migration-status

Create the medallion architecture on Oracle AI Data Platform - registering an EXTERNAL catalog of source type SNOWFLAKE by default, and generating Spark SQL for schemas, tables and views from an approved Snowflake plan only when the user has explicitly asked for a Standard catalog. This stage copies no data - rows are copied only by the snowmig_02_copy_schema job, when the operator runs it. Use when the user asks to create the medallion structure, soft clone, shallow clone, create the target catalog, or deploy the target schema.

Dev mode. Run the entire Snowflake-to-AIDP migration pipeline against a built-in emulated estate and an emulated AIDP — no credentials, no network, nothing real is touched. Use when the user wants to understand what the migrator does before pointing it at a real account, wants a training walkthrough, or asks "show me how this works" / "demo mode" / "dev mode". Produces every real artifact plus DEMO.md, a narrated walkthrough of the lessons.

First-run setup for the Snowflake to AIDP migrator. Finds or creates the one migration config file (snowmig-config.yaml), reads every field back with secrets masked, verifies Snowflake authentication by password, key-pair, programmatic access token or SSO, verifies the AIDP end, then smoke-tests the connection and reports the account, region, role and warehouse. Use the first time the migrator runs on a machine, or when any other stage fails with an authentication, connection or "no config found" error.

Build a high-level Snowflake to AIDP migration plan and present it for approval. States which objects can migrate and which cannot with a brief reason for each, applies user-supplied restrictions such as excluded databases or size caps, derives dependency order so views follow their base tables, and lists the Silver and Gold job stubs. Use after an estate assessment, or when the user asks what can be migrated, in what order, why something is excluded, or wants to see the migration plan.

Generate the table-creation script for a Standard AIDP catalog as an executable notebook, locally and offline. --upload is a dry run and is refused with --execute; the structure itself is created by the snowmig_01_structure workflow at S10. The script creates schemas, then tables, then views in dependency order, prints per-object progress with elapsed time so a long run stays visible, and verifies each object individually at the end. This script creates structure only and copies no data - every table arrives with zero rows; rows are copied only by the snowmig_02_copy_schema job, when the operator runs it. Use when the user has explicitly asked for a Standard catalog, or wants the migration delivered as a runnable script rather than executed straight from the CLI.

Read-only PREVIEW of a Snowflake environment from the operator's machine - inventory of tables and views with row counts, byte sizes and column types, plus the census of objects that are not tables or views, table-maintenance state and security posture. Use ONLY when the user wants to look at an account without migrating it - answering what is in there, how big the tables are, what the security posture looks like. This is NOT the discovery step of a migration: a migration discovers inside AIDP as a workflow (runbook S6), because a laptop-side read leaves no log and no evidence on the platform. If the user asked to migrate, route to snowflake-migrator-overview and follow S1 through S12.

Router, runbook and shared rules for migrating a Snowflake estate onto Oracle AI Data Platform (AIDP). Read this first whenever the user mentions migrating, moving, assessing, inventorying or cloning Snowflake databases, schemas, tables, views or warehouses onto AIDP, asks to "run the migration" or "start from zero", or asks what a Snowflake migration would involve. Carries the FIXED twelve-step order of operations and the rules that apply at every step; adds no API surface of its own.

Map Snowflake warehouses onto AIDP Spark compute clusters and produce a sizing and cost proposal. Inventories every warehouse with its size, multi-cluster scaling policy and auto-suspend, pulls observed credit consumption from ACCOUNT_USAGE where permitted, and proposes worker counts and autoscale ceilings per warehouse. Use when the user asks about compute sizing, warehouse equivalence, cluster shapes, credits, or what the migration will cost.

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.

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.

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 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.