By sickn33
Build and orchestrate production data pipelines with Airflow, dbt, and Spark, including data quality validation, streaming architectures, and vector database integration for RAG systems.
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.
Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
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A complete local skill catalog for coding agents—from project inspection and agent-owned selection to a reproducible, reviewable plan.
Current release: V15.1.0. This release includes AAS Core for complete local catalog search, agent-owned selection, manifest validation, planning, and diagnosis. Apply and recovery remain experimental and outside the supported preview path.
Codex or Claude inspects your project, enumerates its primary capabilities, searches and compares candidates across the complete local AAS catalog, and chooses the exact skills. Core imposes no semantic policy that favors a small stack; the manifest format has an explicit technical maximum of 128 skills. All 1,968 skills in the current catalog remain individually searchable, readable, and selectable. AAS Core does not rank or recommend skills. Its read-only compose_stack tool validates and returns the agent-owned manifest in memory; a client or the aas CLI persists the reviewed stack and its optional selection-evidence sidecar.
Read the AAS Core preview guide →
Project
-> inspected by Codex or Claude (not by AAS)
-> agent searches and reads the complete local catalog
-> AAS MCP (local stdio, read-only)
-> Codex or Claude chooses exact skill IDs
-> compose_stack validates the selection in memory (read-only)
-> client or AAS CLI persists aas-stack.json and optional evidence
-> 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:
npx claudepluginhub sickn33/agentic-awesome-skills --plugin agentic-bundle-data-engineeringPlugin-safe Claude Code distribution of Agentic Awesome Skills with 1,933 supported skills.
Editorial "AAS Security Engineer" bundle for Claude Code from Agentic Awesome Skills.
Plugin-safe Claude Code distribution of Agentic Awesome Skills with 1,916 supported skills.
Editorial "Web Designer" bundle for Claude Code from Agentic Awesome Skills.
Editorial "AAS QA & Test Automation" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Data Engineering" bundle for Claude Code from Antigravity Awesome Skills.
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration
🔧 Data Engineer — Data Pipeline Engineer + Data Infrastructure Specialist
Comprehensive data engineering toolkit combining ETL pipelines, data quality, and data architecture. Includes data architect agent for holistic data engineering decisions.
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture