Plugins
10 plugins@auto-skiller
Content Production
Content Production from Auto-Skiller/plugboot.
2 skills · plugin
curated
ML Model Lifecycle
Train, evaluate, and deploy a production ML system with monitoring.
10 skills · plugin
curated
AI Video Production
For creators producing AI-generated videos with avatars, lipsync, and voiceover.
1 skills · plugin
@cjthompson
Python Development
Deep Python production guidance for testing, project tooling, concurrency, and type-system work.
6 skills · plugin
@cjthompson
Typescript Development
Deep TypeScript production guidance for testing, tooling, modules, packaging, and type-system work.
6 skills · plugin
curated
Ship Production Deployment
Sets up CI/CD pipeline, deploys with staged rollout, configures observability, and enforces safety checks.
5 skills · plugin
curated
Safe Production Deployment
Deploy a web application safely with pre-deployment audit, rollout plan, canary monitoring, and rollback strategy.
9 skills · plugin
curated
Backend Framework Patterns
For developers building production-grade backends with NestJS, Spring Boot, or Ktor, covering architecture patterns and best practices.
7 skills · plugin
curated
Build RAG Pipeline with Pinecone
Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.
6 skills · plugin
curated
Build Agent with LangGraph
Build production-grade stateful AI agents using LangGraph, covering graph construction, state management, persistence, and human-in-the-loop patterns.
9 skills · plugin
Results for “production”
16 skills
neuralblitz Tensorflow
Build and deploy machine learning models with TensorFlow, covering Keras, data pipelines, and production serving.
1
orchestra-research Mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.
10.4k · bundle
lingxling Shap
Explains machine learning model predictions using SHAP values, covering feature importance, visualizations, debugging, bias analysis, and production deployment.
253 · bundle
oyi77 LLM Deployment
Deploy and serve LLMs in production with vLLM, Ollama, TGI, and llama.cpp, including quantization and GPU optimization.
10
jorcan LLM Ops
Guides production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and AI architectures.
0 · bundle
mhassan0000 Mle Workflow
Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
1
More results
qhjqhj00 Shap
Explains machine learning model predictions using SHAP values, covering feature importance, visualization plots, model debugging, bias analysis, and production deployment.
3 · bundle
antigravity Ml Engineer
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.
42.4k
neuralblitz Llmops
Manages the lifecycle of large language models in production, covering model versioning, prompt management, inference optimization, and cost control.
1
affaan-m Mle Workflow
Turn model work into a production ML system with data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.
226k
antigravity Julia Pro
Provides expert guidance on modern Julia 1.10+ development, covering performance optimization, multiple dispatch, tooling, testing, and production-ready practices.
42.4k
sakamoto-family-smile Mle Workflow
Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.
0
phoroth LLM Ops
Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.
3
jeffallan Ml Pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking, creates orchestration DAGs, builds feature store schemas, deploys model registries, and automates retraining and validation workflows.
10.4k · bundle
orchestra-research Model Merging
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining, covering SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.
10.4k · bundle
orchestra-research Serving Llms Vllm
Deploy and serve LLMs with high throughput using vLLM's PagedAttention and continuous batching. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism for production inference.
10.4k · bundle