AI & LLM Engineering · By borghei

Test and evaluation harness for AI agents — scenario suites, deterministic replay, regression diffing, cost and latency budgets.

borghei/Claude-Skills

Design a product metrics dashboard — North Star, input metrics, and guardrails — that a team actually uses to make decisions.

borghei/Claude-Skills

Design AI agent communication protocols: MCP tool schemas, A2A, function calling, and inter- agent messaging.

borghei/Claude-Skills

Design multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management.

borghei/Claude-Skills

This skill should be used when the user asks to "evaluate LLM output quality", "set up LLM-as-judge", "build an eval rubric", "compare model outputs pairwise", or "measure agent quality".

borghei/Claude-Skills

This skill should be used when the user asks to "estimate LLM costs", "count tokens in prompts", "optimize prompt token usage", "compare model pricing", or "reduce LLM API costs".

borghei/Claude-Skills

Context management engine for AI coding agents. An agent skill from borghei/Claude-Skills.

borghei/Claude-Skills

Prompt engineering frameworks for building, testing, versioning, and evaluating prompts: chain-of-thought, few-shot, regression testing, and rubrics.

borghei/Claude-Skills

Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously.

borghei/Claude-Skills

Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment.

borghei/Claude-Skills

Prompt engineering and LLM evaluation. An agent skill from borghei/Claude-Skills.

borghei/Claude-Skills

Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation.

borghei/Claude-Skills

Design RAG pipelines: chunking, retrieval evaluation, and architecture.

borghei/Claude-Skills