elastic/cursor-plugins is a collection of Cursor plugins that give AI assistants access to Elastic documentation and guidance for Elastic Cloud, Elasticsearch, Kibana, Observability, and Security. Developers use it when working with Elastic products and related technologies. The catalogue lists these plugins' skills and documentation MCP tools.
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/cursor-plugins/kibana-anomaly-detectionnpx skills add elastic/cursor-plugins --skill kibana-anomaly-detectiongit clone --depth 1 https://github.com/elastic/cursor-pluginsWrote 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/cursor-plugins/kibana-anomaly-detection)<a href="https://agentmods.dev/skills/elastic/cursor-plugins/kibana-anomaly-detection"><img src="https://agentmods.dev/badge/skills/elastic/cursor-plugins/kibana-anomaly-detection.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 5.1 | $0.00094 | $0.05275 |
| Opus 5 | $0.00047 | $0.02638 |
| Sonnet 5 | $0.00019 | $0.01055 |
| Haiku 4.5 | $0.00009 | $0.00528 |
Grade A, and why
kibana-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 yesterday.
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.
This is a copy
100% identical to kibana-anomaly-detection — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elastic ML Anomaly Detection
Expert process for ML anomaly detection: attribute incidents to entities, explain scores and model behavior, diagnose
job lifecycle failures, and manage jobs. Read anomaly results from POST /.ml-anomalies-*/_search (Serverless-safe)
and job/datafeed state from ML REST APIs. When the user embeds fixture evidence (influencer rows, job stats) in the
prompt, apply the judgment below directly — do not re-fetch fields already supplied.
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.
Mode selector
| User intent | Mode |
|---|---|
| "What broke?" / RCA / cross-job / blast radius / influencers / log categories | Investigate |
| "Why score high/low?" / renormalization / model bounds / forecasts | Explain |
| Missing docs / memory limit / datafeed stopped / lifecycle / calendars | Troubleshoot |
| Create a job / configure a datafeed / start analysis / retrieve results | Manage |
| Security framing (attack chains, MITRE, exfil) | Investigate + references/security-anomaly-expert.md |
| Observability/SRE framing (degradation, capacity, deployment regression) | Investigate + references/observability-anomaly-expert.md |
What ships with it
11 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.
- references/agent-builder-companion.md 882 B
- references/anomaly-detection-functions.md 12 KB
- references/investigation-queries.md 3.4 KB
- references/job-creation-recipes.md 7.7 KB
- references/observability-anomaly-expert.md 4.6 KB
- references/protocols/investigation.md 5.7 KB
- references/README.md 2.0 KB
- references/score-reference.md 7.0 KB
- references/security-anomaly-expert.md 5.0 KB
- references/troubleshooting-reference.md 4.1 KB
- references/worked-example.md 7.6 KB
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.
- yesterday Changed · -75 lines · -45 tokens per session 899d09513e8e
- 6d ago First seen · 417 lines · 139 tokens per session scan A ede7af664c4f
kibana-anomaly-detection is a skill published in the GitHub repository elastic/cursor-plugins (31 stars, last pushed yesterday), licensed Apache-2.0. It adds 94 tokens to every session and 5,275 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to kibana-anomaly-detection, differing in 0 lines, and is treated as a copy.
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