ML Ops
Machine learning operations
faion-ml-ops
by faionfaion
"ML operations: fine-tuning (LoRA, QLoRA), model evaluation, cost optimization, observability."
ml-engineer
by k1lgor
Use this for building machine learning models, feature engineering, training pipelines, and integrating predictions into applications.
Comunicador Documentación
by scaleto
Enlace A2A del Grupo Documentación.
Comunicador Seguridad
by scaleto
Enlace A2A del Grupo Seguridad.
Comunicador Marketing
by scaleto
Enlace A2A del Grupo Marketing.
ml-engineering
by eyadsibai
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"
ai-sdk-6-skills
by gocallum
AI SDK 6 Beta overview, agents, tool approval, Groq (Llama), and Vercel AI Gateway. Key breaking changes from v5 and new patterns.
litellm
by BbgnsurfTech
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
deepchem
by hxk622
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
scvi-tools
by hxk622
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
excel-lbo-modeler
by Jst-Well-Dan
Build leveraged buyout (LBO) models in Excel with debt schedules and IRR analysis. Use when structuring LBO transactions or analyzing PE returns. Trigger with phrases like 'excel lbo', 'build lbo model', 'calculate pe returns'.
arboreto
by hxk622
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
geniml
by hxk622
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
excel-dcf-modeler
by Jst-Well-Dan
Build discounted cash flow (DCF) valuation models in Excel. Use when creating DCF models, calculating enterprise value, or valuing companies. Trigger with phrases like 'excel dcf', 'build dcf model', 'calculate enterprise value'.
pict-test-designer
by Jst-Well-Dan
Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT models with parameters, values, and constraints for valid scenarios using pairwise testing. Outputs the PICT model, markdown table of test cases, and expected results.
esm
by hxk622
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
Apple Foundation Models
by Eyadkelleh
Use this skill when working with Apple's Foundation Models framework for on-device AI and LLM capabilities in iOS/macOS apps
design-system-foundations
by KentoShimizu
"Define scalable design-system foundations with clear ownership and adoption boundaries. Use when multiple teams need shared component standards, foundational patterns, and ownership rules to deliver consistent UI across products; do not use for backend data-model or deployment pipeline decisions."
runpod-serverless-builder
by AvivK5498
Build production-ready RunPod serverless endpoints with optimized cold start times. Use when creating or modifying RunPod serverless workers for (1) vLLM-based LLM inference, (2) ComfyUI image/video generation, or (3) custom Python inference. Supports both baked models (fastest cold starts) and dynamic loading (shared models). Generates complete projects including Dockerfiles, worker handlers, startup scripts, and configuration optimized for minimal cold start latency.
agentform
by AvivK5498
Create and debug Agentform AI agent configurations (.af files). Use when: (1) Creating new agentform projects or workflows (2) Debugging agentform syntax errors (3) Adding MCP server integrations (4) Configuring agents, models, policies, or capabilities (5) Writing workflow steps with routing and human approval Agentform is "Infrastructure as Code for AI agents" - declarative .af files define agents, workflows, and policies.
architecture-tradeoff-analysis
by KentoShimizu
"Architecture trade-off analysis workflow for comparing options with explicit criteria, weighting, and sensitivity under uncertainty. Use when multiple viable architecture choices exist and rationale must be defensible; do not use after architecture direction is already fixed."
ai-engineer
by ngxtm
"Build production-ready LLM applications, advanced RAG systems, and"
architecture-ddd
by KentoShimizu
"Domain-driven design workflow for bounded context partitioning, aggregate design, and context mapping in complex domains. Use when domain complexity or organizational scaling requires explicit model boundaries; do not use for small CRUD domains without competing language models."
3d-web-experience
by ngxtm
"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 ..."