ML Ops
Machine learning operations
domain-layer
by Nomik94
Domain Layer 설계 및 구현 가이드. Use when: Entity/Value Object/Aggregate Root 설계, 도메인 이벤트 구현, 비즈니스 로직 배치 판단, Repository Protocol 정의, Domain vs Application Service 구분, 상태 전이 로직, 도메인 예외 설계, 서비스 비대화 해결, 로직 분리. NOT for: 단순 CRUD (비즈니스 규칙 없으면 domain layer 불필요), SQLAlchemy 모델 작성, 단순 dataclass 문법.
cohere-java-sdk
by RSHVR
Cohere Java SDK reference for chat, streaming, embeddings, reranking, and tool use. Use when building Java/Kotlin applications with Cohere APIs.
pricing-strategy
by jamelna-apps
When the user mentions "pricing", "price", "monetization", "subscription", "tiers", "freemium", "revenue model", or asks about how to price a product or service.
SKILL: Документирование PuzzleAI
by marozz1k2
документация docs.pxsto.re
deepagent
by htooayelwinict
Expert guidance for DeepAgents framework - simplified agent creation with tool integration for LangChain/LangGraph workflows.
Gradient Checkpointing (Activation Recomputation)
by sovr610
This skill should be used when the user asks to "enable gradient checkpointing", "reduce training memory", "activation checkpointing", "torch.utils.checkpoint", "memory-compute tradeoff", "checkpoint sequential layers", "selective checkpointing", "recomputation strategy", "activation memory profiling", "per-layer memory budget", "checkpoint_sequential", "checkpoint_wrapper", "SAC selective activation checkpointing", "SNN timestep checkpoint", "FSDP activation checkpointing", "checkpoint per timestep", "memory-efficient training", "recompute activations in backward", or needs guidance on trading compute for memory during training, per-layer memory profiling, selective recomputation strategies, or integration with distributed training wrappers.
concept-cartographer
by PrakharMNNIT
"Generate visual concept maps, flowcharts, architecture diagrams, and relationship diagrams from structured notes or technical content using Mermaid syntax. Use when the user has lecture notes, study materials, or technical documentation and wants visual diagrams to aid understanding. Produces multiple diagram types: concept hierarchy maps, process flowcharts, architecture diagrams, comparison matrices, timeline diagrams, and mind maps. Trigger phrases: 'create diagrams from notes', 'visualize concepts', 'concept map', 'make flowcharts', 'diagram this', 'visual notes'."
cerebras-api
by diskd-ai
Cerebras API integration for building AI-powered applications with ultra-fast LLM inference. Use when working with Cerebras's Chat Completions API, Python SDK (cerebras_cloud_sdk), TypeScript SDK (@cerebras/cerebras_cloud_sdk), tool use/function calling, structured outputs with JSON schemas, reasoning models with thinking tokens, streaming responses, or any Cerebras API integration task. Triggers on mentions of Cerebras, Cerebras Inference, Llama on Cerebras, Qwen on Cerebras, GLM, or fast LLM inference needs.
data-science-notebooks
by legout
"Interactive notebooks for data science: Jupyter, JupyterLab, and marimo. Use for exploratory analysis, reproducible research, documentation, and sharing insights with stakeholders."
hreng-skills
by clous-ai
Use when the user wants to assess engineering team skills, build a skills matrix, identify gaps vs. roadmap, and design training or hiring plans.
transcript-pipeline
by PrakharMNNIT
This skill should be used when the user asks to "process this transcript", "convert lecture to notes", "run transcript pipeline", "generate class tutorial from Zoom captions", "validate transcript coverage", or "enrich class resources" (Notion/Canva/Drive links) for bootcamp notes.
plan
by pmco23
Use after /review to transform the approved design into an atomic execution plan. Writes task groups with exact file paths, complete code examples, and named test cases with assertions. Build agents must never need to ask clarifying questions. Writes .pipeline/plan.md.
eval-frameworks
by cuba6112
Evaluation framework patterns for RAG and LLMs, including faithfulness metrics, synthetic dataset generation, and LLM-as-a-judge patterns. Triggers: ragas, deepeval, llm-eval, faithfulness, hallucination-check, synthetic-data.
pipeline-pattern-react
by progmichaelkibenko
Implements the Pipeline design pattern in React for data transformation. Use when the user mentions pipeline pattern, or when you need a fixed sequence of stages that each transform data and pass to the next—ETL-style processing in the UI, parsing, formatting pipelines, or any linear transformation flow that runs to completion.
ecostral-optimizer
by wilfred-dore
Optimize any HuggingFace model to minimize energy consumption, CO₂ emissions, and inference cost using real GPU measurements and Mistral Large reasoning. Use this skill when asked to quantize a model, reduce its carbon footprint, benchmark deployment configurations (datacenter GPU, Jetson Orin, edge devices), or generate an optimization report with W&B experiment tracking.
data-science-interactive-apps
by legout
"Interactive web apps for data science: Streamlit, Panel, and Gradio. Use for prototyping ML models, creating data exploration dashboards, and sharing insights with non-technical stakeholders."
douyin-downloader
by MgOxides
Use when needing to download and transcribe Douyin/抖音 videos to text. Supports single video URLs (/video/xxx) and user profile URLs (/user/xxx) with auto-detection. Triggers on Douyin links, 抖音 transcription requests, Chinese short video audio extraction, or batch video downloading.
django-model
by jprokay-counterpart
Creating Django models following Counterpart's patterns. Use when building new models, working with audit fields, implementing relationships, or using PydanticJSONEncoder. Triggers: 'create model', 'new database table', 'audit fields', 'BaseModel inheritance', 'add foreign key', 'JSON field validation'
3d-web-experience
by ncdevshiv
"Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ..."
Data Loader Throughput + Sequence Packing
by sovr610
This skill should be used when the user asks to "audit dataloader throughput", "measure data pipeline stalls", "implement sequence packing", "reduce padding waste in training", "add cu_seqlens packing for SFT", "boundary-aware packing FlashAttention", "DataCollatorWithFlattening", "configure DataLoader num_workers prefetch_factor", "tune DataLoader settings", "streaming dataset pipeline", "WebDataset tar shards", "memmap pretraining dataset", "HF datasets streaming IterableDataset", "deterministic sharding per rank", "DistributedSampler set_epoch", "padding ratio metrics", "effective tokens per second", "pretraining block builder", "data stall ratio measurement", "persistent_workers pin_memory tuning", "shard cache policy", "packed sequences with position_ids reset", "varlen_attn cu_seqlens", or needs guidance on turning the input pipeline into a first-class performance target with measurement, streaming I/O, packing, and deterministic distributed sharding.
bg-remover
by yemyat
Remove backgrounds from images using FAL.ai's BiRefNet model. Use when users ask to remove background, make transparent PNG, extract subject from image, or create cutouts. Trigger phrases include "remove background", "transparent background", "cut out", "extract subject", or any background removal request.
hytopia-entities
by Abstrucked
Helps create and manage entities in HYTOPIA SDK games. Use when users need to create game objects, NPCs, items, or any interactive objects. Covers Entity class, spawn/despawn, components, animations, and lifecycle management.
ml-research
by Pranav-Karra-3301
Comprehensive skill for ML/AI research experiments and finetuning. Use when: (1) Setting up new ML research project ("create ML project", "init experiment") (2) Finetuning models ("finetune LLM", "adapt pretrained model", "LoRA", "QLoRA") (3) Training from scratch ("train model", "run experiment") (4) Debugging ML issues ("model not converging", "loss exploding", "GPU OOM") (5) Setting up experiment tracking ("add W&B", "setup MLflow") (6) Optimizing GPU usage ("batch size tuning", "memory optimization") (7) Creating visualizations ("plot training curves", "confusion matrix") (8) Auditing ML code ("check reproducibility", "review experiment") Triggers: "ML", "machine learning", "deep learning", "training", "finetuning", "PyTorch", "TensorFlow", "experiment", "GPU", "CUDA", "model", "neural network", "W&B", "MLflow", "reproducibility", "learning rate", "checkpoint", "epoch"
data-engineering-ai-ml
by legout
"AI/ML data pipelines: embedding generation, vector databases, RAG patterns, LLM monitoring, and batch inference workflows."