DevOps

Creating and maintaining CLAUDE.md project memory files that provide non-obvious codebase context. Use when (1) creating a new CLAUDE.md for a project, (2) adding architectural patterns or design decisions to existing CLAUDE.md, (3) capturing project-specific conventions that aren't obvious from code inspection.

Guide for creating effective Claude Skills. This skill should be used when users want to create (or update) a skill that extends Claude's capabilities with specialised knowledge, workflows, or tool integrations.

Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.

Use when writing or changing tests, adding mocks, or tempted to add test-only methods to production code - prevents testing mock behaviour, production pollution with test-only methods, and mocking without understanding dependencies

Applies the Diataxis framework to create or improve technical documentation. Use when being asked to write high quality tutorials, how-to guides, reference docs, or explanations, when reviewing documentation quality, or when deciding what type of documentation to create. Helps identify documentation types using the action/cognition and acquisition/application dimensions.

Practical bash scripting guidance emphasising defensive programming, ShellCheck compliance, and simplicity. Use when writing shell scripts that need to be reliable and maintainable.

Load before running any glab commands to ensure correct CLI syntax. Use when creating/viewing MRs, checking pipelines, managing issues, or any GitLab operations (when remote contains "gitlab").

Use narsil-mcp code intelligence tools effectively. Use when searching code, finding symbols, analyzing call graphs, scanning for security vulnerabilities, exploring dependencies, or performing static analysis on indexed repositories.

Specialized in reverse-engineering compiled binaries (JARs, DLLs). Use this when the user asks to compare versions, find security fixes, or analyze binary patches.

Use when designing or architecting Elixir/Phoenix applications, creating comprehensive project documentation, planning OTP supervision trees, defining domain models with Ash Framework, structuring multi-app projects with path-based dependencies, or preparing handoff documentation for Director/Implementor AI collaboration

Provides guidance on software architecture patterns and design decisions. Use when designing systems, choosing patterns, structuring projects, or when asked about architectural approaches.

Designs comprehensive testing strategies for any codebase. Use when adding tests, improving coverage, setting up testing infrastructure, or when asked about testing approaches.

Write LaTeX ML/AI review articles for arXiv using the IEEEtran template and verified BibTeX citations.

Post-process LaTeX project prose to improve readability through varied sentence and paragraph lengths. Removes filler phrases and unnecessary transitions while preserving all citations and semantic meaning.

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

Multiplayer game development principles. Architecture, networking, synchronization.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Use when tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from timing guesses