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Found 1,644 Skills
Terminal tool that detects your hardware and recommends which LLM models will actually run well on your system
List available LLM-accessible credentials. Use when you need API keys, passwords, or other secrets that have been made available to you.
MindSpeed-LLM 环境搭建指南，用于华为昇腾 NPU。覆盖 CANN 环境激活、PyTorch + torch_npu 安装、MindSpeed 加速库安装、Megatron-LM 核心模块集成、MindSpeed-LLM 安装及环境验证。当用户需要在昇腾 NPU 上搭建 MindSpeed-LLM 训练环境时使用。
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace URL (e.g. /llm-observability/traces/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs.
Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint. Use this skill whenever the user wants to deploy, run, or serve vLLM on a Kubernetes cluster, including creating deployments, services, checking existing deployments, or managing vLLM on K8s.
CallMiner platform help — enterprise conversation analytics (Eureka) with omnichannel interaction capture, automated QA scoring, agent coaching, real-time alerts, compliance monitoring, and CX automation. Use when QA scoring is inconsistent or takes too long across agents, when needing to analyze 100% of customer interactions instead of sampling, when setting up automated compliance monitoring for regulated industries (healthcare, finance, collections), when CallMiner Coach scorecards aren't surfacing the right coaching moments, when CallMiner RealTime alerts aren't triggering during live calls, when ingesting audio or text into CallMiner via the Ingestion API, when CallMiner Analyze categories aren't matching expected interactions, or when evaluating CallMiner vs Observe.AI or NICE CXone analytics. Do NOT use for CCaaS platform selection (use /sales-ccaas-selection) or for sales-specific coaching strategy (use /sales-coaching).
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the LLM Obs SDK", or has `ddtrace` installed and wants idiomatic SDK code.
Systematic approach to exploring the TensorRT-LLM codebase before implementing new features or optimizations. Teaches how to discover existing infrastructure, trace code paths, and avoid reimplementing what already exists. Derived from real mistakes where ~250 lines of code were written and deleted because existing forward methods weren't discovered upfront. Use when starting any new feature, optimization, or code modification in TRT-LLM.
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs. Preserves explicit latency / balanced / throughput objectives. Excludes disaggregated, multi-node, and non-MTP speculative configs.
Bootstrap evaluators from production traces — emit SDK code, a framework-agnostic JSON spec, or publish online LLM-judge evaluators directly to Datadog. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.
Router skill for LLMQuant portfolio workflows. Use when the user needs company profiles, thesis tracking, theme research, watchlist monitoring, or alert management.
Router skill for LLMQuant prediction-market workflows. Use when the user needs event odds, settlement criteria, probability gaps, cross-market pricing, or prediction-market arbitrage review.