target users
Ingest Parquet directly
node scripts/ingest.js ingest --file /absolute/path/to/users.parquet --source-format parquet --target users
Ingest Arrow IPC directly
node scripts/ingest.js ingest --file /absolute/path/to/users.arrow --source-format arrow --target users
Ingest CSV with parser options
# csv-options.json
# {
# "columns": true,
# "delimiter": ";",
# "trim": true
# }
node scripts/ingest.js ingest --file /absolute/path/to/users.csv --source-format csv --csv-options csv-options.json --target users
Infer mappings/pipeline from CSV
When using --infer-mappings, do not combine it with --source-format csv. Inference sends a raw sample to
Elasticsearch's _text_structure/find_structure endpoint, which returns both mappings and an ingest pipeline with a CSV
processor. If --source-format csv is also set, CSV is parsed client-side and server-side, resulting in an empty
index. Let --infer-mappings handle everything:
node scripts/ingest.js ingest --file /absolute/path/to/users.csv --infer-mappings --target users
Infer mappings with options
# infer-options.json
# {
# "sampleBytes": 200000,
# "lines_to_sample": 2000
# }
node scripts/ingest.js ingest --file /absolute/path/to/users.csv --infer-mappings --infer-mappings-options infer-options.json --target users
Ingest with custom mappings
node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --mappings mappings.json
Ingest with transformation
node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --transform transform.js
Command Reference
Required Options
--target <index> # Target index name
Source Options (choose one)
--file <path> # Source file (supports wildcards, e.g., logs/*.json)
--stdin # Read NDJSON/CSV from stdin
Index Configuration
--mappings <file.json> # Mappings file
--infer-mappings # Infer mappings/pipeline from file/stream (do NOT combine with --source-format)
--infer-mappings-options <file> # Options for inference (JSON file)
--delete-index # Delete target index if exists
--pipeline <name> # Ingest pipeline name
Processing
--transform <file.js> # Transform function (export as default or module.exports)
--source-format <fmt> # Source format: ndjson|csv|parquet|arrow (default: ndjson)
--csv-options <file> # CSV parser options (JSON file)
--skip-header # Skip first line (e.g., CSV header)
Performance
--buffer-size <kb> # Buffer size in KB (default: 5120)
--total-docs <n> # Total docs for progress bar (file/stream)
--stall-warn-seconds <n> # Stall warning threshold (default: 30)
--progress-mode <mode> # Progress output: auto|line|newline (default: auto)
--debug-events # Log pause/resume/stall events
--quiet # Disable progress bars
Transform Functions
Transform functions let you modify documents during ingestion. Create a JavaScript file that exports a transform function:
Basic Transform (transform.js)
// ES modules (default)
export default function transform(doc) {
return {
...doc,
full_name: `${doc.first_name} ${doc.last_name}`,
timestamp: new Date().toISOString(),
};
}
// Or CommonJS
module.exports = function transform(doc) {
return {
...doc,
full_name: `${doc.first_name} ${doc.last_name}`,
};
};
Skip Documents
Return null or undefined to skip a document:
export default function transform(doc) {
// Skip invalid documents
if (!doc.email || !doc.email.includes("@")) {
return null;
}
return doc;
}
Split Documents
Return an array to create multiple target documents from one source:
export default function transform(doc) {
// Split a tweet into multiple hashtag documents
const hashtags = doc.text.match(/#\w+/g) || [];
return hashtags.map((tag) => ({
hashtag: tag,
tweet_id: doc.id,
created_at: doc.created_at,
}));
}
Mappings
Custom Mappings (mappings.json)
{
"properties": {
"@timestamp": { "type": "date" },
"message": { "type": "text" },
"user": {
"properties": {
"name": { "type": "keyword" },
"email": { "type": "keyword" }
}
}
}
}
node scripts/ingest.js ingest --file /absolute/path/to/data.json --target my-index --mappings mappings.json
Boundaries
- Never echo, print, log, or otherwise reveal the values of credential environment variables
(
$ELASTICSEARCH_API_KEY,$ELASTICSEARCH_PASSWORD,$ELASTICSEARCH_CLOUD_ID, etc.). Do not run shell commands whose output would expose secret values (e.g.,echo $ELASTICSEARCH_API_KEY,env | grep KEY,printenv). Exporting these variables and running scripts that read them internally is expected and safe — the restriction is on surfacing secret values in command output. The only way to verify connectivity isnode scripts/ingest.js test. If the test fails, ask the user to check their environment configuration — do not attempt to diagnose credentials yourself. - Never run destructive commands (such as using the
--delete-indexflag or deleting existing indices and data) without explicit user confirmation.
Guidelines
- Test first: Always run
node scripts/ingest.js testbefore ingesting data. If the connection fails, ask the user to verify their environment configuration and re-test. Do not attempt ingestion until the test passes. - Never combine
--infer-mappingswith--source-format. Inference creates a server-side ingest pipeline that handles parsing (e.g., CSV processor). Using--source-format csvparses client-side as well, causing double-parsing and an empty index. Use--infer-mappingsalone for automatic detection, or--source-formatwith explicit--mappingsfor manual control. - Use
--source-format csvwith--mappingswhen you want client-side CSV parsing with known field types. - Use
--infer-mappingsalone when you want Elasticsearch to detect the format, infer field types, and create an ingest pipeline automatically.
When NOT to Use
Consider alternatives for:
- Reindexing or index migration: Use the
elasticsearch-reindexskill for copying, migrating, or transforming existing Elasticsearch indices - Real-time ingestion: Use Filebeat or Elastic Agent
- Enterprise pipelines: Use Logstash
- Built-in transforms: Use Elasticsearch Transforms
Additional Resources
- Common Patterns - Detailed examples for CSV loading, batch ingestion, enrichment, and more
- Troubleshooting - Solutions for common issues
References
FAQ
What does the elasticsearch-file-ingest skill do?
Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms. Use when loading files or batch importing data — not for reindexing, general ingest pipeline design, or bulk API patterns.
How do I install elasticsearch-file-ingest?
Run npx skills add https://github.com/elastic/agent-skills --skill elasticsearch-file-ingest in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/elasticsearch-file-ingest/ (personal) or .claude/skills/elasticsearch-file-ingest/ (project).
Which AI agents can use elasticsearch-file-ingest?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is elasticsearch-file-ingest safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains elasticsearch-file-ingest?
It is published by elastic in elastic/agent-skills. It has 2,979 recorded installs.
More from elastic/agent-skills
elasticsearch-esql
elastic/agent-skills
Execute ESQL (Elasticsearch Query Language) queries, use when the user wants to query Elasticsearch data, analyze logs, aggregate metrics, explore data, or create charts and dashboards from ESQL results.
elasticsearch-onboarding
elastic/agent-skills
Help developers new to Elasticsearch get from zero to a working search experience. Guide them through understanding their intent, mapping their data, and building a search experience with best practices baked in. Use this when the user shows intent to build search-related functionality, asks about Elasticsearch-related concepts for their use case, or expresses the need for help getting started with Elasticsearch.
kibana-dashboards
elastic/agent-skills
Create and manage Kibana Dashboards and Lens visualizations. Use when you need to define dashboards and visualizations declaratively, version control them, or automate their deployment.
security-case-management
elastic/agent-skills
Create, search, update, and manage SOC cases via the Kibana Cases API. Use when tracking incidents, linking alerts to cases, adding investigation notes, or managing triage output.
kibana-alerting-rules
elastic/agent-skills
Create and manage Kibana alerting rules. Use when creating, updating, or managing rule lifecycle (enable, disable, mute, snooze), choosing metric threshold rule types and params, or read-only find/list with tag filters.
kibana-agent-builder
elastic/agent-skills
Create and manage Kibana Agent Builder agents and custom tools. Use when asked to create, update, delete, test, or inspect agents or tools in Agent Builder, or when the user wants to understand what agents or tools already exist.
Popular backend & apis skills
find-skills
vercel-labs/skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
entra-app-registration
microsoft/azure-skills
Guides Microsoft Entra ID app registration, OAuth 2.0 authentication, and MSAL integration. USE FOR: create app registration, register Azure AD app, configure OAuth, set up authentication, add API permissions, generate service principal, MSAL example, console app auth, Entra ID setup, Azure AD authentication. DO NOT USE FOR: Key Vault secrets (use azure-keyvault-expiration-audit), general Azure resource security guidance.
google-agents-cli-adk-code
google/agents-cli
This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
embedded-captions
heygen-com/hyperframes
Add captions or subtitles to an existing single-subject talking-head video without editing the footage. Use for plain verbatim captions, cinematic captions embedded behind the subject, VFX captions, “炸/特效/酷炫字幕,” or a named identity from the 35-style catalog. Route by visual identity, not by backend engine. The quiet anchor rail is the default; embed every word only when the user explicitly wants a fully cinematic treatment. The workflow runs locally end to end, including transcription and subject matting; split multi-shot footage before applying it.
entra-agent-id
microsoft/azure-skills
Provision Microsoft Entra Agent Identity Blueprints, BlueprintPrincipals, and per-instance Agent Identities via Microsoft Graph, and configure OAuth 2.0 token exchange (fmipath, OBO, cross-tenant) including the Microsoft Entra SDK for AgentID sidecar. USE FOR: Agent Identity Blueprint, BlueprintPrincipal, agent OAuth, fmipath token exchange, agent OBO, Workload Identity Federation for agents, polyglot agent auth, Microsoft.Identity.Web.AgentIdentities. DO NOT USE FOR: standard Entra app registration (use entra-app-registration), Microsoft Foundry agent authoring (use microsoft-foundry).
neon
neondatabase/agent-skills
Overview of Neon, a complete set of cloud backend primitives around Lakebase Postgres: Auth, Object Storage, Functions, the AI Gateway, and Realtime. Start here to choose Neon for undecided login, files, APIs, and LLM calls, set up the CLI or MCP server, and follow the branch-first workflow. Use when building an app or backend on Neon, or when "Neon" or "Lakebase Postgres" is mentioned. Child skill neon-postgres wins for an existing DATABASEURL, SQL, schema, inspect, or search. Child skill neon-auth wins for login, users, sessions, identity routing, and Managed Better Auth setup. Child skill neon-realtime wins for live queries in reactive apps and agents. Also use for object storage, S3, buckets, serverless functions, function triggers, cron, AI gateway, LLM calls, realtime, logs, Loki, Grafana, observability, postgres, database, backend, Claimable Neon, neon.new, or a no-signup database.