datahub-search

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.

Reviews DataHub connector implementations against 22 golden standards for compliance, code quality, silent failures, test coverage, type design, and merge readiness. Use when reviewing connector code, checking a PR, auditing a connector implementation, or verifying connector standards compliance.

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.

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.

Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

Search, scrape, crawl, extract structured data, and monitor web pages via the ScrapeGraph AI CLI. Use when the user asks to search the web, scrape a webpage, grab content from a URL, extract JSON from a site, crawl documentation or site sections, monitor a page for changes, inspect request history, check ScrapeGraph credits, or validate API setup.

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps,' 'cold call script,' or 'voicemail script.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.