ML Ops
Machine learning operations
frontend-razor
by twofoldtech-dakota
Apply when working with Razor views, MVC layouts, partial views, and tag helpers
fine-tuning
by pluginagentmarketplace
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
ai-model-cascade
by vishalsachdev
A production-ready pattern for integrating AI models (specifically Google Gemini) with automatic fallback, retry logic, structured output via Zod schemas, and comprehensive error handling. Use when integrating AI/LLM APIs, need automatic fallback when models are overloaded, want type-safe structured responses, or building features requiring reliable AI generation.
codex-cli
by rafaelcalleja
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
product-tryon-visualization
by tara-shopos
Generate realistic product try-on visualizations showing how garments, accessories, or products look when worn or used. Use for virtual try-on experiences, fit visualization, and helping customers see products in use before purchase.
自运行量化交易系统（Machine Learning Driven Quant Trading System）
by 1WangToby
重大更新： skill_finance_analysis 作为 submodule 引入到 tools/tool_finance_analysis，ZIP 安装自动包含工具库，无需额外克隆
east-py-datascience
by elaraai
"Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy, Optimization, GoogleOr), machine learning (XGBoost, LightGBM, NGBoost, Torch MLP, Lightning, GP), Bayesian inference (PyMC), simulation (Simulation DES), ML utilities (Sklearn preprocessing, metrics, splits), conformal prediction (MAPIE), or model explainability (SHAP). Triggers for: (1) Writing East programs with @elaraai/east-py-datascience, (2) Derivative-free optimization with MADS, (3) Bayesian optimization with Optuna, (4) Discrete/combinatorial optimization with SimAnneal, (5) Gradient boosting with XGBoost or LightGBM, (6) Probabilistic predictions with NGBoost or GP, (7) Neural networks with Torch MLP or Lightning, (8) Data preprocessing and metrics with Sklearn, (9) Conformal prediction intervals with MAPIE, (10) Model explainability with Shap, (11) Iterative coordinate descent with Optimization, (12) Constraint programming, vehicle routing, LP/MIP, or graph algorithms with GoogleOr, (13) Bayesian regression, hierarchical models, and multi-layer estimation with PyMC, (14) Economic ontology simulation via discrete event simulation with Simulation."
aws-bedrock-evals
by antstackio
Build and run LLM-as-judge evaluation pipelines using Amazon Bedrock Evaluation Jobs with pre-computed inference datasets. Use when setting up automated model evaluation, designing test scenarios, collecting pre-computed responses, configuring custom metrics, creating AWS infrastructure, running evaluation jobs, parsing results, and iterating on findings.
claude-command-example-plugin-example-command
by monkey1sai
Converted from Claude plugin command "example-command" (example-plugin).
subagent-configuration
by rafaelcalleja
This skill should be used when the user asks to "configure a subagent", "create a custom agent", "set up agent tools", "configure permissionMode", "add agent skills", or mentions subagent fields like tools, model, or permissionMode. Provides comprehensive guidance on subagent configuration options and best practices.
XState v5 Expert Skill
by boneskull
Expert guidance for implementing and analyzing XState v5 state machines with TypeScript support and best practices
kgz — Execute Code on Kaggle Kernels (GPU & TPU)
by mlnomadpy
Dep: websocket-client
huggingface-daemon
by fast-gateway-protocol
Fast Hugging Face Inference API via FGP daemon. Use when user needs text generation, embeddings, classification, image captioning, or model inference. Triggers on "huggingface inference", "generate text", "get embeddings", "classify text", "hugging face", "HF model", "zero-shot".
embedding-comparison
by mindmorass
Compare and evaluate embedding models for semantic search
redux-saga
by anivar
Redux-Saga best practices, patterns, and API guidance for building, testing, and debugging generator-based side-effect middleware in Redux applications. Covers effect creators, fork model, channels, testing with redux-saga-test-plan, concurrency, cancellation, and modern Redux Toolkit integration. Baseline: redux-saga 1.4.2. Triggers on: saga files, redux-saga imports, generator-based middleware, mentions of "saga", "takeEvery", "takeLatest", "fork model", or "channels".
finance-forecast
by jforksy
Detailed financial scenario modeling, revenue projections, and burn rate analysis
explaining
by AlexanderStephenThompson
Repeatable execution process for producing clear explanations. Covers Subject and Situational frameworks, depth scaling, and relatability tools.
evaluation-metrics
by pluginagentmarketplace
LLM evaluation frameworks, benchmarks, and quality metrics for production systems.
agentscope
by changxubo
Build production-ready multi-agent systems with AgentScope framework. Use this skill when users want to create AI agents, multi-agent workflows, or distributed agent applications. Provides patterns for ReActAgent, MsgHub, Pipelines, MCP integration, memory management, and fault-tolerant agent systems.
fashion-model-photography
by tara-shopos
Generate professional fashion photography with models wearing products, including pose direction, styling, lighting, and composition for editorial and commercial use. Use for garment shoots, accessory photography, and high-end fashion content.
garment-lifestyle-photography
by tara-shopos
Generate lifestyle photography of garments in real-world contexts with models in natural environments. Use when creating aspirational fashion content, social media imagery, or editorial-style product photography that shows garments in authentic lifestyle settings.
umbraco-development
by twofoldtech-dakota
Apply when working with Umbraco CMS, Composers, services, or content APIs
llm-basics
by pluginagentmarketplace
LLM architecture, tokenization, transformers, and inference optimization. Use for understanding and working with language models.
implement-paper-from-scratch
by GhostScientist
Guides you through implementing a research paper step-by-step from scratch. Use when asked to implement a paper, code up a paper, reproduce research results, or build a model from a paper. Focuses on building understanding through implementation with checkpoint questions.