By sickn33
Build and optimize production data pipelines with Airflow orchestration, dbt transformations, data quality validation, vector search for RAG, and scalable warehouse architectures.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
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Local, deterministic skill-stack composition for coding agents—from an explicit project profile to a reviewable plan before any target change.
Current release: V15.0.0. This release includes AAS Core under the Agent-First Preview claim for local search, inspection, recommendation, manifest validation, planning, and diagnosis. Apply and recovery remain experimental and outside the supported preview path.
Codex or Claude inspects your project using its own capabilities; AAS does not scan it. The agent sends the local, read-only AAS MCP an explicit project profile. AAS Core evaluates that profile and your policy against a verified local catalog, returns an explainable recommendation, and lets the agent propose aas-stack.json. The aas CLI validates that desired state and creates an immutable per-target plan before any skill changes are made.
Read the AAS Core preview guide →
Project
-> inspected by Codex or Claude (not by AAS)
-> explicit, allowlisted project profile
-> AAS MCP (local stdio, read-only)
-> deterministic AAS Core + verified local catalog
-> recommendation with evidence, exclusions, coverage, and unknowns
-> agent proposes aas-stack.json
-> AAS CLI validate + immutable plan preview
-> human review (optionally in Workbench)
The 1,967+ reusable SKILL.md playbooks, specialized plugins, bundles, workflows, and direct installers remain important. They are the content, curation, distribution, and compatibility layers around AAS Core—not competing primary products.
This is an independent community project. It is not affiliated with, sponsored by, endorsed by, or authorized by Google. Google, Antigravity, Gemini, and related product names are referenced only to describe compatibility and install targets. The GitHub repository is canonical; the hosted catalog and browser-local Workbench are companion discovery and review surfaces, not a hosted control plane.
The agent composes. You control. AAS keeps the stack reproducible.
AAS Core gives the repository one product model:
search_skills, get_skill, recommend_stack, inspect_stack, and diff_stack; it does not install skills, scan source files, call a remote model, or write to the project.aas-stack.json. The manifest pins catalog identity, targets, goals, policy, and exact skill IDs without storing repository source or model reasoning.aas stack validate checks the proposal, while aas stack plan produces an immutable, per-target plan without applying it.Editorial "AAS Agent & MCP Builder" bundle for Claude Code from Agentic Awesome Skills.
Editorial "AAS API Platform Builder" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Security Developer" bundle for Claude Code from Agentic Awesome Skills.
Editorial "DDD & Evented Architecture" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Mobile Developer" bundle for Claude Code from Agentic Awesome Skills.
npx claudepluginhub sickn33/agentic-awesome-skills --plugin agentic-bundle-aas-data-engineering-platformEditorial "Data Engineering" bundle for Claude Code from Agentic Awesome Skills.
Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Spec-Driven Development framework for Data Engineering — 58 agents, 24 KB domains, 5-phase SDD workflow, 31 commands
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture
Comprehensive data engineering toolkit combining ETL pipelines, data quality, and data architecture. Includes data architect agent for holistic data engineering decisions.