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-detection-explainergit 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-explainer)<a href="https://agentmods.dev/skills/elastic/agent-skills/elasticsearch-anomaly-detection-explainer"><img src="https://agentmods.dev/badge/skills/elastic/agent-skills/elasticsearch-anomaly-detection-explainer/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-explainer"><img src="https://agentmods.dev/badge/skills/elastic/agent-skills/elasticsearch-anomaly-detection-explainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 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 23 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 33 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 101 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
- medium Prompt Injection · line 132 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
- medium Prompt Injection · line 175 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 | $0.00055 | $0.03925 |
| Opus 5 | $0.00028 | $0.01962 |
| Sonnet 5 | $0.00011 | $0.00785 |
| Haiku 4.5 | $0.00006 | $0.00392 |
Grade A, and why
elasticsearch-anomaly-detection-explainer 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 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.
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-explainer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anomaly Detection Score Explainer
Explain anomaly scores, model behavior, and why results look the way they do. Use the ML REST API for job config and
the standard _search API against .ml-anomalies-* for results — no ES|QL, fully compatible with Elastic
Serverless. For job lifecycle (create, start, stop), use the elasticsearch-anomaly-detection skill.
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
monitor_mlto read job config and anomaly results.Serverless note: The
_ml/.../results/*REST endpoints return HTTP 410 in Elastic Serverless. Always usePOST /.ml-anomalies-*/_searchfor result queries instead — fully supported everywhere this skill runs.
Process
-
Decide whether to fetch data or interpret what the user supplied. If the user embeds an anomaly record (or job config) in the prompt, interpret it directly using the domain knowledge below — do not call APIs to re-fetch fields already present. If the job ID, time range, or record is missing, retrieve it from the cluster.
The decision: proceed with judgment-only explanation when the record contains
record_score,initial_record_score,actual,typical, andfunction; otherwise fetch the missing pieces before explaining.
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.
- 4d ago First seen · 245 lines · 55 tokens per session scan A b970d67ea132
elasticsearch-anomaly-detection-explainer is a skill published in the GitHub repository elastic/agent-skills (571 stars, last pushed 5d ago), licensed Apache-2.0. It adds 55 tokens to every session and 3,925 once invoked, about $0.0003 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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