Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Other
When the user wants to solve knapsack optimization problems, select items with constraints, or maximize value with limited capacity. Also use when the user mentions "0/1 knapsack," "bounded knapsack," "unbounded knapsack," "multiple knapsack," "multidimensional knapsack," "value-based packing," "capacity-constrained selection," or "resource allocation with limits." For bin packing, see 2d-bin-packing or 3d-bin-packing. For general optimization, see optimization-modeling.
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Skill dedicated to an elderly bedroom renovation guide. Covers anti-slip and fall prevention, night waking and lighting, accessibility and elderly-friendly facilities, elderly bedroom layout, and overall elderly-friendly design across the full process, integrating the highlights of the Xi'an Renovation Classroom member knowledge base in reverse order to help users renovate an elderly bedroom with less hassle and fewer pitfalls.
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. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
Classifies LLM prompts and responses as safe or unsafe using Meta's LlamaGuard (7B v1, 8B v2 and v3) across six categories: violence and hate, sexual content, guns and illegal weapons, regulated substances, suicide and self-harm, and criminal planning. Covers HuggingFace Transformers and vLLM inference, a FastAPI moderation endpoint, Sagemaker deployment, and NeMo Guardrails integration. Use when filtering user prompts before an LLM call, moderating model outputs before display, serving a self-hosted moderation API, or reducing latency and GPU memory of a moderation model with vLLM or quantization. Use when tuning thresholds to cut false positives. For a simpler hosted option, use the OpenAI Moderation API instead.
Explains how to use Mamba selective state-space models (state-spaces/mamba package, Mamba-1 with d_state=16 and Mamba-2 with multi-head structure and d_state=128) for linear-time sequence modeling. Covers installation, the Mamba block, building a language model with MambaLMHeadModel, loading pretrained state-spaces checkpoints (130M to 2.8B) from HuggingFace, and benchmarking against Transformers. Use when implementing or loading Mamba models, processing very long sequences without a KV cache, building streaming applications, choosing between Mamba-1 and Mamba-2, or fixing install and CUDA memory problems. Requires Linux and an NVIDIA GPU. Do not use for standard Transformer models, or for RWKV, RetNet, or Hyena architectures.
Designs, reviews, and migrates MATLAB R2026a and GNU Octave numerical code, covering functions with arguments blocks, arrays and indexing, tables and timetables, matlab.unittest tests, MATLAB Projects, exportgraphics figures, MAT file inventory, and MATLAB-Python interoperability. Includes static helper scripts that scan .m files, plan argv commands, and hash artifacts without executing anything. Use when writing or reviewing .m code, migrating code between MATLAB releases or to Octave, setting up reproducible tests and projects, inspecting an untrusted MAT or .mlx file safely, or checking Python Engine compatibility. Do not use for pure Python numerical work with NumPy or SciPy.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
Computes the full impact of a proposed MLB fantasy trade across all 10 H2H categories (R/HR/RBI/SB/OBP, K/ERA/WHIP/QS/SV), rest-of-season dollar value, positional flexibility, slot-value optionality, adverse-selection prior, and weeks 21-23 playoff impact. Produces a signed verdict (accept / counter / reject) with rationale and a specific counter if applicable. Use when user mentions "trade evaluation", "trade value", "should I accept", "trade delta", "counter offer", or pastes in a trade proposal from Yahoo. Defaults to COUNTER in the middle band — pure REJECT is reserved for clearly predatory offers.
Provides nanoGPT, Karpathy's minimal PyTorch GPT implementation (model.py and train.py), with workflows for training character-level Shakespeare on CPU, reproducing GPT-2 124M on OpenWebText with multi-GPU torchrun, fine-tuning pretrained GPT-2 checkpoints, and training on custom text. Use when learning how GPT and transformer blocks work from scratch. Use when running a small training experiment on CPU or a single GPU. Use when modifying a transformer variant in plain PyTorch. Use when preparing character-level or BPE binary datasets. Use when troubleshooting out-of-memory or slow training in nanoGPT. Not for production or large-scale distributed training; use HuggingFace Transformers or Megatron-LM instead.
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
Design or redesign an org structure and operating model by producing an Organizational Design Pack (design brief, current-state map, operating-model decision, target org blueprint, transition plan). Use for org design, reorgs, team topology, functional vs divisional structures, and centralized vs decentralized decision-making. Category: Leadership.
Answer questions from the perspective of Plato — the theory of Forms, dialectic, philosophical dialogue, and questioning the essence. Use when the user explicitly asks for this figure's perspective, mentions "plato-perspective", or wants to apply his core methods to analyze a problem; do not trigger automatically for general questions.
Prepare manuscripts for PLOS journals (PLOS ONE, PLOS Biology, PLOS Computational Biology, PLOS Genetics, PLOS Medicine, PLOS Pathogens, PLOS Neglected Tropical Diseases, PLOS Global Public Health and others), covering structure, abstract and author summary, Vancouver-style references, the mandatory data availability policy, ethics and financial disclosure statements, figure requirements (PACE), the PLOS LaTeX template, reporting guidelines and PLOS ONE's publication criteria. Use when targeting any PLOS journal or checking compliance with PLOS policies.
Use Monarch Money CLI for personal finance account balances, tags, transactions, cashflow summaries, explicit transaction CRUD workflows, and guarded read-only GraphQL queries. Use when the user asks about Monarch Money data, recent spending, tagged transactions, account balances, cashflow, or transaction cleanup from a terminal or agent workflow.
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
Skill dedicated to a wall construction pitfalls guide. Covers emulsion paint and putty, wallpaper and wall panels, microcement and artistic paint, substrate treatment and leveling, cracking, hollowing and mold, wall storage and utilization, and overall wall topics across the full process, integrating the highlights of the Xi'an Renovation Classroom member knowledge base in reverse order to help users avoid pitfalls in wall construction with less hassle.
Covers the RWKV (Receptance Weighted Key Value) architecture, an RNN/Transformer hybrid with O(n) inference and no KV cache, including RWKV-7, its parallel GPT-mode training and sequential RNN-mode inference, state passing, fine-tuning with DeepSpeed, and CUDA kernel setup. Use when generating text token by token with constant memory, processing very long contexts of 100K+ tokens, fine-tuning an RWKV model, comparing RWKV memory and speed against Transformers, or debugging RWKV state handling, loading, or out-of-memory errors. Prefer a standard Transformer when peak accuracy matters more than memory, and Mamba for state-space models.
SAP ECC expert for automotive manufacturing operations. Covers Materials Management (MM), Production Planning (PP), Sales & Distribution (SD), Quality Management (QM), Plant Maintenance (PM), and Finance/Controlling (FI/CO). USE WHEN user says 'SAP', 'ECC', 'transaction code', 't-code', 'MRP', 'purchase order', 'production order', 'goods receipt', 'goods issue', 'material master', 'BOM', 'routing', 'work center', or needs help with SAP processes. Integrates with AutomotiveManufacturing and SupplyChain skills.
Builds, evaluates, and audits right-censored survival analysis workflows with scikit-survival (sksurv): Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs, plus nonparametric cumulative incidence for competing risks. Covers leakage-safe scikit-learn pipelines, nested CV, and censoring-aware metrics. Use when fitting survival models on time-to-event data, building structured outcome arrays, computing IPCW concordance, dynamic AUC, or Brier scores, estimating cause-specific cumulative incidence, or tuning models without leakage. Not for Fine-Gray regression, which scikit-survival does not provide.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
Apply trader Serenity's (@aleabitoreddit) AI/semiconductor supply-chain analytical lens to US-stock ideas and market judgment. Use this skill whenever evaluating a stock decision (buy / sell / hold / size); forming an outlook on any AI, semiconductor, optical/CPO, memory, power/grid, or neocloud name; mentioning any ticker in Serenity's universe (NBIS, AXTI, LITE, SIVE, COHR, AAOI, IREN, CRWV, MU, SNDK, NVDA, TSM, MRVL, AVGO, INTC, SOI, IQE, TSEM, CIFR, XLU, VST, CEG, EWY, etc.); asking "what would Serenity think", "is this a real bottleneck", or wanting a supply-chain / bottleneck read on a thesis. Decision-support only — never auto-trades and never places or cancels orders.
Settle disputes, resolve conflicts, mediate fights, negotiate agreements, run debates, and arbitrate disagreements between any parties — humans, AI agents, teams, roommates, co-founders, colleagues, or organizations. Use this skill whenever the user has a complaint, grievance, argument, or disagreement with ANYONE or ANYTHING — including with you (the AI agent), with another person, with a company, with a coworker, with a roommate, with a partner, or with another AI. Also use this when the user wants to debate a topic, argue for or against a position, do a mock trial, play devil's advocate, have a structured argument, or roleplay an opposing side. Also use this when parties need to establish shared rules, contracts, household agreements, team norms, community guidelines, or any kind of binding or non-binding consensus. Covers: conflict resolution, dispute resolution, arbitration, mediation, negotiation, litigation alternative, fair division, binding rulings, relationship conflicts, workplace disputes, collaborative agreement-making, structured debates, mock trials, devil's advocate exercises, and adversarial discussions.
Configure settings on an existing Enhanced `MessagingChannel` of any type—WhatsApp, Text/SMS, LINE, Apple, Facebook, or EmbeddedMessaging—given its `{CHANNEL_ID}`. Covers five areas: consent (ConsentType and per-language keyword records), automated responses, messaging keywords, session inactivity timeouts, and pre-chat parameters. Consent and a matching language-keyword record are required for the activation readiness check. Trigger when setting or changing settings on an existing channel (opt-out keyword, consent type, double opt-in confirmation, initial/help auto-responses, OptIn/OptOut keywords, session inactivity timeout)—typically between insertion/routing and activation, or on a live channel. Do not trigger for creating a new channel (`service-de-channel-create`), deploying a MIAW/EmbeddedMessaging channel from metadata XML (`service-digital-engagement-channel-configure`), setting routing (`service-de-channel-routing-configure`), or activating a channel (`service-de-channel-activate`)
SHARK v3.0 — SM Conviction + Liquidation Cascade Hunter. Consolidated from v1.0's 8-cron pipeline into a single scanner. 4-gate entry: SM concentration (30+ traders, 5%+) → top 5 trader alignment → price momentum → funding structure. Score 8+ to enter. DSL manages all exits. No thesis exit. DSL exit managed by plugin runtime via runtime.yaml.
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
Format papers for Springer Lecture Notes in Computer Science (LNCS) and related proceedings series (LNAI, LNBI, CCIS) and Springer Nature journals using the llncs class or Springer Nature's sn-jnl template, covering page limits, abstract and keywords, splncs04 references, ORCID, running heads, camera-ready packages and the consent-to-publish form. Use when writing for conferences published in LNCS (e.g. ECCV, MICCAI, ESWC, many workshops), preparing an LNCS camera-ready, or targeting Springer Nature journals.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy algorithm experimentation, or scalable training with Monarch and TorchTitan.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.