Methasit-Pun

Umbrella skill for running a data project end-to-end through its lifecycle stages — discover sources → profile the data → architect the platform → build the medallion pipeline → refactor the code. Use this whenever the user is kicking off a new data project, asks "where do I start" or "what are the steps", or is somewhere mid-lifecycle and unsure which stage skill applies. This skill ROUTES to the stage sub-skills (data-sourcing, data-profiling, data-architecture, medallion-design, notebook-refactor) and sequences them, pulling in the right one for the user's current stage.

Data collection and readiness — the step BEFORE a pipeline exists. Catalog candidate data sources for an objective, rank each by importance/impact, and record where to get it (internal system vs. open/public dataset), how to access it, its refresh cadence, licensing, and readiness blockers. Use this skill whenever a project is starting and the sources aren't decided yet, when the user asks "what data do I need for X and where do I get it", when weighing internal vs. open-source data, or when assessing whether a source is accessible/usable before committing to a build. This sits upstream of pipeline-design — it decides WHAT to ingest before deciding HOW.

Profile and map raw data BEFORE designing a schema. One-time exploratory analysis to learn the true shape of a dataset — row/column counts, null rates, cardinality, value distributions, ranges, data types, candidate keys, duplicates, referential relationships — then a source-to-target field mapping and an ER diagram. Use this skill whenever the user has data in hand and needs to understand it before modeling, is about to design a schema, asks "what does this data actually look like", needs to find the primary/composite key of an unfamiliar table, or must map source fields to a target model. This is exploratory and one-time; ongoing production validation is data-quality's job, and modeling patterns are schema-design's job.

Umbrella skill for shaping data once it has landed — warehouse schema design (star/snowflake/OBT/SCD/grain), SQL for analytics (window functions, CTEs, optimization), dbt model layers/tests/macros/incrementals, and Python transforms (pandas/Polars/PySpark performance). Use this whenever the user is designing tables, writing or reviewing analytical SQL, building dbt models, or writing Python data transformations and it isn't yet clear which tool dominates. This skill ROUTES to the focused sub-skills (schema-design, sql-patterns, dbt-patterns, python-data-patterns) and pulls in more than one when a task spans them. Trigger on: fact/dimension design, table grain, SCD, window functions, deduplication, dbt refs/materializations, or slow/memory-heavy DataFrame code.

dbt model design, ref chains, sources, tests, macros, incremental strategies, materializations, and documentation best practices. Use this skill whenever the user is writing or reviewing dbt models, configuring dbt tests, designing model layers (staging/intermediate/marts), asking about incremental models, choosing materializations, writing macros, setting up sources.yml, or troubleshooting dbt run/test failures. Also trigger when the user mentions dbt refs, lineage graphs, model dependencies, dbt Cloud, the dbt CLI, or when they want to transform data already in the warehouse using SQL. If the project uses dbt at all, this skill should be active for any transformation questions.

Pandas, Polars, and PySpark idioms for production data engineering — chunked reads, memory-safe transforms, vectorized operations, type optimization, and performance patterns. Use this skill whenever the user is writing a Python data transformation script and running into memory issues, slow performance, or correctness bugs with large datasets. Also trigger when the user asks how to handle large CSV/Parquet files, process data in batches, use Polars instead of Pandas, optimize a PySpark job, or reduce DataFrame memory usage. If you see someone iterating row-by-row over a DataFrame, this skill should trigger immediately.

Data modeling for analytical workloads — star schema, snowflake schema, one big table (OBT), slowly changing dimensions (SCD), normalization tradeoffs, grain definition, and surrogate key strategies. Use this skill whenever the user is designing or reviewing a data warehouse schema, planning a fact/dimension table layout, deciding how to model a business entity (customer, order, event, product), or asking how to handle historical changes to dimension attributes. Also trigger when the user asks about dbt model design, table granularity, or how to structure data for BI tools like Looker, Tableau, or Power BI. Get this right before building the pipeline — a bad schema is expensive to fix later.

Best-practice SQL for analytical workloads — window functions, CTEs, query optimization, partitioning strategies, and anti-patterns to avoid. Use this skill whenever the user is writing or reviewing a SQL query that goes beyond a basic SELECT, especially on BigQuery, Snowflake, Redshift, or DuckDB. Trigger on mentions of aggregations, ranking, running totals, session analysis, lag/lead comparisons, deduplication, slowly-changing lookups, or any time the user asks "how do I write a query for X". Also trigger when a query looks slow, returns wrong results, or the user asks for a code review of existing SQL.

Review TypeScript DDD clean architecture code for correctness bugs, layer violations, and simplification opportunities. Trigger when the user says "review this", "check this code", "code review", "what's wrong with this", "is this correct DDD?", "does this follow clean architecture?", or when changes touch domain, application, or infrastructure layers.

Security review for TypeScript DDD code — OWASP-mapped checks for injection, auth/authz, data exposure, and misconfiguration. Trigger when the user says "security review", "check for vulnerabilities", "is this secure?", "review auth code", or when changes touch authentication, authorization, input handling, external APIs, file uploads, or user-controlled data.

Review and simplify TypeScript DDD code — remove unnecessary complexity, eliminate duplication, reduce premature abstraction. Trigger when the user says "simplify this", "too much boilerplate", "over-engineered", "clean this up", "refactor this", or when code has grown too abstract or introduces patterns before they prove value.

Design and implement the CQRS (Command Query Responsibility Segregation) pattern in a TypeScript DDD clean architecture project. Trigger when the user says "implement CQRS", "add a command", "add a query", "create a use case", "add a command handler", "build the application layer", "set up the command bus", or when the user needs to add a new feature and is asking how to wire up the application layer. Also trigger when distinguishing between write operations (commands) and read operations (queries) in any context.

Design and implement the Repository pattern in a TypeScript DDD clean architecture project — define repository interfaces in the domain layer and implementations in the infrastructure layer. Trigger when the user says "create a repository", "implement persistence", "add database access", "wire up TypeORM/Prisma/Drizzle", "implement the repository interface", "add Unit of Work", "how do I persist this aggregate", or when connecting domain aggregates to any data store. Also trigger when reviewing persistence code that may be violating the dependency rule.

Design and implement CI/CD pipelines for a TypeScript DDD clean architecture project — GitHub Actions, GitLab CI, Docker builds, environment promotion, and secrets management. Trigger when the user says "set up CI", "add a pipeline", "automate tests", "write a GitHub Actions workflow", "configure deployment", "add Docker support", "set up CD", "automate the build", or when the project needs automated quality gates before merge. Also trigger when the user asks about environment promotion (dev → staging → prod) or secrets management strategy.

Write a structured Architecture Decision Record (ADR) for any technology or design choice in a TypeScript DDD clean architecture project. Trigger when the user says "document this decision", "write an ADR", "why did we choose X", "we decided to use Y instead of Z", "record this architecture choice", or when a significant design trade-off is being discussed and should be captured for the team. Also trigger when the user debates between two patterns (e.g. TypeORM vs Prisma, REST vs gRPC, monolith vs microservices).

Turn a one-line feature or system objective into a complete, step-by-step construction plan for a TypeScript DDD clean architecture project. Each step is self-contained so a fresh agent can execute it cold. Use this skill whenever the user says "plan", "blueprint", "roadmap", "how do we build", "design the system for", or describes a feature that spans multiple layers (domain → application → infrastructure → presentation). Also trigger when the user asks to break down a bounded context, design an aggregate, or plan a new module.

CI/CD specialist for TypeScript DDD projects on GitHub Actions. Use this skill whenever: a GitHub Actions workflow is failing or missing; `tsc` reports type errors in CI; Vitest tests are red in the pipeline; the user wants to verify the project is ready to deploy or merge; or any combination of "CI is broken", "types are failing", "tests won't pass", "fix the pipeline", "deploy ready", "prepare for production", "make CI green". Covers scaffolding missing workflow files, diagnosing and fixing TypeScript errors, diagnosing and fixing Vitest failures, and running a pre-deploy readiness gate. Always invoke before touching any .yml, tsconfig, or test file in a CI context.

Use this skill to generate well-branded interfaces and assets for ForgetMeNotReels, either for production or throwaway prototypes/mocks/etc. Contains essential design guidelines, colors, type, fonts, assets, and UI kit components for prototyping.