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Claude Skills by pauldx
github.com/pauldx3,929 skills0 installs0 views
- Cf Kpi Dashboard TemplateManage kpi dashboard template operations. Auto-activating skill for Enterprise Workflows. Triggers on: kpi dashboard template, kpi dashboard template Part of the Enterprise Workflows skill category. Use when working with kpi dashboard template functionality. Trigger with phrases like "kpi dashboard template", "kpi template", "kpi".Votes: 0GitHub stars: 2
- Cf Linear Issue GeneratorGenerate linear issue generator operations. Auto-activating skill for Enterprise Workflows. Triggers on: linear issue generator, linear issue generator Part of the Enterprise Workflows skill category. Use when working with linear issue generator functionality. Trigger with phrases like "linear issue generator", "linear generator", "linear".Votes: 0GitHub stars: 2
- Cf Okr Tracker CreatorCreate okr tracker creator operations. Auto-activating skill for Enterprise Workflows. Triggers on: okr tracker creator, okr tracker creator Part of the Enterprise Workflows skill category. Use when working with okr tracker creator functionality. Trigger with phrases like "okr tracker creator", "okr creator", "okr".Votes: 0GitHub stars: 2
- Cf Risk Assessment CreatorCreate risk assessment creator operations. Auto-activating skill for Enterprise Workflows. Triggers on: risk assessment creator, risk assessment creator Part of the Enterprise Workflows skill category. Use when working with risk assessment creator functionality. Trigger with phrases like "risk assessment creator", "risk creator", "risk".Votes: 0GitHub stars: 2
- Cf Roadmap GeneratorGenerate roadmap generator operations. Auto-activating skill for Enterprise Workflows. Triggers on: roadmap generator, roadmap generator Part of the Enterprise Workflows skill category. Use when working with roadmap generator functionality. Trigger with phrases like "roadmap generator", "roadmap generator", "roadmap".Votes: 0GitHub stars: 2
- Cf Sla Monitor SetupConfigure sla monitor setup operations. Auto-activating skill for Enterprise Workflows. Triggers on: sla monitor setup, sla monitor setup Part of the Enterprise Workflows skill category. Use when monitoring systems or services. Trigger with phrases like "sla monitor setup", "sla setup", "sla".Votes: 0GitHub stars: 2
- Cf Sprint Planning HelperConfigure with sprint planning helper operations. Auto-activating skill for Enterprise Workflows. Triggers on: sprint planning helper, sprint planning helper Part of the Enterprise Workflows skill category. Use when working with sprint planning helper functionality. Trigger with phrases like "sprint planning helper", "sprint helper", "sprint".Votes: 0GitHub stars: 2
- Cf Stakeholder Communication TemplateManage stakeholder communication template operations. Auto-activating skill for Enterprise Workflows. Triggers on: stakeholder communication template, stakeholder communication template Part of the Enterprise Workflows skill category. Use when working with stakeholder communication template functionality. Trigger with phrases like "stakeholder communication template", "stakeholder template", "stakeholder".Votes: 0GitHub stars: 2
- Cf Status Report GeneratorGenerate status report generator operations. Auto-activating skill for Enterprise Workflows. Triggers on: status report generator, status report generator Part of the Enterprise Workflows skill category. Use when working with status report generator functionality. Trigger with phrases like "status report generator", "status generator", "status".Votes: 0GitHub stars: 2
- Cf User Story GeneratorGenerate user story generator operations. Auto-activating skill for Enterprise Workflows. Triggers on: user story generator, user story generator Part of the Enterprise Workflows skill category. Use when working with user story generator functionality. Trigger with phrases like "user story generator", "user generator", "user".Votes: 0GitHub stars: 2
- Agent Memory McpA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).Votes: 0GitHub stars: 2
- Agents AutogptAutonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.Votes: 0GitHub stars: 2
- Agents CrewaiMulti-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.Votes: 0GitHub stars: 2
- Agents LangchainFramework 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.Votes: 0GitHub stars: 2
- Agents LlamaindexData framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.Votes: 0GitHub stars: 2
- Data Processing Nemo CuratorGPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.Votes: 0GitHub stars: 2
- Data Processing Ray DataScalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.Votes: 0GitHub stars: 2
- Distributed Training AccelerateSimplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.Votes: 0GitHub stars: 2
- Distributed Training DeepspeedExpert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attentionVotes: 0GitHub stars: 2
- Distributed Training Megatron CoreTrains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.Votes: 0GitHub stars: 2
- Distributed Training Pytorch FsdpExpert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2Votes: 0GitHub stars: 2
- Distributed Training Pytorch LightningHigh-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.Votes: 0GitHub stars: 2
- Distributed Training Ray TrainDistributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.Votes: 0GitHub stars: 2
- Emerging Techniques Knowledge DistillationCompress 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.Votes: 0GitHub stars: 2
- Emerging Techniques Long ContextExtend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.Votes: 0GitHub stars: 2
- Emerging Techniques Model MergingMerge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.Votes: 0GitHub stars: 2
- Emerging Techniques Model PruningReduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.Votes: 0GitHub stars: 2
- Emerging Techniques Moe TrainingTrain Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.Votes: 0GitHub stars: 2
- Emerging Techniques Speculative DecodingAccelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.Votes: 0GitHub stars: 2
- Evaluation Bigcode Evaluation HarnessEvaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.Votes: 0GitHub stars: 2
- Evaluation Lm Evaluation HarnessEvaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.Votes: 0GitHub stars: 2
- Evaluation Nemo EvaluatorEvaluates 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.Votes: 0GitHub stars: 2
- Fine Tuning AxolotlExpert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal supportVotes: 0GitHub stars: 2
- Fine Tuning Llama FactoryExpert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal supportVotes: 0GitHub stars: 2
- Fine Tuning PeftParameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.Votes: 0GitHub stars: 2
- Fine Tuning UnslothExpert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimizationVotes: 0GitHub stars: 2
- Inference Serving Llama CppRuns 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.Votes: 0GitHub stars: 2
- Inference Serving SglangFast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.Votes: 0GitHub stars: 2
- Inference Serving Tensorrt LlmOptimizes 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.Votes: 0GitHub stars: 2
- Inference Serving VllmServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.Votes: 0GitHub stars: 2
- Infrastructure Lambda LabsReserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.Votes: 0GitHub stars: 2
- Infrastructure ModalServerless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.Votes: 0GitHub stars: 2
- Infrastructure SkypilotMulti-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.Votes: 0GitHub stars: 2
- Llm OpsLLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.Votes: 0GitHub stars: 2
- Mechanistic Interpretability NnsightProvides 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.Votes: 0GitHub stars: 2
- Mechanistic Interpretability PyveneProvides 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.Votes: 0GitHub stars: 2
- Mechanistic Interpretability SaelensProvides 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.Votes: 0GitHub stars: 2
- Mechanistic Interpretability Transformer LensProvides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.Votes: 0GitHub stars: 2
- Ml Paper WritingWrite publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.Votes: 0GitHub stars: 2
- Mlops MlflowTrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platformVotes: 0GitHub stars: 2