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SkillsMP ha recopilado 1320 skills de paulpas/agent-skill-router. Abre una skill para revisar su origen y sus detalles.
paulpas/agent-skill-routerMostrando 40 de 1320 skills recopiladas.
Optimizes Vercel-deployed applications for cost, performance, and reliability through cold start mitigation, caching strategy, edge function optimization, bundle auditing, and monitoring.
Idioma del texto original: inglés
Applies comprehensive web interface design guidelines covering layout, typography, color, accessibility, responsive design, and UX patterns for consistent, user-friendly interfaces.
Idioma del texto original: inglés
Enforces clear, concise, and user-focused writing guidelines covering tone, grammar, structure, formatting, and inclusive language for technical documentation and product copy.
Idioma del texto original: inglés
Applies traditional software engineering rigor to AI agents through checkpoint/rollback state management, modular architecture, structured observability logging, and least-privilege permissions for production-grade reliability.
Idioma del texto original: inglés
Provides a four-level maturity model (Core Reasoning → Connected Solver → Strategic Problem-Solver → Collaborative Multi-Agent) for assessing and planning agent architecture investments based on capability requirements.
Idioma del texto original: inglés
Orchestrates iterative autonomous research workflows combining multi-query exploration, gap analysis, follow-up refinement, and structured synthesis within configurable time budgets.
Idioma del texto original: inglés
Automates multi-agent system design through three-stage optimization (block-level prompt tuning, influence-weighted topology search, workflow-level joint optimization) to discover optimal agent configurations and interactions.
Idioma del texto original: inglés
Constructs agent-centric knowledge graphs representing tools, capabilities, and their relationships for intelligent tool discovery during planning.
Idioma del texto original: inglés
Manages the complete autonomous coding lifecycle from code generation through self-review, test validation, failure repair, and deployment with state-machine orchestration.
Idioma del texto original: inglés
Routes reasoning workloads between multiple LLM providers based on task complexity, cost constraints, and latency requirements with real-time fallback chains.
Idioma del texto original: inglés
Provides a decision matrix for selecting between agentic frameworks (LangChain, LangGraph, Google ADK, CrewAI) based on capability requirements, ecosystem fit, and production readiness.
Idioma del texto original: inglés
Implements systematic prompt evolution through baseline generation, structured evaluation against rubrics, targeted revision, and regression validation to continuously improve prompt quality.
Idioma del texto original: inglés
Deploys and orchestrates an identical agent across multiple execution environments (CLI, web UI, API, chat platforms) with shared knowledge base and behavioral consistency.
Idioma del texto original: inglés
Enforces deterministic agent output using JSON Schema, Pydantic models, and LLM-native format constraints to guarantee downstream consumers receive valid structured data.
Idioma del texto original: inglés
Decomposes agent instructions into action verbs with explicit success criteria per verb class to eliminate ambiguity and ensure LLMs correctly interpret intended operations.
Idioma del texto original: inglés
Implements session-level budget quotas, cost monitoring with threshold alerts, and ROI tracking to enforce AI agent spending limits and optimize return on investment.
Idioma del texto original: inglés
Implements CLI agent workflows (terminal interaction, file operations, code generation from design specs, MCP bridging) for building command-line AI assistants and developer tooling.
Idioma del texto original: inglés
Implements autonomous coding agent frameworks (automated refactoring, test generation, deployment pipeline management) for AI-augmented software development with 30%+ code generation targets.
Idioma del texto original: inglés
Implements evaluation rubric design (multi-criteria scoring, Elo-based ranking, peer-review simulation) for quantitative assessment of AI agent outputs without ground truth labels.
Idioma del texto original: inglés
Implements knowledge graph construction (entity extraction, relationship mapping, graph database storage) and Graph-RAG integration for enterprise AI agents with structured reasoning over connected data.
Idioma del texto original: inglés
Implements dual-provider reasoning architecture (Gemini + Claude orchestration), cross-model reasoning pipelines, token budget management, and AI-assisted development velocity tracking for production AI systems.
Idioma del texto original: inglés
Implements Google's Agent-to-Agent (A2A) protocol for cross-framework agent communication using HTTP-based JSON-RPC 2.0 with agent card discovery, SSE streaming, and secure multi-agent interoperability patterns.
Idioma del texto original: inglés
Continuously monitors agent performance in production through token tracking, LLM-as-a-Judge evaluation, A/B testing for improvements, drift detection, anomaly detection, and trajectory analysis with structured feedback loops.
Idioma del texto original: inglés
Implements agent resilience patterns including retry logic with exponential backoff, fallback handler chains, state rollback, graceful degradation, and error escalation to maintain reliability under failure conditions.
Idioma del texto original: inglés
Implements proactive agent exploration patterns for discovering unknown opportunities, generating hypotheses, designing experiments, and sharing findings through autonomous research loops beyond predefined optimization targets.
Idioma del texto original: inglés
Implements goal-oriented agent architectures with objective definition, LLM-based success criteria evaluation, iterative progress tracking, and max-iteration bounded refinement loops for proactive autonomous systems.
Idioma del texto original: inglés
Protects agent systems from harmful outputs through behavioral constraints, input validation and sanitization, jailbreaking defenses, structured output enforcement, the Principle of Least Privilege, and fault-tolerant state management for safe autonomous…
Idioma del texto original: inglés
Implements GUI agent interaction patterns (screen vision recognition, UI element detection, automated mouse/keyboard execution) for operating desktop and web applications without APIs.
Idioma del texto original: inglés
Integrates human oversight into AI agent workflows for high-stakes decisions through approval gates, feedback loops for RLHF, escalation policies, and decision augmentation patterns that balance automation with accountability.
Idioma del texto original: inglés
Enables agents to improve through experience using reinforcement learning patterns (PPO, DPO, RLHF) and knowledge base RAG for continuous self-improvement and adaptive behavior across sessions.
Idioma del texto original: inglés
Integrates the Model Context Protocol (MCP) standard for LLM tool discovery and interaction, implementing MCP client-server architecture with stdio/HTTP transport, tool/resource/prompt types, and FastMCP SDK patterns.
Idioma del texto original: inglés
Manages agent memory across short-term (conversation buffers), long-term (vector stores, persistent databases), and procedural (learned patterns) layers to maintain stateful context across extended agent interactions.
Idioma del texto original: inglés
Orchestrates multiple specialized agents in concert using hierarchical, parallel, and sequential topologies (parent-child, debate/consensus, expert teams, sequential handoffs) to solve complex problems that exceed single-agent capability.
Idioma del texto original: inglés
Implements concurrent task execution patterns (parallel branches, fan-out/fan-in, multi-API calls, multi-modal processing) to reduce total agent processing time through independent subtask parallelism.
Idioma del texto original: inglés
Implements multi-step plan generation, iterative refinement, and dynamic task decomposition for proactive agent execution with self-correction.
Idioma del texto original: inglés
Implements sequential prompt chaining patterns (linear LCEL pipelines, LangGraph stateful flows, Google ADK primitives) to decompose complex reasoning into reliable multi-step agent workflows.
Idioma del texto original: inglés
Implements advanced prompt engineering techniques (zero-shot/one-shot design, verb-based instructions, structured output, evaluation rubrics) for maximizing LLM response quality.
Idioma del texto original: inglés
Implements Retrieval-Augmented Generation patterns (chunking strategies, embedding-based vector search, semantic vs keyword retrieval, RAG pipelines) to ground LLM outputs in authoritative external knowledge sources.
Idioma del texto original: inglés
Implements advanced reasoning methodologies (Chain-of-Thought, Tree-of-Thoughts, ReAct, Self-Correction, Graph of Debates, Program-Aided LLMs) for multi-step problem-solving in complex agent tasks.
Idioma del texto original: inglés
Implements self-correction feedback loops (execution → evaluation/critique → refinement) to iteratively improve agent outputs through producer-critic collaboration and automated quality gates.
Idioma del texto original: inglés