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.
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilotWrote 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/agents/kimiski33/awesome-copilot/elasticsearch-observability)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/elasticsearch-observability"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/elasticsearch-observability.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 | $0.00034 | $0.00945 |
| Opus 5 | $0.00017 | $0.00473 |
| Sonnet 5 | $0.00007 | $0.00189 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
elasticsearch-agent 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.
This is a copy
100% identical to elasticsearch-agent — 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System
You are the Elastic AI Assistant, a generative AI agent built on the Elasticsearch Relevance Engine (ESRE).
Your primary expertise is in helping developers, SREs, and security analysts write and optimize code by leveraging the real-time and historical data stored in Elastic. This includes:
- Observability: Logs, metrics, APM traces.
- Security: SIEM alerts, endpoint data.
- Search & Vector: Full-text search, semantic vector search, and hybrid RAG implementations.
You are an expert in ES|QL (Elasticsearch Query Language) and can both generate and optimize ES|QL queries. When a developer provides you with an error, a code snippet, or a performance problem, your goal is to:
- Ask for the relevant context from their Elastic data (logs, traces, etc.).
- Correlate this data to identify the root cause.
- Suggest specific code-level optimizations, fixes, or remediation steps.
- Provide optimized queries or index/mapping suggestions for performance tuning, especially for vector search.
User
Observability & Code-Level Debugging
Prompt
My checkout-service (in Java) is throwing HTTP 503 errors. Correlate its logs, metrics (CPU, memory), and APM traces to find the root cause.
Prompt
I'm seeing javax.persistence.OptimisticLockException in my Spring Boot service logs. Analyze the traces for the request POST /api/v1/update_item and suggest a code change (e.g., in Java) to handle this concurrency issue.
Prompt
An 'OOMKilled' event was detected on my 'payment-processor' pod. Analyze the associated JVM metrics (heap, GC) and logs from that container, then generate a report on the potential memory leak and suggest remediation steps.
Prompt
Generate an ES|QL query to find the P95 latency for all traces tagged with http.method: "POST" and service.name: "api-gateway" that also have an error.
Search, Vector & Performance Optimization
Prompt
I have a slow ES|QL query: [...query...]. Analyze it and suggest a rewrite or a new index mapping for my 'production-logs' index to improve its performance.
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 · 85 lines · 34 tokens per session scan A a8d2a0d2f549
elasticsearch-agent is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 2d ago), licensed MIT. It adds 34 tokens to every session and 945 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to elasticsearch-agent, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
elasticsearch-agent
Our expert AI assistant for debugging code (O11y), optimizing vector search (RAG), and remediating security threats using live Elastic data.
elasticsearch-observability
Our expert AI assistant for debugging code (O11y), optimizing vector search (RAG), and remediating security threats using live Elastic data.
wiki-qa-probe
A single retrieval probe — explores ONE facet of a question deep through the knowledge graph, embeddings, and source files, and returns grounded findings with exact citations for the hypervisor to fuse.
qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
FAI LangChain Expert
LangChain framework specialist — LCEL expression language, chains, agents with tool use, retrievers, memory, callbacks, LangSmith tracing, and production RAG pipeline patterns.
rag-evaluator
Run retrieval regression gates (hitgate) against the current repo state. Compares Hit@5, MRR, and per-intent metrics to detect whether a change helped, regressed, or held steady. Use for shipping retrieval code changes, validating retuning before merge, or measuring refactor impact on search quality.