By sickn33
Implement end-to-end data analytics: configure tracking and metrics, transform data with dbt and SQL, optimize Postgres performance, build dashboards and d3.js visualizations, and apply AI-driven analysis for data-driven decisions.
Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.
This skill provides guidance for creating sophisticated, interactive data visualisations using d3.js.
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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.npx claudepluginhub sickn33/agentic-awesome-skills --plugin agentic-bundle-aas-data-analyticsEditorial "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.
Editorial "Data & Analytics" bundle for Claude Code from Agentic Awesome Skills.
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.
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
Explore, query, model, embed, and manage Omni Analytics through the REST API and embed SDK. Includes 9 skills, 3 specialized agents, and 3 context rules for model exploration, querying, model building, content browsing, content building, embedding, AI optimization, AI eval, and administration.
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.