By sickn33
Build and test AI agents and MCP servers: design autonomous agents, implement RAG pipelines, manage LLM context windows, build MCP servers from scratch, and use observability with Langfuse. Includes prompt engineering, agent testing, and LangGraph patterns.
Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.
Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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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-aas-agent-mcp-builderPlugin-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 "Agent Architect" bundle for Claude Code from Antigravity Awesome Skills.
Unified capability management center for Skills, Agents, and Commands.
Official Agno AI agent framework skill - build production-ready agents, multi-agent teams, workflows, MCP integrations, and deploy with AgentOS.
Agents for multi-agent orchestration, MCP tooling, and agentic workflows
AgenticFlow developer tools for Claude Code — build AI agents, deploy multi-agent workforces, and automate operations against the AgenticFlow platform via the `af` CLI.
Agent configuration utilities - project assimilation, config auditing, teammate definitions, MCP management, and hooks configuration