---
title: "agentic-bundle-aas-agent-mcp-builder"
description: "Portable skills-only \"AAS Agent & MCP Builder\" plugin from Agentic Awesome Skills."
canonical: https://agentpluginsdirectory.com/plugins/agentic-bundle-aas-agent-mcp-builder--ibrdot
last-updated: 2026-10-24
---

# agentic-bundle-aas-agent-mcp-builder
Portable skills-only "AAS Agent & MCP Builder" plugin from Agentic Awesome Skills.
- Slug: agentic-bundle-aas-agent-mcp-builder--ibrdot
- Publisher: ibrdot and contributors
- Repository: https://github.com/ibrdot/swe-re-expert
- Manifest: plugins/agentic-bundle-aas-agent-mcp-builder/plugin.json
- Version: 19.11.0
- License: MIT
- Category (editorial): other
- Skills: 17 (agent-evaluation-reporting, agent-evaluation, agent-harness-fault-injection, agent-memory-systems, ai-agents-architect, context-window-management, langfuse, langgraph, llm-app-patterns, llm-evaluation, mcp-builder, mcp-tool-developer, prompt-caching, prompt-engineering, rag-engineer, rag-implementation, skill-security-audit)
- MCP servers: 0
- Stars: 4
- Repository created: 2026-08-24
- Repository last pushed: 2026-10-19
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/agentic-bundle-aas-agent-mcp-builder--ibrdot
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## Skills

- agent-evaluation-reporting: Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.
- agent-evaluation: Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.
- agent-harness-fault-injection: Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
- agent-memory-systems: Design tenant-scoped memory, retrieval and retention for agents. Use when planning RAG, conversation history, vector stores or remembering across sessions.
- ai-agents-architect: Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
- context-window-management: Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot
- langfuse: 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.
- langgraph: 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.
- llm-app-patterns: Architecture and integration sketches for LLM applications, with explicit retrieval, tool, privacy and verification boundaries.
- llm-evaluation: Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
- mcp-builder: Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.
- mcp-tool-developer: Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.
- prompt-caching
- prompt-engineering
- rag-engineer
- rag-implementation
- skill-security-audit

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.