Results for “patterns”

Remove AI writing patterns from prose by cutting filler phrases, breaking formulaic structures, and enforcing active voice and specificity.

Manages Azure cloud resources via the Azure CLI, covering authentication, compute, storage, networking, and automation patterns.

Provides principles for 2D game development, covering sprite organization, tilemap design, physics, camera systems, and genre-specific patterns.

Generates a structured post-task reflection capturing surprises, patterns, prompt improvements, and gotchas, writing it to a markdown file without modifying project rules.

Analyzes codebases to map entry points, execution flows, architecture layers, patterns, and dependencies before new development begins.

Implements accessible, performant UI motion for React and Next.js apps, covering tokens, patterns, and modal behavior.

Identifies operational toil, prioritizes automation by ROI, and provides patterns for self-healing systems and runbook automation.

Reference guide for building Preact 10 applications with HTM, covering components, signals, web components, and performance patterns.

Query, filter, transform, and aggregate JSON data using jq, with practical patterns for shell pipelines and integration with CLI tools.

Pinia official Vue state management library, type-safe and extensible. Use when defining stores, working with state/getters/actions, or implementing store patterns in Vue apps.

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

Optimizes Dockerfiles, container security, multi-stage builds, Compose orchestration, and production deployment patterns.

Patterns for debugging and extending the AegisNex GitHub Actions CI pipeline. Covers common failure modes, encoding issues, dependency problems, and how to add new CI steps.

Self-evolution and contribution system for makepad-skills. Use when capturing new patterns, correcting skill errors, contributing learnings, or setting up hooks for auto-triggered skill updates.

Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.

Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.

Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.

Use when managing Render platform services, configuring render.yaml, provisioning Render databases, or automating Render infrastructure. This skill provides Render-specific operational patterns.

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

Handle errors as values using fp-ts Either and TaskEither for cleaner, more predictable TypeScript code. Use when implementing error handling patterns with fp-ts.

Bidirectional conversion between Elm and Erlang. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Erlang specific patterns.

Bidirectional conversion between Elm and Fsharp. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Fsharp specific patterns.

Bidirectional conversion between Fsharp and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Fsharp↔Roc specific patterns.

Bidirectional conversion between Erlang and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Erlang↔Scala specific patterns.

Bidirectional conversion between Fsharp and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Fsharp↔Scala specific patterns.

Bidirectional conversion between Elixir and Fsharp. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elixir↔Fsharp specific patterns.

Bidirectional conversion between Erlang and Fsharp. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Erlang↔Fsharp specific patterns.

Patterns for H5AD-backed Shiny apps with Excel fallback, groovy data in .uns, scVI QC validation, Leiden clustering merge, per-donor tissue extraction

Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.

Read BEFORE writing any code that adds Additive White Gaussian Noise (AWGN) to signals and converts between SNR, Eb/No, Es/No, and per-subcarrier SNR for communications simulations, using awgn(), convertSNR(), berawgn(). The default MATLAB patterns for AWGN (e.g., 'measured' option, manual SNR formulas) produce subtly incorrect results. This skill specifies the correct calling conventions, required function usage, and critical anti-patterns that must be avoided.

Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).

Configures GKE workload reliability with PodDisruptionBudgets, health probes, topology spread constraints, and graceful shutdown patterns.

Map requirements to the most suitable VueUse composable and apply correct usage patterns for concise, maintainable Vue.js/Nuxt features.

Provides C# and .NET testing patterns using xUnit, FluentAssertions, mocking, integration tests, and test organization best practices.

Automatically evaluates Claude Code sessions to extract reusable patterns and save them as learned skills.