Review or design a data pipeline architecture. Assesses ingestion pattern, transformation design, orchestration, idempotency, freshness SLAs, data contracts at boundaries, dbt test coverage, lineage, and observability. Covers batch ETL/ELT, streaming, lambda, and kappa architectures. DataOps and OpenLineage aligned. TOGAF Phase C aware.
EnglishSystematic data validation, error detection, cross-source reconciliation, and query correctness checking for analytical work. Use when validating Snowflake queries, catching calculation errors, reconciling metrics across different data sources, checking for null values, ensuring date range validity, detecting statistical anomalies, validating metric calculations (median vs mean, rate normalization), checking aggregation grain (per-record vs per-entity), validating contribution analysis for non-additive metrics, or validating consistency across analysis sections. Essential when reviewing analysis before publication, debugging unexpected results, or ensuring data quality in reports. Triggers include "validate this query", "check for errors", "why don't these numbers match", "should I use median or mean", "why don't contributions sum to 100%", "reconcile these metrics", "verify data quality", or any request to catch potential issues in data or calculations.
EnglishUse when you need to synthesize market, competitor, audience, and positioning research with citations.
EnglishGenerate a complete, production-ready application from an OpenAPI…
EnglishEnd-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptoolsscm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptoolsscm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
EnglishProvides exact patterns for diagnosing and fixing automatic batching regressions in React 18 class components. Use this skill whenever a class component has multiple setState calls in an async method, inside setTimeout, inside a Promise .then() or .catch(), or in a native event handler. Use it before writing any flushSync call - the decision tree here prevents unnecessary flushSync overuse. Also use this skill when fixing test failures caused by intermediate state assertions that break after React 18 upgrade.
EnglishRefactor given method ${input:methodName} to reduce its cognitive complexity to ${input:complexityThreshold} or below, by extracting helper methods.
EnglishUpdate an existing implementation plan file with new or update requirements to provide new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
English
AGENT SKILLS ATLASIncludes Data Pipeline Review, Data Quality Validation, Market Research Synthesis, and other workflows.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLISupported by the Atlas install prompt. Skill-specific requirements vary.
Pack stats
What’s included
Data Pipeline Review
Review or design a data pipeline architecture. Assesses ingestion pattern, transformation design, orchestration, idempotency, freshness SLAs, data contracts at boundaries, dbt test coverage, lineage, and observability. Covers batch ETL/ELT, streaming, lambda, and kappa architectures. DataOps and OpenLineage aligned. TOGAF Phase C aware.
Data Quality Validation
Systematic data validation, error detection, cross-source reconciliation, and query correctness checking for analytical work. Use when validating Snowflake queries, catching calculation errors, reconciling metrics across different data sources, checking for null values, ensuring date range validity, detecting statistical anomalies, validating metric calculations (median vs mean, rate normalization), checking aggregation grain (per-record vs per-entity), validating contribution analysis for non-additive metrics, or validating consistency across analysis sections. Essential when reviewing analysis before publication, debugging unexpected results, or ensuring data quality in reports. Triggers include "validate this query", "check for errors", "why don't these numbers match", "should I use median or mean", "why don't contributions sum to 100%", "reconcile these metrics", "verify data quality", or any request to catch potential issues in data or calculations.
Market Research Synthesis
Use when you need to synthesize market, competitor, audience, and positioning research with citations.
Openapi to Application Code
Generate a complete, production-ready application from an OpenAPI specification
Python Pypi Package Builder
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI. Covers all four build backends (setuptools+setuptoolsscm, hatchling, flit, poetry), PEP 440 versioning, semantic versioning, dynamic git-tag versioning, OOP/SOLID design, type hints (PEP 484/526/544/561), Trusted Publishing (OIDC), and the full PyPA packaging flow. Use for: creating Python packages, pip-installable SDKs, CLI tools, framework plugins, pyproject.toml setup, py.typed, setuptoolsscm, semver, mypy, pre-commit, GitHub Actions CI/CD, or PyPI publishing.
React18 Batching Patterns
Provides exact patterns for diagnosing and fixing automatic batching regressions in React 18 class components. Use this skill whenever a class component has multiple setState calls in an async method, inside setTimeout, inside a Promise .then() or .catch(), or in a native event handler. Use it before writing any flushSync call - the decision tree here prevents unnecessary flushSync overuse. Also use this skill when fixing test failures caused by intermediate state assertions that break after React 18 upgrade.
Refactor Method Complexity Reduce
Refactor given method ${input:methodName} to reduce its cognitive complexity to ${input:complexityThreshold} or below, by extracting helper methods.
Update Implementation Plan
Update an existing implementation plan file with new or update requirements to provide new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
Quick start
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Try it with your agent
After installation, start with a request like:
Use the Data Pipeline Review skill to help with my task.
Ask me for the context you need before getting started.Requirements & license
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