Skills in diesem Repository
SkillsMP hat 1.338 Skills aus paulpas/agent-skill-router gesammelt. Öffne einen Skill, um Quelle und Details zu prüfen.
paulpas/agent-skill-routerEs werden 40 von 1.338 gesammelten Skills angezeigt.
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
Implements proactive agent exploration patterns for discovering unknown opportunities, generating hypotheses, designing experiments, and sharing findings through autonomous research loops beyond predefined optimization targets.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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…
Quellsprache: Englisch
Implements GUI agent interaction patterns (screen vision recognition, UI element detection, automated mouse/keyboard execution) for operating desktop and web applications without APIs.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
Implements multi-step plan generation, iterative refinement, and dynamic task decomposition for proactive agent execution with self-correction.
Quellsprache: Englisch
Implements sequential prompt chaining patterns (linear LCEL pipelines, LangGraph stateful flows, Google ADK primitives) to decompose complex reasoning into reliable multi-step agent workflows.
Quellsprache: Englisch
Implements advanced prompt engineering techniques (zero-shot/one-shot design, verb-based instructions, structured output, evaluation rubrics) for maximizing LLM response quality.
Quellsprache: Englisch
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.
Quellsprache: Englisch
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.
Quellsprache: Englisch
Implements self-correction feedback loops (execution → evaluation/critique → refinement) to iteratively improve agent outputs through producer-critic collaboration and automated quality gates.
Quellsprache: Englisch
Dynamically routes agent work based on cost, budget constraints, latency requirements, and query complexity using LLM-driven model selection with feedback-loop optimization for efficient resource utilization.
Quellsprache: Englisch
Implements intent-based routing patterns (LLM classifiers, node transitions, computational graphs) to dispatch queries to specialized sub-agents with fallback chains and confidence scoring.
Quellsprache: Englisch
Enables agents to rank and schedule tasks by urgency, importance, dependencies, and resource cost using priority matrices, dynamic re-prioritization, and dependency-aware scheduling for optimal execution order.
Quellsprache: Englisch
Implements agent tool use and function calling patterns (@tool decorators, CrewAI tools, Google ADK built-in tools) to enable agents with real-time external data access, API operations, calculations, and code execution.
Quellsprache: Englisch
Implements intelligent acceptance orchestrator with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements intelligent address github comments with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements structural design patterns for AI agent systems including monolithic, multi-agent, hierarchical, and event-driven architectures with state management and security primitives.
Quellsprache: Englisch
Implements inter-agent communication patterns (message passing, event-driven coordination, shared memory protocols, RPC-style calls, structured JSON messaging) for reliable multi-agent systems.
Quellsprache: Englisch
Implements context window management, sliding window strategies, and persistent memory patterns to maintain AI agent coherence across long interactions.
Quellsprache: Englisch
Implements context window management and memory architectures for LLM agents including token budgeting, sliding window strategies, summarization fallbacks, cross-turn state persistence, and external vector store integration.
Quellsprache: Englisch
Implements systematic debugging workflows for LLM agent failures including hallucination detection, infinite loop recovery, context window exhaustion, tool call errors, and cascading failure diagnosis using distributed tracing patterns.
Quellsprache: Englisch
Implements intelligent agent evaluation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements intelligent agent manager skill with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements intelligent agent memory systems with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements tracing, cost tracking, and latency monitoring patterns for AI agent systems to debug failures, control token spend, and optimize response times across multi-agent workflows.
Quellsprache: Englisch
Implements fault-tolerance mechanisms for AI agent systems including circuit breakers, exponential backoff retries, graceful degradation, health checks, dead letter queues, and timeout management with observability hooks.
Quellsprache: Englisch
Defines and structures functional, non-functional, and safety requirements for AI agent systems including capability matrices, hallucination thresholds, data quality standards, and evaluation criteria.
Quellsprache: Englisch
Implements prompt injection detection, input validation, tool access control, and output sanitization to secure LLM agents against adversarial attacks.
Quellsprache: Englisch
Implements systematic evaluation, benchmarking, and testing of AI agent behaviors with tool-use accuracy, hallucination detection, multi-turn reasoning metrics, and automated grading pipelines.
Quellsprache: Englisch
Implements intelligent ai agent development with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch
Implements guardrails, safety checks, hallucination detection, prompt injection defense, and output validation for autonomous AI agents to prevent misuse, unauthorized actions, and unreliable behavior.
Quellsprache: Englisch
Implements intelligent ai agents architect with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense
Quellsprache: Englisch