datahub-connector-pr-review

Plans new DataHub connectors by classifying the source system, researching it using a dedicated agent or inline research, and generating a _PLANNING.md blueprint with entity mapping and architecture decisions. Use when building a new connector, researching a source system for DataHub, or designing connector architecture. Triggers on: "plan a connector", "new connector for X", "research X for DataHub", "design connector for X", "create planning doc", or any request to plan/research/design a DataHub ingestion source.

Use this skill when the user wants to add or update metadata in DataHub: descriptions, tags, glossary terms, ownership, deprecation, domains, data products, structured properties, documents, or field-level metadata. Triggers on: "add tag to X", "update description for X", "set owner of X", "add glossary term", "deprecate X", "create a domain", "create a glossary term", "add a document", or any request to modify DataHub metadata.

Use this skill to run DataHub's saved evals and report answers for judging. Triggers on: "run our evals", "run the eval suite", "run eval urn:li:eval:...", "how are our evals doing", "check for eval regressions", "upload this answer as an eval result", "score this answer with the DataHub judge", "compare two agents on the same eval". Answers each eval in a fresh agent with the DataHub tools attached, reports the answer through the DataHub Cloud CLI, and reads back the verdict DataHub's own judge produced.

Use this skill when the user wants to explore lineage, trace data dependencies, perform impact analysis, find root causes, map data pipelines, or understand how data flows between systems. Triggers on: "what feeds into X", "what depends on X", "show lineage for X", "impact analysis", "trace the pipeline", "root cause", "upstream of X", "downstream of X", or any request involving data lineage and dependency tracking.

Configure a DataHub instance to load and display a Micro Frontend (MFE) app. Use when the user wants to register an MFE with DataHub, add an MFE to the nav sidebar, set up MFE config for local dev or production/k8s, or troubleshoot MFE loading issues.

Scaffold a new DataHub Micro Frontend (MFE) app with all boilerplate files. Use when the user wants to create a new micro frontend, MFE, remote app, or Module Federation app for DataHub.

Use this skill when the user wants to manage data quality in DataHub: create or run assertions, check assertion outcomes, raise or resolve incidents, create notification subscriptions, or diagnose health problems across their estate. Triggers on: "create assertion", "run assertion", "check quality", "data quality", "health check", "raise incident", "resolve incident", "subscribe to", "failing assertions", "active incidents", or any request involving data quality, assertions, incidents, or quality notifications.

Use this skill when the user wants to search the DataHub catalog, discover entities, answer ad-hoc questions about their data, find datasets, or browse by platform or domain. Triggers on: "search DataHub", "find datasets", "who owns X", "what tables contain PII", "what columns does X have", or any request to search, discover, browse, or answer one-off questions about DataHub metadata. For lineage questions ("what feeds into X"), use `/datahub-lineage`. For systematic audits ("how complete is our metadata"), use `/datahub-audit`.

Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.

Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).

Set up Claude Code hooks to block dangerous git commands (push, reset --hard, clean, branch -D, etc.) before they execute. Use when user wants to prevent destructive git operations, add git safety hooks, or block git push/reset in Claude Code.

Generate AI music on RunComfy via the `runcomfy` CLI — a smart router across the music-model catalog. Routes to ElevenLabs AI Music Generation (premium 44.1 kHz stereo vocal tracks, 5 s–5 min, $0.0083/s) and ACE Step / ACE Step 1.5 (StepFun-AI open-weights, tag-driven composition, multilingual lyrics, $0.0002–0.0003/s, ~27× cheaper), plus ACE Step audio-inpaint (regenerate a time range inside an existing track) and ACE Step audio-outpaint (extend a track before or after). Picks the right model for the user's actual intent — premium vocal hook, cheap background music library, multilingual pop song, repair a bad chorus, lengthen a 30 s draft into a 2 min cut — and ships each model's documented prompting patterns plus the minimal `runcomfy run` invoke. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", "music with lyrics", "extend music", "fix this song", "inpaint music", or any explicit ask to generate or edit music.

创建 lark-cli 的自定义 Skill。当用户需要把飞书 API 操作封装成可复用的 Skill（包装原子 API 或编排多步流程）时使用。