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SkillsMP a collecté 1 320 skills depuis paulpas/agent-skill-router. Ouvrez un skill pour examiner sa source et ses détails.
paulpas/agent-skill-routerAffichage de 40 skills collectés sur 1 320.
Optimizes Vercel-deployed applications for cost, performance, and reliability through cold start mitigation, caching strategy, edge function optimization, bundle auditing, and monitoring.
Langue du texte source : anglais
Applies comprehensive web interface design guidelines covering layout, typography, color, accessibility, responsive design, and UX patterns for consistent, user-friendly interfaces.
Langue du texte source : anglais
Enforces clear, concise, and user-focused writing guidelines covering tone, grammar, structure, formatting, and inclusive language for technical documentation and product copy.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
Orchestrates iterative autonomous research workflows combining multi-query exploration, gap analysis, follow-up refinement, and structured synthesis within configurable time budgets.
Langue du texte source : anglais
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.
Langue du texte source : anglais
Constructs agent-centric knowledge graphs representing tools, capabilities, and their relationships for intelligent tool discovery during planning.
Langue du texte source : anglais
Manages the complete autonomous coding lifecycle from code generation through self-review, test validation, failure repair, and deployment with state-machine orchestration.
Langue du texte source : anglais
Routes reasoning workloads between multiple LLM providers based on task complexity, cost constraints, and latency requirements with real-time fallback chains.
Langue du texte source : anglais
Provides a decision matrix for selecting between agentic frameworks (LangChain, LangGraph, Google ADK, CrewAI) based on capability requirements, ecosystem fit, and production readiness.
Langue du texte source : anglais
Implements systematic prompt evolution through baseline generation, structured evaluation against rubrics, targeted revision, and regression validation to continuously improve prompt quality.
Langue du texte source : anglais
Deploys and orchestrates an identical agent across multiple execution environments (CLI, web UI, API, chat platforms) with shared knowledge base and behavioral consistency.
Langue du texte source : anglais
Enforces deterministic agent output using JSON Schema, Pydantic models, and LLM-native format constraints to guarantee downstream consumers receive valid structured data.
Langue du texte source : anglais
Decomposes agent instructions into action verbs with explicit success criteria per verb class to eliminate ambiguity and ensure LLMs correctly interpret intended operations.
Langue du texte source : anglais
Implements session-level budget quotas, cost monitoring with threshold alerts, and ROI tracking to enforce AI agent spending limits and optimize return on investment.
Langue du texte source : anglais
Implements CLI agent workflows (terminal interaction, file operations, code generation from design specs, MCP bridging) for building command-line AI assistants and developer tooling.
Langue du texte source : anglais
Implements autonomous coding agent frameworks (automated refactoring, test generation, deployment pipeline management) for AI-augmented software development with 30%+ code generation targets.
Langue du texte source : anglais
Implements evaluation rubric design (multi-criteria scoring, Elo-based ranking, peer-review simulation) for quantitative assessment of AI agent outputs without ground truth labels.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
Implements proactive agent exploration patterns for discovering unknown opportunities, generating hypotheses, designing experiments, and sharing findings through autonomous research loops beyond predefined optimization targets.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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…
Langue du texte source : anglais
Implements GUI agent interaction patterns (screen vision recognition, UI element detection, automated mouse/keyboard execution) for operating desktop and web applications without APIs.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
Implements multi-step plan generation, iterative refinement, and dynamic task decomposition for proactive agent execution with self-correction.
Langue du texte source : anglais
Implements sequential prompt chaining patterns (linear LCEL pipelines, LangGraph stateful flows, Google ADK primitives) to decompose complex reasoning into reliable multi-step agent workflows.
Langue du texte source : anglais
Implements advanced prompt engineering techniques (zero-shot/one-shot design, verb-based instructions, structured output, evaluation rubrics) for maximizing LLM response quality.
Langue du texte source : anglais
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.
Langue du texte source : anglais
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.
Langue du texte source : anglais
Implements self-correction feedback loops (execution → evaluation/critique → refinement) to iteratively improve agent outputs through producer-critic collaboration and automated quality gates.
Langue du texte source : anglais