Loading...
Loading...
Found 1,839 Skills
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
Invoke the `groundcover` Go CLI to manage Groundcover resources (dashboards, monitors, silences, connected apps, notification routes, API keys, policies, integrations, pipelines, workflows) AND to answer production observability questions by querying logs, traces, metrics, k8s inventory, and k8s events. Use whenever a task needs an authenticated call against the Groundcover API or whenever the user is debugging a prod issue and asks things like "why is X erroring in prod", "show me logs for service Y", "what's the p99 latency on Z", "what pods are crashlooping", "search traces for slow requests", "any k8s events for namespace N", "is service S receiving traffic", "list groundcover monitors", "create a silence", "update notification route", "hit a groundcover endpoint". Covers required env vars, the SDK-backed vs raw command split, and concrete request-body templates for logs/traces/metrics/k8s so the CLI can be driven from anywhere.
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
Interview the operator and write the strategic layer of a product —vision, problem, users, scope, competitors, opportunities, open questions, glossary— into the `context/` of its node in the brain, never into the body repo. Manually triggered, last link of the discovery pipeline and the gate before specs.
Use when setting up CI/CD pipelines, containerizing applications, or managing infrastructure as code. Invoke for pipelines, Docker, Kubernetes, cloud platforms, GitOps.
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
Integrate automated testing into CI/CD pipelines for continuous quality feedback. Use for continuous testing, CI testing, automated testing pipelines, test orchestration, and DevOps quality practices.
OpenTelemetry observability patterns: traces, metrics, logs, context propagation, OTLP export, Collector pipelines, and troubleshooting
Add Vitest testing infrastructure and GitHub Actions CI/CD to any TypeScript project. Supports Next.js, NestJS, and React projects with 80% coverage thresholds. Use this skill when setting up tests for a new project or adding CI/CD pipelines.