agentic-bundle-aas-agent-mcp-builder

Plugin agentic-bundle-aas-agent-mcp-builder from marketplace agentic-awesome-skills in ibrdot/swe-re-expert. Commands read from the repo's manifest — 19 days ago.

Skills (17)

• 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

  Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)

• prompt-engineering

  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.

• rag-engineer

  Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

• rag-implementation

  RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

• skill-security-audit

  Audit an Agent Skill, MCP server, connector, or desktop extension before installation by tracing code, dependencies, permissions, credentials, data flow, and irreversible actions.

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