strmt7

Azure Resource Manager SDK for API Management in .NET. Use for MANAGEMENT PLANE operations: creating/managing APIM services, APIs, products, subscriptions, policies, users, groups, gateways, and backends via Azure Resource Manager. Triggers: "API Management", "APIM service", "create APIM", "manage APIs", "ApiManagementServiceResource", "API policies", "APIM products", "APIM subscriptions".

Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.

Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: "Microsoft 365 Agents SDK", "microsoft_agents", "AgentApplication", "start_agent_process", "TurnContext", "Copilot Studio client", "CloudAdapter".

Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

Use a deterministic browser workflow only when direct fetch or extraction is insufficient for a public-web task.

Run the smallest correct verification lane and report it precisely.

Keep public-web research compliant, bounded, and efficient through rule checks, caching, and backoff instead of evasive scraping.

Optimize a parity-proven ome-zarr native C++ surface for measurable speedups by improving data layout, copies, cache behavior, and boundary design without changing behavior.

Benchmark converted code against the frozen upstream implementation before making performance claims.

Port one upstream ome-zarr-py surface to standalone native C++ without touching the frozen snapshot.

Legacy reference only. Current main does not use pybind11 ports; use this only to understand old debt before removing it.

Choose Python-side parity tests and syntax checks without weakening coverage.

Use the pinned external CocoIndex Code MCP workflow for broad repository routing, then prove exact candidates with rg and direct file reads.

Bound repository context reads and semantic-search output without weakening source, test, or evidence verification.

Choose staged contract tests that catch partial AI-generated fixes across models, data, risk, execution ownership, and CLI/Windows parity.

Keep concise user docs and machine-readable model evidence synchronized with real behavior without losing provenance or caveats.

Apply four anti-error coding principles plus this repository's stricter financial-evidence, safety, and single-session rules.

Research the existing implementation, tests, primary technical sources, and financial rationale before adding trading code or dependencies.

Audit financial, data, model, and dependency claims for primary-source quality, dates, provenance, and reproducibility before implementation or publication.

Containerization rules for this repo — pinned base image, non-root user, read-only FS, explicit volumes for config and data.

Use when changing UE5 smartphone rendering, graphics settings, anti-aliasing, ambient occlusion, reflections, post-process, device profiles, or mobile performance tiers. Applies when exposing graphics options to players or claiming that a visual feature works on-device.

Use when changing a major UE5 front-end menu, options flow, modal stack, routed input surface, or layered smartphone-first menu architecture. Applies to CommonUI adoption decisions, activatable-widget stacks, menu settings flows, and player-facing mobile front-end structure.

Use when changing UE5 mobile gameplay flow, menu-to-battle state, touch input routing, hidden debug bindings, deterministic simulation ownership, or verification for a smartphone-first game. Applies to controller state, battle bootstrap, Enhanced Input, and automation-backed gameplay checks.

Use when changing any UE5 player-facing menu, HUD, pause surface, settings surface, touch control, or smartphone layout. Applies to UMG/CommonUI choice, safe zones, touch hit targets, information density, keyboard-hint suppression, and keeping the playfield visible on phones.

State the narrowest correct verification lane and report exactly what passed.

Use when validating Project Air Defense with screenshots, OCR, template matching, or live emulator navigation, especially when libGDX SurfaceView content is not visible in the Android accessibility tree.

Use this skill when building, debugging, balancing, profiling, or visually upgrading the Project Air Defense Android 3D missile-defense prototype, especially for libGDX battle flow, mobile rendering quality, interception logic, HUD ergonomics, and emulator-verified Android fixes.

One-shot caveman reference card aligned to upstream caveman v1.5.0.

Security review and hardening workflow for SuperZip archive extraction, path handling, Defender integration, GitHub Actions scanning, secrets, and third-party dependency updates.

Build, test, benchmark, and security-scan the SuperZip native C++ AMD HIP project on Windows. Use when changing SuperZip C++ source, build scripts, security checks, GPU code, ZIP/SUZIP archive logic, or GUI behavior.

Validate SuperZip CPU/GPU performance, RAM-only benchmarks, compression-level/block-size tuning, and AMD HIP telemetry without causing SSD wear. Use when changing benchmark scripts, compression levels, compression block sizes, worker allocation, GPU utilization, performance docs, or performance claims.

Plan, audit, and execute behavior-preserving SuperZip refactors with tests, benchmark gates, and security boundaries. Use when the user asks for refactoring, cleanup, architecture simplification, automatic code-quality remediation, or large-file/function reduction.