Results for “bed”
8 skills
itsmostafa bedrock
Access AWS Bedrock foundation models for generative AI, including text generation, embeddings, and image generation, with CLI and Python examples.
1.1k · bundle
k-dense-ai polars-bio
Perform high-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames, including overlap, nearest, merge, coverage, complement, subtract, and reading/writing BED, VCF, BAM, GFF, FASTA, and FASTQ formats with streaming and cloud-native support.
30.2k · bundle
github arize-ai-provider-integration
Manage Arize AI integrations that store LLM provider credentials for evaluators and other features. Supports creating, reading, updating, and deleting integrations with providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, and NVIDIA NIM.
36.2k · bundle
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huggingface hf-cloud-sagemaker-deployment-planner
Plans and coordinates the deployment of a model to Amazon SageMaker AI, selecting the appropriate pathway (real-time, serverless, async, batch, or Bedrock CMI) based on model type, traffic, latency, and cost constraints.
10.8k
muratcankoylan hosted-agents
Design and build hosted agent infrastructure with sandboxed execution, warm pools, session persistence, and multiplayer collaboration.
16.9k · bundle
nimoqup046-collab loki-mode
Runs an autonomous multi-agent software development pipeline that takes a PRD through to production with zero human intervention, using model-tiered agents, memory, and verification cycles.
2 · bundle
akillness opik
Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.
42 · bundle
alterlab-ieu alterlab-eda
Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its structure/content/quality before deciding what analysis to run. Covers tabular (.csv .tsv .xlsx .parquet), arrays (.npy .npz .hdf5 .h5 .mat .fits), sequence/genomics (.fasta .fastq .sam .bam .vcf .bed .gff .gtf .h5ad), microscopy (.tif .nd2 .czi .lif .ims .dcm .nii), spectroscopy/MS (.mzML .mzXML .mgf .fid .jdx), chemistry (.pdb .cif .mol .sdf .xyz .gro), and proteomics/metabolomics (.pepXML .mzid .mzTab). For zero-shot forecasting of a series use alterlab-timesfm; to create/configure a chunked cloud array store use alterlab-zarr. Part of the AlterLab Academic Skills suite.
60 · bundle