Results for “storage-quota”

Expert knowledge for Azure Backup development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when protecting Azure VMs, SQL/SAP HANA, AKS, Files/Blobs, or automating backup via CLI/PowerShell/REST, and other Azure Backup related development tasks. Not for Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files).

Expert knowledge for Azure Service Bus development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when securing namespaces, configuring queues/topics, using JMS clients, geo-DR/replication, or NServiceBus, and other Azure Service Bus related development tasks. Not for Azure Event Hubs (use azure-event-hubs), Azure Event Grid (use azure-event-grid), Azure Queue Storage (use azure-queue-storage), Azure Relay (use azure-relay).

Diagnoses and reduces Qdrant memory usage by analyzing resident memory, page cache, and providing optimization techniques like quantization, on-disk storage, and async_scorer.

Stores, retrieves, and curates persistent facts in a structured memory map, distinguishing between user facts and reference text.

Compute, object storage, block storage, a managed relational database, a message

Decide where to save any piece of information — monday.com, local file, daily notes, or MEMORY.md. Use this skill before saving anything to ensure the right destination. Prevents local clutter and ensures the owner can access all relevant content in monday.com.

Quantize LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss using bitsandbytes. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers.

Almost every cloud estate is over-provisioned.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations. Use when the user runs /si:status or asks how full or healthy the agent memory is.

Check and manage Azure quotas and service limits across resource providers and regions for deployment planning and capacity validation.

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

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.

Guides scaling decisions for Qdrant vector databases when data volume exceeds single-node capacity, covering tenant scaling, time window rotation, vertical scaling, and horizontal sharding.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Diagnoses Qdrant capacity needs and guides horizontal scaling decisions, including node count, shard count, replication factor, and resharding trade-offs.

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

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

Compress bloated project or global memory while preserving decisions, rationale, revisit triggers, provenance, and active user preferences. Load when memory exceeds budget, global memory is too large, session logs are repetitive, or before appending to an over-budget memory file.

Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.

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.

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

Manage AWS S3 buckets and objects: create buckets, upload/download files, configure lifecycle policies, set up static websites, manage permissions, and implement cross-region replication.

Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".

CQO quality and operational governance lead. Owns gates, regression strategy, memory hygiene, port/service policy, and archive approval.

Multi-tiered memory management (HOT/WARM/COLD) for context compaction. Invoke ONLY for explicit compaction events: post-`/compact` cleanup, MEMORY.md tier promotion, archive batch, or "trim my context". NOT for general recall (use deep-recall) or routine memory writes (use storage-router). Triggers: "compact memory", "promote to durable", "archive old context", "tier this".

Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".

Scout research skill — Idea Memory

Use this when a Supabase app needs file or object storage design, bucket strategy, signed URL handling, metadata patterns, upload trust boundaries, or storage access controls.

Training Data Lifecycle Management (v5.4.2)

Optimizes C++ memory management and performance with smart pointers, custom allocators, move semantics, SIMD, cache-friendly design, and memory pools.

Optimize Kubernetes cluster costs by right-sizing resources, configuring autoscalers, using spot instances, and monitoring spend.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.