productivity▌

Before running any command, follow ../_shared/preflight.md. Use metadata.version from this file's frontmatter as the reference for Step 2.

Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization. \n \n Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment \n Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments \n Includes five-stage workflow: dataset preparation, method selection, training wit

Multi-layer memory architecture for agents: short-term context, long-term vector storage, and retrieval optimization. \n \n Covers seven memory types: short-term (context window), long-term (vector stores), working, episodic, semantic, and procedural memory, each suited to different information patterns \n Provides three core patterns: memory type selection, vector store choice, and chunking strategy to maximize retrieval accuracy \n Highlights critical retrieval challenges: contextual chunking,

Clean Architecture patterns for Android and KMP projects. Covers module boundaries, dependency inversion, UseCase/Repository patterns, and data layer design with Room, SQLDelight, and Ktor.

OMERO is an open-source platform for managing, visualizing, and analyzing microscopy images and metadata. Access images via Python API, retrieve datasets, analyze pixels, manage ROIs and annotations, for high-content screening and microscopy workflows.

Web frontend development with CloudBase integration, static hosting deployment, and SDK-based authentication. \n \n Covers project structure conventions (src/dist/cloudfunctions directories), modern build systems (Vite, Webpack), and hash routing for static hosting compatibility \n Provides CloudBase Web SDK integration patterns for database, cloud functions, and authentication, with strict rules against implementing login logic in cloud functions \n Includes deployment guidance for CloudBase st

Skill by ara.so — Daily 2026 Skills collection.

$23

Build and debug MCP servers and clients connecting AI systems with external tools and data sources. \n \n Supports TypeScript (Node.js SDK) and Python (FastMCP) for implementing tool handlers, resource providers, and prompt templates with Zod or Pydantic schema validation \n Covers three transport layers: stdio (local), HTTP, and SSE (streaming), with JSON-RPC 2.0 protocol compliance and interactive debugging via the MCP inspector \n Includes scaffolding workflows, schema design patterns, and er

Coordinate specialized agents to solve complex problems through orchestrated workflows and collaborative reasoning. \n \n Supports four core orchestration patterns: sequential task chains, parallel execution, hierarchical delegation, and consensus-based debate \n Includes templates and examples for CrewAI, AutoGen, LangGraph, and OpenAI Swarm frameworks \n Provides utilities for agent communication (message brokers, shared memory), workflow management (execution, optimization, monitoring), and p