By sickn33
Enables end-to-end data analytics workflows: design tracking systems, validate data quality with dbt, optimize SQL and Postgres performance, run structured A/B tests, and transform raw data into executive-ready narratives.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
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.
Transform raw data into compelling narratives that drive decisions and inspire action.
Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.
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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-analyticsPlugin-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 "AAS Data Analytics" bundle for Claude Code from Agentic Awesome Skills.
Skills collection covering data engineering workflows, pipelines, infrastructure, and tools for Claude Code.
Data & analytics skills: Metrics Framework, SQL Query Explainer, Dashboard Brief, Cohort Analysis, Data Pipeline Spec, Chart Data Extractor, A/B Test Readout, Metric Tree Builder, Data Quality Audit. Build North Star metric trees, explain and optimise SQL, spec dashboards, read out A/B test results with significance and guardrails, and audit datasets for quality before you trust them.
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
Data analysis toolkit — multi-dialect SQL patterns, statistical methods, dataset profiling, and analysis quality assurance.
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.