By sickn33
Build autonomous AI agents and MCP servers with production-ready LLM patterns, including RAG, prompt engineering, observability, and agent testing.
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.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.
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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-agent-mcp-builderEditorial "AAS API Platform Builder" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Mobile Developer" 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 "Web Designer" bundle for Claude Code from Agentic Awesome Skills.
Editorial "Agent Architect" bundle for Claude Code from Antigravity Awesome Skills.
Agents for multi-agent orchestration, MCP tooling, and agentic workflows
Agent configuration utilities - project assimilation, config auditing, teammate definitions, MCP management, and hooks configuration
Official Agno AI agent framework skill - build production-ready agents, multi-agent teams, workflows, MCP integrations, and deploy with AgentOS.
AgenticFlow developer tools for Claude Code — build AI agents, deploy multi-agent workforces, and automate operations against the AgenticFlow platform via the `af` CLI.
Unified capability management center for Skills, Agents, and Commands.