ai-agent-codex

Apply layer-specific quality checklists for Python ETL, Airflow DAGs, dbt models, SQL, FastAPI, Vue.js, and Docker Compose changes in the HH data platform.

Inspect git history and diffs to identify what changed, which platform layers are affected, and what risk signals to prioritise before applying review checklists.

Design small, maintainable Airflow DAGs with clear task boundaries, retry policy, and runtime assumptions.

Apply asyncio patterns for concurrent IO, background work, and coordinated refresh flows in Python analytics services.

Design dbt source, staging, and mart models with explicit schemas and clear layer responsibilities.

Break down a data-platform request into sequenced work items, dependencies, risks, and definition of done.

Design a reproducible local Docker Compose setup for Airflow, PostgreSQL, and related developer workflows.

Design FastAPI services with explicit request and response contracts, async handlers, and clean analytical API boundaries.

Map analytical questions to appropriate visual encodings, interactions, and dashboard behavior for vacancy-market data.

Analyze HH API entities, response fields, pagination behavior, and ingestion considerations for vacancy-related pipelines.

Define stable analytical metrics, dimensions, grains, and mart requirements for labor-market analytics.

Write PostgreSQL DDL and analytical SQL with explicit types, maintainable naming, and raw-to-analytics conventions.

Implement small Python ETL components that keep extraction, normalization, and persistence responsibilities separate.

Define backend-to-frontend delivery patterns for near-realtime vacancy analytics, freshness indicators, and streaming updates.

Produce concise implementation-ready requirements, assumptions, field mappings, and acceptance criteria.

Design Vue.js dashboard interfaces for filtering, exploring, and monitoring HH vacancy analytics without diluting metric clarity.