Methasit-Pun
- 40 skills
- 0 followers
- 14 hours ago last updated
- ▌ Cicd Fix Deploy · methasit-punCI/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.
- ▌ TS Ddd Simplify · methasit-punReview 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.
- ▌ TS Ddd CI Design · methasit-punDesign 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.
- ▌ Docker Redis Iac · methasit-punMulti-stage Docker builds, Docker Compose for local dev, Redis Socket.IO adapter for horizontal scaling, and safe Prisma production migrations.
- ▌ Tdd Domain Layer · methasit-punStrict TDD for Domain and Application layers in Clean Architecture TypeScript. Red-Green-Refactor with Vitest. Mocks ports, never hits a database.
- ▌ TS Ddd Code Review · methasit-punReview 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.
- ▌ Agent Orchestration · methasit-punNode.js automation scripts, AST-based code generation, and CLI tools for scaffolding Clean Architecture boilerplate from Prisma schemas.
- ▌ TS Ddd Cqrs · methasit-punDesign 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.
- ▌ Zod Express Validation · methasit-punRuntime request validation with Zod for Express and Socket.IO. Generates TypeScript DTOs via z.infer. Blocks unvalidated data from reaching Use Cases.
- ▌ TS Ddd Security Review · methasit-punSecurity 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.
- ▌ TS Ddd Clean Architecture · methasit-punEnforces Hexagonal Architecture, Domain-Driven Design (DDD), and Event-Driven workflows for Node.js using Express, Prisma, and Socket.IO.
- ▌ TS Ddd Blueprint · methasit-punTurn 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.
- ▌ TS Ddd Adr Writer · methasit-punWrite 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).
- ▌ TS Ddd Bounded Context · methasit-punMap bounded contexts for a TypeScript DDD project — identify context boundaries, define ubiquitous language, map relationships (ACL, Partnership, Shared Kernel, etc.), and surface aggregate roots and domain events. Trigger when the user says "map the domain", "define bounded contexts", "what are the aggregates", "design the domain model", "identify the ubiquitous language", "how should we split this domain", or when planning a new feature that touches multiple domain areas. Also trigger when the user describes a business process and wants to know how to structure it in DDD.
- ▌ TS Ddd Repository Pattern · methasit-punDesign 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.
- ▌ Dbt Patterns · methasit-pundbt 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.
- ▌ SQL Patterns · methasit-punBest-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.
- ▌ Data Sourcing · methasit-punData 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.
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- ▌ Schema Design · methasit-punData 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.
- ▌ Data Profiling · methasit-punProfile 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.
- ▌ Data Migration · methasit-punMoving data between systems safely — cutover planning, backfill strategies, dual-write patterns, validation, rollback procedures, and zero-downtime migration techniques. Use this skill whenever the team is migrating from one database or warehouse to another (MySQL → Snowflake, Redshift → BigQuery, on-prem → cloud), replacing a legacy pipeline, doing a major schema change on a live table, or planning a cutover that cannot have downtime. Also trigger when the user asks about dual-write, shadow reads, data validation across systems, incremental vs. full migration, or how to safely retire an old system. If the phrase "migrate", "move data", "cutover", "legacy system", or "replace the old pipeline" appears, this skill should be active.
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- ▌ Data Quality · methasit-punWrite systematic data quality checks — validation rules, Great Expectations suites, dbt tests, anomaly detection, null/type/range/referential integrity assertions, and monitoring patterns for production pipelines. Use this skill whenever the user is dealing with bad data in a pipeline, setting up validation before or after a load step, adding tests to dbt models, writing Great Expectations expectations, or trying to detect when upstream data has changed shape. Also trigger when stakeholders keep finding incorrect numbers, when a pipeline silently loads garbage, or when the user asks "how do I make sure my data is correct". Prevention is cheaper than debugging.
- ▌ Data Lifecycle · methasit-punUmbrella 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.
- ▌ Pipeline Design · methasit-punDesign ETL/ELT pipelines end-to-end — source connectors, extraction strategies, transform logic, load patterns, idempotency, scheduling, and error handling. Use this skill whenever the user is starting a new ingestion job, planning how data moves from a source (REST API, database, file, webhook, message queue) into a data warehouse or data lake. Also trigger when the user asks about pipeline architecture, incremental vs. full loads, backfill strategies, CDC, retry logic, or orchestration choices (Airflow, Prefect, dbt). This skill should feel like pairing with a senior data engineer on day one of a new pipeline project.
- ▌ Medallion Design · methasit-punDesign a medallion (bronze/silver/gold) ETL architecture interactively, objective-first. List the available data, confirm the objective, then design GOLD first to match the objective and get the user to review it before moving down to silver, then bronze (top-down default) — or bronze-up if the user asks. Asks the user to confirm at each layer boundary rather than designing all three in one shot. Wraps the reusable utils/ library (bronze.py, silver.py, scd.py, watermark.py, metadata.py, quality.py). Use this skill whenever the user wants a bronze/silver/gold or medallion/lakehouse layout, is building a layered Delta/warehouse pipeline, or wants an objective-driven top-down layer design. For extraction/idempotency mechanics reference pipeline-design; this owns the layered design conversation.
- ▌ Data Contracts · methasit-punDefine and enforce schema contracts between producer and consumer teams — field types, nullability, allowed values, versioning, breaking vs. non-breaking changes, and change detection patterns. Use this skill whenever two teams or services share a dataset and upstream changes keep breaking the downstream silently, when a team wants to formalize what a dataset "promises" to its consumers, or when setting up schema validation at pipeline boundaries. Also trigger when the user asks about schema evolution, backward/forward compatibility, schema registries, Great Expectations for inter-team contracts, or when a producer is about to make a "small" schema change and you want to assess its downstream impact. If upstream and downstream are owned by different people, this skill should be active.
- ▌ Data Governance · methasit-punData lineage tracking, PII tagging, access control policies, data catalog metadata standards, retention policies, and audit logging for regulatory compliance. Use this skill whenever the company is subject to PDPA, GDPR, HIPAA, or any data privacy regulation, when an audit requires proof of who accesses what data, when PII fields need to be identified and classified in a dataset, when setting up column-level access control, or when building a data catalog. Also trigger when someone asks about data masking, anonymization, right-to-erasure workflows, role-based data access, or data lineage from source to BI tool. If the word "compliance", "audit", "PII", "sensitive data", or "regulation" appears, this skill should be active.
- ▌ Streaming Patterns · methasit-punKafka, Flink, Kinesis, and Spark Structured Streaming design — consumer groups, partitioning, exactly-once semantics, lag monitoring, windowing, and late-arriving data. Use this skill whenever the user needs real-time or near-real-time data processing, is redesigning a batch pipeline into streaming, asks about event-driven architectures, or mentions Kafka topics, consumer lag, checkpointing, watermarks, or stream-table joins. Also trigger when the user says batch is "too slow", stakeholders want "live" dashboards, or the pipeline needs to react to events as they happen rather than on a schedule. If latency requirements are under a few minutes, this skill should be active.
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- ▌ Python Data Patterns · methasit-punPandas, 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.
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- ▌ Cloud Infra Data · methasit-punAWS/GCP/Azure data infrastructure — S3/GCS/ADLS partitioning, BigQuery slot management, Redshift spectrum, Snowflake warehouses, IAM roles for data access, cost optimization, and managed service selection. Use this skill whenever the user is deploying a pipeline to cloud, choosing between managed data services, configuring storage for a data lake, setting up IAM/permissions for pipelines, asking about BigQuery pricing, Redshift vs. BigQuery vs. Snowflake, S3 bucket layout, or cloud-specific performance tuning. Also trigger when the user mentions cloud costs, slow BigQuery queries, Redshift concurrency scaling, storage formats in the cloud, or cross-account data access. If it touches cloud + data together, this skill should be active.
- ▌ Stakeholder Reporting · methasit-punTranslate pipeline metrics, SLA breaches, data quality failures, and incidents into clear non-technical summaries for business stakeholders. Use this skill whenever data was late, wrong, or missing and someone needs to communicate what happened to a manager, director, or business team. Also trigger when writing incident reports, SLA breach notifications, data quality summaries, pipeline health updates, or any communication where the audience doesn't know what Airflow or BigQuery is. If the user needs to explain a technical data failure to a non-technical person, this skill should be active.
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- ▌ Notebook Refactor · methasit-punRefactor a Jupyter/.ipynb notebook from top-to-bottom exploratory sprawl into small, named, reviewable functions with clear inputs and outputs. Extract subfunctions, separate config/params from logic, remove hidden cell-order dependencies and global-state leakage, add docstrings and light tests, and make the notebook re-runnable top-to-bottom and diff-friendly. Use this skill whenever the user has a messy notebook they want cleaned up, wants notebook code turned into functions or a module, is preparing a notebook for review/handoff/production, or asks to make an .ipynb readable or testable. This is about code STRUCTURE and reviewability; performance and correctness of data code is python-data-patterns' job.
- ▌ Orchestration Patterns · methasit-punAirflow/Prefect/Dagster DAG design — task dependencies, retries, SLAs, backfill strategies, sensors, and failure recovery. Use this skill whenever the user is building or debugging a scheduled pipeline with multiple steps, asking how to handle task failures, setting up retries or alerts, designing a DAG structure, choosing between orchestrators, or dealing with backfill/reprocessing of historical data. Also trigger when the user mentions Airflow operators, Prefect flows, Dagster assets, task queues, or pipeline scheduling — even if they don't say "orchestration" explicitly. If a pipeline has more than two steps and needs to run on a schedule, this skill should be active.
- ▌ Ml Feature Engineering · methasit-punFeature store patterns, training/serving skew prevention, feature pipelines for ML teams, point-in-time correct joins, and bridging data engineering with MLOps conventions. Use this skill whenever an ML team needs feature pipelines, when building a feature store or deciding whether to use one, when there's a training/serving skew problem (model performance in production differs from validation), when features need to be shared across multiple models, or when designing point-in-time correct feature computation. Also trigger when the user mentions feature stores (Feast, Tecton, Hopsworks), label leakage, backfilling features, offline/online store separation, or when data engineering work feeds directly into model training. If the words "features", "training set", "model pipeline", or "MLOps" appear alongside data engineering questions, this skill should be active.
- ▌ Cost Optimization Data · methasit-punQuery cost analysis, partition pruning, slot reservation strategies, storage tiering, and cloud data warehouse cost reduction. Use this skill whenever the cloud data bill is unexpectedly high, a specific query is scanning too much data, the team wants to understand what's driving BigQuery/Snowflake/Redshift costs, or when choosing between on-demand vs. reserved capacity. Also trigger when the user mentions bytes scanned, slot utilization, query cost, storage costs, Redshift concurrency, Snowflake credits, or when trying to set up cost alerts and budgets. If someone says "our BigQuery bill jumped" or "this query is expensive", this skill should be active immediately.