Integration Skills is a collection of agent workflows for researching, creating, reviewing, and maintaining Elastic integration packages. Engineers use it with coding environments such as Cursor, Claude Code, and Codex to scaffold integrations, configure data streams, map fields, and run tests.
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/integration-skills --skill dashboard-reviewgit clone --depth 1 https://github.com/elastic/integration-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/integration-skills/dashboard-review)<a href="https://agentmods.dev/skills/elastic/integration-skills/dashboard-review"><img src="https://agentmods.dev/badge/skills/elastic/integration-skills/dashboard-review/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/integration-skills/dashboard-review"><img src="https://agentmods.dev/badge/skills/elastic/integration-skills/dashboard-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.01145 |
| Opus 5 | $0.00017 | $0.00573 |
| Sonnet 5 | $0.00007 | $0.00229 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
dashboard-review 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dashboard-review
When to use
Use this skill when tasks include:
- reviewing a PR that modifies Kibana dashboard JSON files
- comparing dashboard changes between branches
- summarizing what changed in dashboard exports
- checking dashboard changes against the official dashboard guidelines
When not to use
Do not use this skill as the primary guide for:
- creating new dashboards or exporting them from Kibana (
dashboard-guidelines) - dashboard naming conventions and file layout (
dashboard-guidelines→references/kibana-assets-layout.md) - package-wide command orchestration (
elastic-package-cli) - test suite selection (
integration-testing)
Prerequisites
kbdash must be installed:
go install github.com/efd6/kbdash@latest
Lines prefixed with [!] in the output are warnings about potential
issues in the dashboard JSON (consistency mismatches, incomplete
extraction, parse errors). Run kbdash -h for a description of each
warning type.
Review procedure
Read references/review-procedure.md for the full step-by-step
workflow. The high-level flow is:
- Identify changed dashboard files (added, removed, modified).
- Extract before/after descriptions with
kbdash. - Compare descriptions and classify changes as meaningful or cosmetic.
- Verify suspected issues against the raw JSON before reporting.
- Format output as one section per dashboard with bullet-pointed changes.
- Check guideline compliance on added or modified dashboards.
Guideline compliance checks
After summarizing changes, check the final state of every added or modified dashboard against the official dashboard guidelines. Report violations in a "Guideline notes" subsection after the change summary for each dashboard.
Check for these issues:
- TSVB panels: Flag any
visualizationpanel using TSVB. The guidelines require Lens for all new visualizations. Migrating existing TSVB to Lens is encouraged. - Missing dataset filter: Each visualization should filter on
data_stream.datasetor an equivalently specific scope. Flag panels that query broad index patterns (metrics-*,logs-*) without scoping. - By-reference visualizations: Visualization and lens panels
should be embedded by value. In the raw JSON,
referencesentries with apanelRefNameindicate by-reference panels. Flag these only when the reference type isvisualization,lens, ormap. Saved searches (searchtype) are inherently referenced and should not be flagged. - Deprecated input controls: The
input-control-vistype is deprecated. Dashboard-native controls should be used instead. - Package-name title prefix: Panel titles matching
[<Package Name> ...]create unnecessary repetition. Flag these. - Broad wildcard filters: Filters using unscoped
-*patterns without further qualification are a performance concern. - High panel count: If a dashboard has more than roughly 20 panels, note it. The guidelines recommend splitting across dashboards and linking with drilldowns.
- Queries on
event.datasetinstead ofdata_stream.dataset: Saved-object queries (dashboards, saved searches, packaged ML job datafeeds) must filter ondata_stream.dataset. Some inputs (e.g. packetbeat) never setevent.dataset, so anevent.datasetfilter silently matches nothing. Flag every saved-object query in a diff that renames or re-maps fields. - YAML dashboard sources out of sync: When a package keeps YAML
dashboard sources in
_dev/shared/kibana/*.yaml(compiled tokibana/dashboard/*.jsonwith kb-dashboard), BOTH must be committed and in sync. Flag JSON-only edits when a YAML source exists for that dashboard. - Regeneration diffs: A recompile PR is expected to change only
state.adHocDataViews,state.internalReferences, and per-layerindexkeys (the ES|QL/Discover fix on Kibana 9.3+). Diffs beyond those keys in a "regenerate" PR deserve inspection. - Missing Kibana asset tags: New content-pack dashboards are expected to carry Kibana asset tags — note their absence (LOW).
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.
- 9d ago First seen · 119 lines · 34 tokens per session scan A c5b240235156
dashboard-review is a skill published in the GitHub repository elastic/integration-skills (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 1,145 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 2026-08-31.
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omh-code-review
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revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
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full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.