Integration Skills is a collection of agent workflows for researching, creating, reviewing, and maintaining Elastic integration packages. Engineers use it with coding environments such as Cursor, Claude Code, and Codex to scaffold integrations, configure data streams, map fields, and run tests.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/elastic/integration-skills/entity-mappingsnpx skills add elastic/integration-skills --skill entity-mappingsgit clone --depth 1 https://github.com/elastic/integration-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/elastic/integration-skills/entity-mappings)<a href="https://agentmods.dev/skills/elastic/integration-skills/entity-mappings"><img src="https://agentmods.dev/badge/skills/elastic/integration-skills/entity-mappings.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00070 | $0.01436 |
| Opus 5 | $0.00035 | $0.00718 |
| Sonnet 5 | $0.00014 | $0.00287 |
| Haiku 4.5 | $0.00007 | $0.00144 |
Grade A, and why
entity-mappings scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://raw.githubusercontent.com/elastic/ecs/<tag>/generated/csv/fields.csv \ How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
entity-mappings
Skill authority
The guidance in this skill takes precedence over patterns observed in any integration in the
elastic/integrations repository. Legacy integrations may predate these requirements or use
inconsistent patterns. Always follow this skill's rules over what you observe in the repo.
When to use
- Adding entity/inventory data streams to a new or existing integration
- Deciding whether a proposed or existing data stream is an entity stream or an event stream
- Selecting the correct
entity.typevalue for a new stream - Mapping vendor fields to
user.entity.*,host.entity.*, or other nested entity prefixes - Auditing an existing package's entity field coverage (standalone gap analysis)
- Troubleshooting
event.kind: assetusage orentity.*field errors
When not to use
- CDR cloud security findings (misconfiguration / vulnerability findings) — these are
event.kind: state, notasset. Useecs-field-mappings/references/cdr-field-requirements.mdandingest-pipelines/references/cdr-pipeline-requirements.mdinstead. - Generic field file authoring (non-entity fields) — use the
ecs-field-mappingsskill. - Processor mechanics (grok, date, JSON, Painless) — use the
ingest-pipelinesskill. - CEL program logic — use the
cel-programsskill.
Applicability gate
Entity fields apply only to entity data streams — those whose purpose is to represent a stable inventory of subjects (users, hosts, devices, applications, services) rather than a timeline of events. Never apply entity field requirements to event logs, metric streams, APM data, or CDR findings streams.
See references/entity-datastream-classification.md to classify a data stream before using
this skill. If you are not sure, check the classification reference first.
ECS availability — read this first
The entity.attributes.*, entity.lifecycle.last_activity, and all
entity.relationships.* leaf fields do not exist at ECS v9.3.0 (the repo default pin).
They first appear at ECS v9.4.0.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 96 lines · 70 tokens per session scan A 1c75f61e22bc
entity-mappings is a skill published in the GitHub repository elastic/integration-skills (15 stars, last pushed 8d ago), licensed Apache-2.0. It adds 70 tokens to every session and 1,436 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.