review-connector

name	review-connector
description	Review connector spec changes (spec, docs). Use when reviewing a PR involving connector specs, doing post-creation review after create-connector or build-connector, or preparing a connector PR checklist.

Review Connector

Use this skill when reviewing or preparing changes to a connector spec (spec code, documentation). Apply the checklist below; use the optional thorough check when the user asks for deeper validation against the vendor API.

When to use

• Reviewing a PR that adds or changes a connector spec
• Post-creation code review (e.g. after create-connector or build-connector)
• Preparing a connector PR or self-review before opening a PR
• Thorough check: When the user explicitly asks for deeper validation (e.g. validate against vendor API docs); more expensive, use when quality bar is high.

Checklist

If the connector is MCP-native, apply the MCP-specific checks in reference/mcp-connectors.md in addition to the items below.

Connector Spec

• Look at existing specs for patterns: src/platform/packages/shared/kbn-connector-specs/src/specs/
• Valid structure with required fields, correct auth type
• ID alignment: metadata.id (e.g. .zendesk) and ConnectorIconsMap key match. IDs must start with a dot.
• metadata.description quality: The description must list the key actions the connector supports and the objects they operate on (e.g., "Search messages, list public channels, and send messages in Slack"). Flag descriptions that are vague ("Connect to X to pull data"), say nothing about capabilities ("Kibana Stack Connector for X"), or omit actions the connector actually provides. Keep to one sentence, ~15 words.
• Schema UI: Every config field in schema has .meta() with at least label (or uses a UISchemas.* helper). Otherwise fields render as unlabeled.
• Action param schema (Workflow editor): For custom connector actions, the Zod schema in the input handler should give each param a short, clear .describe() so the Workflow editor shows helpful descriptions when mapping inputs.
• Auth: Auth type matches the service. Auth format (e.g. header value) must match the vendor's official docs; document or link how to obtain tokens. For OAuth, use defaults/overrides so users only fill instance URL, client ID, client secret where possible.
• OAuth defaults vs placeholders: Every defaults value must be paired with { hidden: true } in overrides.meta so the field is invisible in the form. Defaults for visible fields will overwrite encrypted user values on "Edit". For fields where the user must enter their own value (e.g. tenant-specific URLs), use placeholder in overrides.meta instead of a default. For fields that should never be edited (e.g. fixed OAuth endpoints, scopes), use both a default and { hidden: true }. Flag any visible auth field that has a default without { hidden: true }.
• Spec is exported from all_specs.ts. Do not add unused/cargo-culted flags; only set flags the platform or this connector actually uses.
• Input schemas & types: Action input schemas and their z.infer<> types must live in a separate types.ts file alongside the spec (not inline in the spec file, and not as as casts in handlers). Handlers must be typed with the inferred type (e.g. handler: async (ctx, input: SearchInput) => {}), not input as { field: string }. See servicenow_search/types.ts for the canonical pattern.

LLM Descriptions and Skill Content

• isTool: Actions intended for AI agent use should set isTool: true (the default is false, which hides the action from Agent Builder). Most actions should be tools. Flag actions that are missing isTool: true unless there is a clear reason to hide them (e.g. destructive or admin-only operations).
• Every action has a description field that clearly explains its purpose, when to use it, and what it returns. Flag actions with missing, vague, or generic descriptions that would not help an LLM choose the right action.
• Action descriptions must be plain strings — they are for LLM consumption only and should NOT use i18n.translate(). In contrast, metadata.description IS shown in the UI and MUST use i18n.translate(). Flag any action description wrapped in i18n.translate(), and flag any metadata.description that is a plain string without i18n.
• Download/binary actions: Actions that return base64-encoded or binary data must include a WARNING in their description advising agents to only call them when they have a plan to process the data (e.g. via an Elasticsearch ingest pipeline attachment processor). Flag download actions that lack this guidance.
• Every Zod param has .describe() with useful guidance: examples, constraints, format hints (e.g. query syntax, allowed values, units). Params without .describe() leave LLMs guessing — flag them.
• The skill property (if present) covers multi-step patterns, common gotchas, and cross-action references that help an LLM use the connector correctly. Review for accuracy and completeness. The skill should NOT repeat information already in action description fields or param .describe() calls — it should add higher-level guidance that cannot be expressed per-action (e.g. "call X before Y", auth-mode differences, pagination patterns, typical workflows). Flag skill content that is redundant with individual action/param descriptions.
• Reference ServiceNow, Slack, and GitHub connector specs as quality benchmarks for description and skill content.
• Look at existing connector specs for patterns (e.g. slack/, github/, servicenow_search/)

Documentation and Icons

• Generator scaffold docs are filled in (no remaining TODO: placeholders)
• docs/reference/connectors-kibana/_snippets/elastic-connectors-list.md entry filled in
• docs/reference/toc.yml entry exists in the correct section
• Icon: Connector has an icon (ConnectorIconsMap entry and icon component or asset). No placeholder icons or generated icons. If a brand icon does not exist elsewhere in the repo, prompt the user to provide one.

Docs quality checks

If the PR includes documentation changes in docs/reference/connectors-kibana/, run the following skills on each connector doc file. These require skills from https://github.com/elastic/elastic-docs-skills — if any are unavailable, tell the user to install them (curl -sSL https://raw.githubusercontent.com/elastic/elastic-docs-skills/main/install.sh | bash).

1. docs-check-style — Elastic style guide compliance. Flag violations.
2. crosslink-validator — Validate cross-links resolve. Flag broken links.
3. frontmatter-audit — Check applies_to, description, navigation_title completeness.
4. content-type-checker — Verify page follows correct content type guidelines.
5. applies-to-tagging — Validate applies_to tags match connector availability.

Report documentation issues alongside code issues.

Naming and Conventions

• Directory and file names follow repo conventions (snake_case for dirs/files; camelCase for TS exports)
• Connector IDs don't collide with existing ones. If a connector already exists for the same product, use a distinct ID (e.g. .servicenow_search)
• If the PR changes behavior that could affect existing callers, document why and address backwards compatibility in the PR description
• TypeScript (touched files): Use strict equality (=== / !==), follow repo style (early returns, explicit types, no any)

Security

• SSRF: Any URL field in connector config or workflow action input (e.g. base_url, endpoint, webhook_url) must be validated. URLs should be allowlisted, restricted to HTTPS, or otherwise prevented from being user-controlled in a way that could trigger requests to internal/private hosts. Flag any case where a user-supplied URL flows directly into a network call without validation.
• Sensitive data in logs: Check that query parameters and user-supplied inputs are not logged. Queries come directly from users in chat and may contain sensitive context. Look for logger.debug, console.log, or any logging that captures query, input, prompt, or similar fields; flag these as high-risk.

Tool Design

• Discovery / metadata tools: The tool set should include at least one tool that helps an agent orient itself — e.g. who_am_i, get_current_user, list_projects, get_table_schema, list_spaces. Without these, an agent must guess IDs or structure before it can call other tools. Flag if the set has no discovery/metadata tooling.
• Tool consolidation: Look for tools that do the same operation on different entity types (e.g. get_issue_by_id, get_ticket_by_id, get_task_by_id). Where practical, these should be consolidated into one tool with a type enum parameter. Flag redundant tools and suggest a merged alternative.
• Tool completeness: Consider whether the full set of tools is sufficient for agents to answer realistic user questions against this connector. Would you, given only these tools, be able to find the answer to questions a user is likely to ask? Flag obvious gaps (e.g. search-only tooling with no way to drill into a result, or write operations with no way to read back state).
• API efficiency: Check whether tools are designed to minimize round-trips. Are tools making redundant API calls? Are there patterns that will force agents into trial-and-error loops (e.g. a tool that requires an ID with no tool to discover it)? Flag workflows that will reliably require multiple back-and-forth calls for a single user goal.

List all issues found. If no issues, note that the code looks good.

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Thorough check (optional)

Run when the user asks for thorough or deep validation. Same areas as the checklist, with deeper validation:

1. Vendor API: Find official API docs; map actions to endpoints; confirm auth format and version. Verify auth header/body format matches vendor docs exactly.
2. Input validation: Compare connector/workflow input schema to the official API — parameter names, required vs optional, types, constraints (enums, min/max, format). Report mismatches and suggest fixes.
3. Output shape: Compare expected response shape to the actual API response in the docs — top-level shape, fields, maps/arrays, pagination fields. Report expected vs actual for any mismatch.