Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable — from…
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable — from…
Automates LLDB debugging sessions with scripted breakpoint management and expression evaluation. Uses the LLDB Python SB API (lldb.SBDebugger, SBTarget, SBProcess) for…
Automatically applies when building LLM applications. Ensures proper async patterns for LLM calls, streaming responses, token management, retry logic, and error handling.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring.
Use when building LLM applications: prompt engineering, structured output, agents, RAG integration, memory management, or production deployment.
Expert LLM architect specializing in large language model architecture, deployment, and optimization.
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure.
Use when user needs LLM system architecture, model deployment, optimization strategies, and production serving infrastructure.
LLM architecture, tokenization, transformers, and inference optimization. Use for understanding and working with language models.
Generate and execute terminal workflows using llm-box. Use when the user wants to automate multi-step terminal tasks, chain commands, fetch URLs, process data, create reusable…
Generate and execute deterministic terminal workflows using llm-box. Use when the user wants to automate multi-step terminal tasks, chain commands, fetch URLs and save results,…
Implement multi-layer LLM caching with exact match, semantic similarity, and provider-side prompt caching.
Multi-level caching strategies for LLM applications - semantic caching (Redis), prompt caching (Claude/OpenAI native), cache hierarchies, cost optimization, and Langfuse cost…
Implement or refactor a reusable LLM client layer for text-only or multimodal projects. Use when a project needs one final repo-local client module, OpenAI-compatible provider…
Cut LLM cost and latency with caching, model tiering, prompt diet, batching, and streaming UX. Use when the inference bill or response time needs engineering down.
Model the cost and latency of an LLM feature before it ships and surprises the bill. Use when asked to estimate LLM API costs, set a latency/token budget, decide which model tier…
Reduce LLM API costs without sacrificing quality. Covers prompt caching (Anthropic), local response caching, prompt compression, debouncing triggers, and cost analysis.
Use when you need to reduce LLM API spend, control token usage, route between models by cost/quality, implement prompt caching, or build cost observability for AI features.
Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated pipelines without deep…
Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues.
当需要部署或训练 LLM/VLM 时使用；覆盖 vLLM OpenAI-compatible 服务、多模态输入限制、Qwen3.5 工具调用、thinking/reasoning 控制、CUDA Graph 策略，以及 ms-swift SFT/DPO/GRPO full training、数据校验、显存排错和训练检查。
LLM deployment and serving — vLLM, Ollama, TGI, llama.cpp. Model quantization, GPU optimization, API serving
Patterns for building LLM applications - prompt engineering, RAG pipelines, cost optimization, multi-model routing, and evaluation. Auto-triggers when working with AI/LLM code.
LLM evaluation harness for accuracy benchmarking. MMLU/HumanEval/MATH eval runners, model-graded scoring, prompt regression testing, and per-skill accuracy tracking.
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing. — from whatamelon/AGENT-HARNESS-BASELINE
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
LLM testing and evaluation tools including promptfoo, trulens, and evals frameworks
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Build automated LLM evaluation pipelines with benchmarks, regression tests, RAGAS, and human eval workflows.
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing. — from majiayu000/claude-skill-registry
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
LLM Fine-Tuning expert. Covers LoRA, QLoRA, PEFT, dataset preparation, Hugging Face Trainer/TRL, RLHF, DPO, quantization (GPTQ/AWQ/GGUF), model merging, distributed training with…
Expert guide for AI FinOps, dynamic model routing (LiteLLM, Portkey, RouteLLM), cost optimization, budget enforcement, semantic caching, and latency-based fallback architectures /…
Comprehensive guide to using LLMs throughout the game development lifecycle - from design to implementation to testingUse when "ai game development, llm game dev, claude game, gpt…
Diagnostic playbook for triaging a self-hosted LLM gateway (LiteLLM or any Postgres-backed OpenAI-compatible proxy) running via Docker Compose on a homelab host.
LLM gateway and routing configuration using OpenRouter and LiteLLM. Invoke when: - Setting up multi-model access (OpenRouter, LiteLLM) - Configuring model fallbacks and r — from…
LLM gateway and routing configuration using OpenRouter and LiteLLM. Invoke when: - Setting up multi-model access (OpenRouter, LiteLLM) - Configuring model fallbacks and r — from…
Design input and output guardrails for an LLM app — decide what to check (injection patterns, PII, secrets, policy, schema, leakage, toxicity), place them as input vs.
Specify the safety and reliability guardrails for an LLM feature before it ships. Use when asked to define LLM guardrails, add safety controls to an AI feature, prevent prompt…
Optimize LLM response latency with caching and architecture. TRIGGERS - Use when user needs help with llm-latency-optimizer related tasks.
LLM inference load testing for throughput and concurrency limits. Token/s benchmarks, concurrent request sweeps, latency-vs-throughput curves, and breaking-point identification.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools.
Instrument LLM applications with request tracing, token accounting, quality signals, and feedback loops.
Testing LLM-powered features including output quality validation, hallucination detection, token usage monitoring, and prompt regression testing.
面向团队与企业用户的 Whisper 语音转文字工具(专业版)。核心能力: - 涵盖免费版全部能力(本地转录、翻译、多格式输出) - 批量处理:目录级递归转录,支持任务队列 - GPU 加速:CUDA / Metal / MPS 全面支持 - 说话人分离(diarization):多人对话识别 - 自定义词典:专业术语与品牌名词优化 - — from…
基于 Whisper v1 稳定版本的企业级语音转文字工具(专业版)。核心能力: - 涵盖免费版全部能力(v1 稳定 CLI、多格式输出、翻译) - 批量处理:目录递归与任务队列 - 模型管理:多版本预加载与热切换 - 性能调优:GPU 加速、半精度推理、批处理 - 自定义词典:initial_prompt 注入领域术语 - 服务化部署: — from…
Implement semantic and exact-match caching for LLM responses to reduce cost 40-60% and latency. Activate on: LLM caching, semantic cache, reduce API costs, cache AI responses.
Use when designing how one canonical agent core runs across Codex, Claude Code, Gemini CLI, Cursor, and AGENTS.md-compatible tools.