Results for “patterns”

API design principles and decision-making.

Bash/Linux terminal patterns and critical commands.

Master modern JavaScript with ES6+, async patterns, and Node.js APIs. Handles promises, event loops, and browser/Node compatibility. Use PROACTIVELY for JavaScript optimization, async debugging, or complex JS patterns.

Master TypeScript with advanced types, generics, and strict type safety. Handles complex type systems, decorators, and enterprise-grade patterns. Use PROACTIVELY for TypeScript architecture, type inference optimization, or advanced typing patterns.

Audits test files for anti-patterns and quality issues, producing a severity-ranked report across multiple languages and frameworks.

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

Provides detailed guidance on Hermes Kanban worker lifecycle, including workspace handling, handoff patterns, retry diagnostics, and edge cases for dispatched workers.

Pre-built cron job templates for common automation patterns.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, file_patterns, code_patterns, " mentioned.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new contexts.

A library of reusable, production-tested prompt engineering patterns for building AI-powered features. Use when designing system prompts for apps, building AI pipelines, selecting the right prompting technique for a use case, or reviewing prompts for common failure modes. Complements the prompt-engineering skill (which covers the optimization framework); this skill covers the pattern library itself.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new problems.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Provides a framework for rolling out organizational changes using the ADKAR model adapted for startups, with communication templates, resistance patterns, and change fatigue management.

Tracks bidirectional confidence scores across five domains (TECH, OPS, JUDGMENT, COMMS, ORCH) for agents and users, with triggers, anti-patterns, and pool scoring per runtime.

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Use when implementing mcp functionality with production-grade patterns and safeguards.

Bidirectional conversion between Clojure and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Roc specific patterns. Use when migrating Clojure applications to Roc's platform model, translating dynamic functional code to static functional style, or refactoring REPL-driven code to compile-time verified patterns. Extends meta-convert-dev with Clojure-to-Roc specific patterns.

Diagnose and mitigate context degradation patterns including lost-in-middle failures, context poisoning, distraction, confusion, and clash in AI agent systems.

Bidirectional conversion between Haskell and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Haskell↔Roc specific patterns. Use when migrating Haskell applications to Roc's platform model, translating lazy pure functional code to strict platform-based architecture, or refactoring type class based designs to ability-based patterns. Extends meta-convert-dev with Haskell-to-Roc specific patterns.

Provides proven architectural patterns for building n8n workflows, covering webhook processing, HTTP API integration, database operations, AI agent workflows, batch processing, and scheduled tasks.

Expert en architecture événementielle (Kafka, RabbitMQ, event sourcing, CQRS, saga patterns)

Agent SDK task integration patterns for Business Central. Triggers on: Agent Task Builder, Agent Task Message Builder, Public API, AssignTask, ExternalId, agent session detection, BindSubscription, TryFunction agent task, multi-turn agent conversation, agent attachment, or task lifecycle management.

MCP (Model Context Protocol) server building principles. Tool design, resource patterns, best practices.

Essential Git patterns for effective version control, eliminating redundant Git guidance per agent.