Elastic Agent Skills is a library of instruction packages that teach AI coding agents how to work with Elastic products, including Elasticsearch, Kibana, Elastic Observability, and Elastic Security. Developers use the skills for tasks such as API work, Kibana content management, observability, and security workflows. The catalogue entries are skills and plugins from this library.
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 skills add elastic/agent-skills --skill elasticsearch-anomaly-detectiongit clone --depth 1 https://github.com/elastic/agent-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/agent-skills/elasticsearch-anomaly-detection)<a href="https://agentmods.dev/skills/elastic/agent-skills/elasticsearch-anomaly-detection"><img src="https://agentmods.dev/badge/skills/elastic/agent-skills/elasticsearch-anomaly-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/elastic/agent-skills/elasticsearch-anomaly-detection"><img src="https://agentmods.dev/badge/skills/elastic/agent-skills/elasticsearch-anomaly-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 22 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Prompt Injection · line 32 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
- medium Prompt Injection · line 151 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 5.1 | $0.00046 | $0.02210 |
| Opus 5 | $0.00023 | $0.01105 |
| Sonnet 5 | $0.00009 | $0.00442 |
| Haiku 4.5 | $0.00005 | $0.00221 |
Grade A, and why
elasticsearch-anomaly-detection scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- elasticsearch-anomaly-detection — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elasticsearch Anomaly Detection
Create, open, and start ML anomaly detection jobs on time-series data. Choose the right count-family detector direction, configure bucket span and time field, wire the datafeed to the correct index, and confirm running state from stats — not from assumptions.
Environment Configuration
This skill executes Elasticsearch operations through the elastic CLI. If the
elastic CLI is not installed, tell the user what it is needed for. Do
not guess credentials, call the HTTP API directly, or attempt other workarounds.
This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping,
GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document
maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API
directly.
Prerequisite: ML anomaly detection requires a Platinum-equivalent license on self-managed clusters. Serverless projects include ML. The caller needs
manage_mlto create and manage jobs.Related skill: For interpreting anomaly scores, influencers, and model behavior after a job is running, use
elasticsearch-anomaly-detection-explainer— not this skill.
Process
-
Discover the target index and time field. List candidate indices with
GET /_cat/indices(pass a pattern when the user names one). Fetch field types for the chosen index withGET /{index}/_mapping. The decision: confirm the index exists, identify the time field (often@timestamp), and verify document volume is sufficient for baseline learning. Never guess index or field names — they vary across deployments. -
Choose detector function and direction. Match the user's intent to a count-family detector in
analysis_config.detectors:- Spike, surge, unusual increase in event volume →
high_count(orcount, which flags both directions but is acceptable when the user cares about spikes). Do not uselow_count— it will miss spikes. - Drop, outage, absence of events, traffic stops →
low_count. Do not usehigh_count— it will miss drops and silence. - Metric deviation (CPU, latency, a numeric field) → mean-family functions (
mean,high_mean,low_mean) withfield_nameset — only when the user asks about a numeric metric, not raw event volume.
- Spike, surge, unusual increase in event volume →
What ships with it
1 file 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.
- 5d ago First seen · 164 lines · 46 tokens per session scan A 80f2837f0660
elasticsearch-anomaly-detection is a skill published in the GitHub repository elastic/agent-skills (575 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 2,210 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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