DevOps & Cloud

Deployment, cloud, containers, CI/CD, monitoring, and infrastructure

skills in category: 20,326
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Showing 8,833–8,856 of 20,326 skills

SentencepieceALanguage-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.devopspythongoVotes: 0GitHub stars: 11
Run Acceptance TestsAGuide for running acceptance tests for a Terraform provider. Use this when asked to run an acceptance test or to run a test with the prefix `TestAcc`.devopsgoterraformVotes: 0GitHub stars: 11
Refactor ModuleATransform monolithic Terraform configurations into reusable, maintainable modules following HashiCorp's module design principles and community best practices.devopsbashawsVotes: 0GitHub stars: 11
ReadmeBWhen the user wants to create or update a README.md file for a project. Also use when the user says "write readme," "create readme," "document this project," "project documentation," or asks for help with README.md. This skill creates absurdly thorough documentation covering local setup, architecture, and deployment.devopsjavascripttypescriptVotes: 0GitHub stars: 11
QdrantAHigh-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.devopspythonrustVotes: 0GitHub stars: 11
Push To RegistryAPush Packer build metadata to HCP Packer registry for tracking and managing image lifecycle. Use when integrating Packer builds with HCP Packer for version control and governance.devopsgoshellVotes: 0GitHub stars: 11
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Project ToolingBgh, vercel, supabase, render CLI and deployment platform setupdevopstypescriptpythonVotes: 0GitHub stars: 11
PineconeAManaged vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.devopspythongoVotes: 0GitHub stars: 11
PhoenixAOpen-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.devopspythongoVotes: 0GitHub stars: 11
OpenrlhfAHigh-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.devopspythongoVotes: 0GitHub stars: 11
Oc Aisa Market SkillAQuery real-time and historical financial data across equities and crypto—prices, market moves, metrics, and trends.devopsawsdocumentationVotes: 0GitHub stars: 11
Oc Aisa Financial DataAQuery real-time and historical financial data across equities and crypto—prices, market moves, metrics, and trends.devopsawsdocumentationVotes: 0GitHub stars: 11
Oc Aisa Financial Data ApiAQuery real-time and historical financial data across equities and crypto—prices, market moves, metrics, and trends.devopsawsapiVotes: 0GitHub stars: 11
Nemo EvaluatorAEvaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.devopspythongoVotes: 0GitHub stars: 11
Nemo CuratorAGPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.devopspythongoVotes: 0GitHub stars: 11
ModalAServerless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.devopspythonrustVotes: 0GitHub stars: 11
MlflowATrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platformdevopspythongoVotes: 0GitHub stars: 11
MilesAProvides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.devopspythongoVotes: 0GitHub stars: 11
Long ContextAExtend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.devopspythongoVotes: 0GitHub stars: 11
Llama CppARuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.devopspythongoVotes: 0GitHub stars: 11
Latchbio IntegrationALatch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.devopspythongoVotes: 0GitHub stars: 11
LamindbAThis skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, disea...devopspythongoVotes: 0GitHub stars: 11
Knowledge DistillationACompress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.devopspythongoVotes: 0GitHub stars: 11
Ginkgo Cloud LabASubmit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run cell-free protein expression (validation or optimization), generate fluorescent pixel art, or interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.devopsgoexpressVotes: 0GitHub stars: 11