Results for “data-lake-storage-gen2”

Create and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores like S3, GCS, Azure, HTTP, or HuggingFace.

Configures GKE storage including PVCs, PersistentVolumes, Filestore, and GCS FUSE with best practices for production workloads.

ORCA v2.0 — Gen-2 Striker with momentum event quality confirmation. FIRST_JUMP detection enhanced with Tier 2 momentum events and TCS trader quality tags. Stalker permanently removed. DSL exit managed by plugin runtime via runtime.yaml.

Provides guidance on enabling and optimizing GKE Cluster Autoscaler, including Node Auto Provisioning, troubleshooting scale-up/down issues, and best practices for capacity management.

Plans, configures, and manages GKE networking including private clusters, VPC-native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning.

Protects stateful GKE workloads by configuring backup plans, restore workflows, and disaster recovery using Backup for GKE.

Structured data + RAG. NL2SQL hybrid patterns (text-to-SQL then execute vs embed rows), table embedding strategies (row-level, schema-level, hybrid), semantic layer integration (Cube, dbt metrics), LangChain SQLDatabaseChain, LlamaIndex PandasQueryEngine, safe SQL execution (read-only, sandboxed), schema-aware retrieval. Full PostgreSQL + pgvector hybrid code. USE WHEN: user mentions "tabular RAG", "NL2SQL", "text to SQL", "RAG on tables", "database RAG", "SQL RAG", "semantic layer", "structured data RAG" DO NOT USE FOR: unstructured doc RAG - use `rag-architecture`; metadata filtering only - use `self-querying-retriever`; KG retrieval - use `graph-rag`

Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.

SUPERSEDED by persistent-cache-gap-filling (v2.8.0). Cache data during symbol selection for instant repeat runs.

Persists app data on Android using Room, SQLite, SharedPreferences, DataStore, and encryption.

Machine learning on genomic interval data (BED files) with the geniml Python package — region embeddings (Region2Vec), joint region+metadata embeddings (BEDspace/StarSpace), single-cell ATAC-seq embeddings (scEmbed), consensus peak sets / universes (build-universe), tokenization, BEDshift randomization, and BBClient/BEDbase caching. Use when training or using region/cell embeddings, clustering scATAC-seq, building a tokenization universe from BED collections, or any ML/feature-learning task over genomic regions. NOT for plain interval arithmetic (overlap/intersect/merge counts) — that is gtars, not geniml. Part of the AlterLab Academic Skills suite.

Build on-chain relational databases on Solana using IQ Labs' IQDB, hanLock, and x402 tools, including CRUD operations, tamper-evident rolling hashes, password-based encoding, and payment-gated file inscription.

Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.

Use when converting GenBank format files to table format as part of the Entrez Direct toolkit from bioconda.

Spark by Lightspark: statechain-derived L2 with FROST threshold signatures, leaf architecture, Lightning compatibility. Beta launched April 2025; managed by Spark Operators (SOs). USE WHEN: integrating with Spark, evaluating Spark vs Ark vs Lightning, designing leaf-based UTXO trees.

Use when converting GenBank flatfiles into XML for downstream EDirect or XML-based sequence annotation workflows.

Design and operate Azure Storage services for durability, performance, and secure access. Use this skill when users ask about Blob, Files, Queues, Tables, or Data Lake storage patterns. Covers redundancy, tiering, access models, lifecycle policies, and compliance.

Manage AWS EC2 virtual machines — instances, security groups, key pairs, AMIs, EBS volumes, Auto Scaling Groups, Spot Instances, Session Manager, placement groups, and instance lifecycle automation.

Generate typed SDK bindings (Pydantic v2 models, TypeScript types) from the Bloodbank JSON Schema tree at `bloodbank/schemas/bloodbank/v1/**` (Draft 2020-12). Uses `datamodel-code-generator` and `json-schema-to-typescript` — NOT the deprecated hand-rolled Holyfields generators. Use when generating event-contract bindings for a Bloodbank consumer, importing typed CloudEvents envelopes into another project, or scaffolding a `bloodbank-contracts` SDK package. Trigger keywords — "bloodbank SDK", "bloodbank contracts", "Pydantic from bloodbank schemas", "TypeScript types for bloodbank events", "bloodbank.v1.* types", "event contract bindings", "datamodel-code-generator", "json-schema-to-typescript", "regenerate SDK", "CloudEvents bindings". Do NOT use for schema authoring (edit `bloodbank/schemas/` directly per `docs/event-naming.md` §12), runtime envelope validation (use `BLOODBANK_HOOK_VALIDATE=1`), schema-tree consistency (`mise run smoketest:schemas`), or generic JSON Schema codegen unrelated to Bloodbank.

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

Assesses a project against all five SOC 2 Trust Service Criteria, producing a control-by-control PASS/PARTIAL/FAIL matrix with evidence references, remediation roadmap, and evidence collection checklist for audit readiness.

Provides a SQLite-backed persistence layer for skill execution metrics, feedback, improvement candidates, and version history, with query and maintenance workflows.

Use when converting compatible BLAST annotation XML/ASN streams into a compact gene-markup-style table for downstream EDirect interval helpers.

Use when working with GenBank format files and need to create reference indexers for sequence data retrieval or processing within the Entrez Direct toolkit.

Stores immutable data on-chain using IQ Labs tech stack including IQDB, hanLock, and x402.

**Trigger**: Use when working with GKE Storage — Google Kubernetes Engine configuration and management.

Session-end memory garbage collector. Promotes valuable L2 session facts to L1 atomic memory, wipes L2, and rotates oversized audit logs. Run at end of session to prevent context inflation and storage bloat.

Use when converting GenBank format (.gbf) files to FASTA format (.fsa) as part of sequence data preprocessing

Manage Azure Cache for Redis instances via the Azure Resource Manager .NET SDK, including creation, firewall rules, access keys, patch schedules, geo-replication, and private endpoints.

Provides reference information and CLI commands for Azure Storage services including Blob, File Shares, Queue, Table, and Data Lake, along with guidance on access tiers, redundancy options, and SDK usage.

Manage, annotate, and trace biological data with LaminDB, an open-source FAIR data framework that makes datasets queryable, versioned, and reproducible. Use when registering or querying biological datasets (scRNA-seq, spatial, flow cytometry), validating and curating data against ontologies (genes, cell types, diseases, tissues), tracking data lineage and computational workflows, building data lakehouses, or wiring integrations with Nextflow, Snakemake, W&B, or MLflow. Part of the AlterLab Academic Skills suite.

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。