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Elasticsearch Logs

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automateyournetwork
elasticsearch-logs

Search and analyse logs in an existing Elasticsearch cluster (8.x/9.x) — syslog, application logs, Zeek/Suricata exports, or any indexed event data. Use for "what errors did we see", "how many times did X happen", "show me logs from device Y", "which host logged the most". Read-only. Counting questions MUST go through ESQL or track_total_hits — a bare search total silently caps at 10,000.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill nameelasticsearch-logs
Stars
680
Forks
189
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by automateyournetwork on GitHub. Read the source before you install it.

Installation

Install the Elasticsearch Logs AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/elasticsearch-logs
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/elasticsearch-logs .claude/skills/elasticsearch-logs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Elasticsearch Logs in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Elasticsearch Logs on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Elasticsearch Logs is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Elasticsearch Logs

Read-only log search over an Elasticsearch cluster you already run. NetClaw installs no cluster and indexes nothing — this queries what is already there.

Server: elasticsearch-mcp (adopted, docker.elastic.co/mcp/elasticsearch, Apache-2.0, digest-pinned) · 5 tools · 1,094 tokens

The rule that matters most

A bare search total is capped at 10,000 and reads as if it were exact.

Elasticsearch stops counting at 10,000 and marks the total relation: "gte" — meaning at least. This server discards that qualifier and prints Total results: 10000. There is nothing in the response to tell you the number is a floor.

Measured against 10,075 real documents:

How you askWhat you get
search with no guardTotal results: 10000 — wrong
search with "track_total_hits": trueTotal results: 10075 — correct
esql STATS COUNT(*)10075 — correct

On a million-document index a bare search still says 10,000. The error is unbounded and invisible.

Therefore, for any question of the form "how many", "how often", "which is most", or any number a human will act on:

  1. Use esql with STATS COUNT(*), or
  2. Use search with "track_total_hits": true in the query_body.

search without that guard is for retrieving example documents only — never for counting. If you report a total that came from an unguarded search, you are reporting a number that may be arbitrarily wrong.

Tools

ToolUse it forRequired arguments
list_indiceswhat indices exist, and their document countsindex_pattern (use *)
get_mappingsfield names and types before writing a queryindex
searchretrieving matching documents (Query DSL)index, query_body
esqlcounting, aggregating, grouping, rankingquery
get_shardsshard health when results look incomplete—

Note the argument names are snake_case (query_body, index_pattern). Wrong names fail loudly with a deserialization error — they are not silently ignored.

Worked patterns

How many errors, by device — a counting question, so ESQL:

esql: FROM netclaw-syslog | WHERE severity == "error" | STATS n = COUNT(*) BY device | SORT n DESC

Show me examples of those errors — a retrieval question, so search:

search: index=netclaw-syslog
        query_body={"query":{"term":{"severity":"error"}}, "size":20, "track_total_hits":true}

What am I even working with — always start here on an unfamiliar cluster:

list_indices: index_pattern=*
get_mappings: index=<the one you picked>

Guessing field names is the most common cause of a query that returns nothing. A term query against a text field, or an aggregation on one, will not behave as expected — check the mapping first. Fields are commonly foo (analysed text) plus foo.keyword (exact); use .keyword for grouping, sorting, and exact matches.

Empty is not the same as zero

A query returning no hits means this query found nothing. It does not establish that the event did not occur. Before reporting "no errors", confirm:

  • the index actually holds data for the time range (list_indices doc counts, or an ESQL count with no filter)
  • the field names came from get_mappings, not from a guess
  • the time range matches how the data is actually timestamped

Say "no matching events in <index> for <range>" — not "no errors occurred".

Boundaries — which backend answers

NetClaw has several log and metric backends. Pick by where the data lives, not by question shape:

BackendUse when
this skilllogs indexed in Elasticsearch / ELK
splunk-searchlogs in Splunk
datadog-logslogs in Datadog
gcp-cloud-loggingGoogle Cloud audit/platform logs
prometheus-monitoring / grafana-observabilitytime-series metrics, not logs
duckdb-analysisexported files on disk (CSV/Parquet/JSON), not a live store

If you do not know where the logs live, ask. Do not query every backend hoping one answers — an empty result from the wrong store is indistinguishable from an absence of events.

Read-only

All five tools read. There is no index, update, delete, or reindex verb in the manifest, so no write is reachable regardless of the credential. Prefer an Elasticsearch API key with read/view_index_metadata only — the server will happily use a superuser credential, and nothing in it needs one.

Configuration

VariableMeaning
ES_URLcluster URL as reached from inside the container — a cluster on this host is http://host.docker.internal:9200, not localhost
ES_API_KEYAPI key (preferred)
ES_USERNAME / ES_PASSWORDbasic auth alternative
ES_SSL_SKIP_VERIFYtrue to skip certificate verification (lab only)

Upstream status

This server is deprecated by Elastic and receives critical security updates only. It was adopted deliberately: its replacement, Agent Builder's MCP endpoint, is Enterprise-tier on self-managed, so the supported path is paywalled while this one is Apache-2.0, already published, and works against a free Basic cluster. The image is digest-pinned so a security-only update cannot change answers underneath you. See specs/096-elastic-logs/spec.md.

Frequently asked questions

What does the Elasticsearch Logs AI skill do?

Search and analyse logs in an existing Elasticsearch cluster (8.x/9.x) — syslog, application logs, Zeek/Suricata exports, or any indexed event data. Use for "what errors did we see", "how many times did X happen", "show me logs from device Y", "which host logged the most". Read-only. Counting questions MUST go through ESQL or track_total_hits — a bare search total silently caps at 10,000.

Why use Elasticsearch Logs on TypingMind?

Because you install it once and use it with any model. Elasticsearch Logs is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Elasticsearch Logs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/elasticsearch-logs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Elasticsearch Logs?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Elasticsearch Logs?

As many as you like. As long as a model supports skills, you can use Elasticsearch Logs with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Elasticsearch Logs AI skill free?

Yes. It is published on GitHub by automateyournetwork under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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