ML Ops
Machine learning operations
dotfile-audit
by harperreed
Use when you have an existing pipeline DOT file that needs review, fixing, or validation. Use when validation produces warnings or errors, when a DOT file was hand-written or generated by another tool, or when you want to verify a pipeline covers 100% of a spec before running it.
Hugging Face
by imoonkey
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jk
by avivsinai
Jenkins CLI for controllers. Use when users need to manage jobs, pipelines, runs, logs, artifacts, credentials, nodes, or queues in Jenkins. Triggers include "jenkins", "jk", "pipeline", "build", "run logs", "job list", "jenkins credentials", "jenkins node".
convert-elm-clojure
by aRustyDev
Convert Elm code to idiomatic Clojure. Use when migrating Elm frontend applications to Clojure/ClojureScript, translating The Elm Architecture to Clojure patterns, or refactoring type-safe functional code to dynamic functional style. Extends meta-convert-dev with Elm-to-Clojure specific patterns.
convert-elm-scala
by aRustyDev
Bidirectional conversion between Elm and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Scala specific patterns. Use when migrating Elm frontend applications to Scala backends or full-stack Scala, translating The Elm Architecture to functional Scala patterns, or refactoring type-safe functional code from compile-time guarantees to more powerful type system features. Extends meta-convert-dev with Elm-to-Scala specific patterns.
visionos-design-guidelines
by ehmo
Apple Human Interface Guidelines for Apple Vision Pro. Use when building spatial computing apps, implementing eye/hand input, or designing immersive experiences. Triggers on tasks involving visionOS, RealityKit, spatial UI, or mixed reality.
MLOps Industrialization
by fmind
Guide to transform prototypes into robust, distributable Python packages using the src layout, hybrid paradigm, and strict configuration management.
MLOps Initialization
by fmind
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
MLOps Observability
by fmind
Guide to implement full stack observability including reproducibility, lineage, monitoring, alerting, and explainability.
MLOps Prototyping
by fmind
Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.
MLOps Validation
by fmind
Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.
mlflow-onboarding
by mlflow
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".
openscad
by mitsuhiko
"Create and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing platforms like MakerWorld."
dart-io
by dartsim
DART model loading - URDF, SDF, MJCF, SKEL parsers and dart::io unified API
pydantic-ai-common-pitfalls
by existential-birds
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
concise
by forcedotcom
when creating/modifying md files in skills/rules for AI
python
by cosmix
Python language expertise for writing idiomatic, production-quality Python code. Covers web frameworks (FastAPI, Django, Flask), data processing (pandas, numpy, dask), ML patterns (sklearn, pytorch), async programming, type hints, testing with pytest, packaging (pip, uv, poetry), linting (ruff, mypy, black), and PEP 8 standards. Use for any Python development including data engineering and machine learning workflows. Triggers: python, py, pip, uv, poetry, virtualenv, pytest, pydantic, fastapi, django, flask, pandas, numpy, dataclass, type hints, asyncio, mypy, ruff, black, sklearn, pytorch, tensorflow, jupyter, pipenv, conda.
ci-cd
by cosmix
Designs and implements CI/CD pipelines for automated testing, building, deployment, and security scanning across multiple platforms. Covers pipeline optimization, test integration, artifact management, and release automation.
Classification Modeling
by aj-geddes
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
project-development
by guanyang
This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.
context-fundamentals
by guanyang
This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.
context-degradation
by guanyang
This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. Provides patterns for recognizing and mitigating context failures.
advanced-evaluation
by guanyang
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
model-evaluation
by cosmix
Evaluates machine learning models for performance, fairness, and reliability using appropriate metrics and validation techniques. Covers training debugging, hyperparameter tuning, and production monitoring. Trigger keywords: model evaluation, metrics, accuracy, precision, recall, F1, F1-score, ROC, AUC, ROC-AUC, confusion matrix, cross-validation, k-fold, stratified, overfitting, underfitting, bias, variance, bias-variance tradeoff, hyperparameter, hyperparameter tuning, loss, loss function, metric, benchmark, benchmarking, model performance, classification metrics, regression metrics, RMSE, MSE, MAE, MAPE, R2, R-squared, train-test split, validation set, test set, hold-out, learning curve, validation curve, model selection, error analysis, residual analysis, ML testing, training issues, convergence, gradient, vanishing gradient, exploding gradient, training instability, LLM evaluation, language model evaluation, prompt engineering evaluation, A/B testing, champion-challenger, model monitoring, model drift, data drift, concept drift, model decay.