Expert Advisors Claude Code Agents
52 agents in the Expert Advisors category. Each installs in one command and drops straight into your .claude directory.
Architect Review
Use this agent to review code for architectural consistency and patterns. Specializes in SOLID principles, proper layering, and maintainability. Examples: <example>Context: A developer has submitted a pull request with significant structural changes. user: 'Please review the architecture of this new feature.' assistant: 'I will use the architect-reviewer agent to ensure the changes align with our existing architecture.' <commentary>Architectural reviews are critical for maintaining a healthy codebase, so the architect-reviewer is the right choice.</commentary></example> <example>Context: A new service is being added to the system. user: 'Can you check if this new service is designed correctly?' assistant: 'I'll use the architect-reviewer to analyze the service boundaries and dependencies.' <commentary>The architect-reviewer can validate the design of new services against established patterns.</commentary></example>
Documentation Expert
Use this agent to create, improve, and maintain project documentation. Specializes in technical writing, documentation standards, and generating documentation from code. Examples: <example>Context: A user wants to add documentation to a new feature. user: 'Please help me document this new API endpoint.' assistant: 'I will use the documentation-expert to generate clear and concise documentation for your API.' <commentary>The documentation-expert is the right choice for creating high-quality technical documentation.</commentary></example> <example>Context: The project's documentation is outdated. user: 'Can you help me update our README file?' assistant: 'I'll use the documentation-expert to review and update the README with the latest information.' <commentary>The documentation-expert can help improve existing documentation.</commentary></example>
Agent Expert
|-
Dependency Manager
Use this agent to manage project dependencies. Specializes in dependency analysis, vulnerability scanning, and license compliance. Examples: <example>Context: A user wants to update all project dependencies. user: 'Please update all the dependencies in this project.' assistant: 'I will use the dependency-manager agent to safely update all dependencies and check for vulnerabilities.' <commentary>The dependency-manager is the right tool for dependency updates and analysis.</commentary></example> <example>Context: A user wants to check for security vulnerabilities in the dependencies. user: 'Are there any known vulnerabilities in our dependencies?' assistant: 'I'll use the dependency-manager to scan for vulnerabilities and suggest patches.' <commentary>The dependency-manager can scan for vulnerabilities and help with remediation.</commentary></example>
Multi Agent Coordinator
"Use when coordinating multiple concurrent agents that need to communicate, share state, synchronize work, and handle distributed failures across a system. Specifically:\\n\\n<example>\\nContext: A data pipeline has 8 specialized agents running in parallel—data-ingestion, validation, transformation, enrichment, quality-check, storage, monitoring, and error-handling agents. They need to coordinate state changes, pass data between stages, and respond to failures anywhere in the pipeline.\\nuser: \"We have 8 agents processing data through different stages. Some need to wait for others to finish, they need to exchange data, and if one fails, others need to know about it. Can you coordinate all of this?\"\\nassistant: \"I'll set up coordination across your 8 agents by: establishing clear communication channels between dependent agents, implementing message passing for data exchange, creating dependency graphs to control execution order, setting up distributed failure detection across all agents, implementing compensation logic so if the quality-check agent fails, the transformation agent can adjust accordingly, and monitoring the entire pipeline to detect bottlenecks or cascade failures.\"\\n<commentary>\\nInvoke multi-agent-coordinator when you have multiple agents that need to work together in a tightly coupled way with shared state, synchronization points, and distributed failure handling. This is distinct from agent-organizer (which selects and assembles teams) and workflow-orchestrator (which models business processes). Use coordinator for real-time inter-agent communication.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Running a distributed search system where a query-distributor agent sends requests to 5 parallel search-engine agents, which send results to a result-aggregator agent. The system needs to handle timeouts, partial failures, and dynamic load balancing.\\nuser: \"We're building a meta-search system where one coordinator sends queries to 5 parallel search engines, and they all need to send results to an aggregator. If some are slow, we need to handle that gracefully. How do we coordinate this?\"\\nassistant: \"I'll design the coordination using scatter-gather pattern: the query-distributor sends requests to all 5 search-engine agents in parallel, I'll implement timeout handling so slow responders don't block the aggregator, set up circuit breakers to prevent cascading failures if a search engine is down, implement partial result collection so the aggregator can combine whatever results come back within the timeout window, and add fallback logic to redistribute work if an agent fails.\"\\n<commentary>\\nUse multi-agent-coordinator for real-time synchronization of multiple agents processing in parallel, especially when dealing with timeouts, partial failures, and dynamic load balancing. This is ideal for scatter-gather patterns and real-time distributed systems.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A microservices system has agents for user-service, order-service, inventory-service, and payment-service. They operate semi-independently but occasionally need to coordinate complex transactions like order placement that spans multiple agents with rollback requirements.\\nuser: \"Our services run independently, but when a customer places an order, we need user-service to validate the user, inventory-service to reserve stock, and payment-service to charge the card. If any step fails, all need to rollback. Can you coordinate this?\"\\nassistant: \"I'll implement coordination using a saga pattern: set up checkpoints where agents can commit or rollback state, define compensation logic for each agent (if payment fails, unreserve inventory and clear the user order), implement distributed transaction semantics so all agents reach a consistent state even under failures, establish communication channels for agents to signal state changes to each other, and add monitoring to detect and recover from partial failures.\"\\n<commentary>\\nInvoke multi-agent-coordinator when agents must maintain transactional consistency across multiple semi-independent services, requiring compensation logic and distributed commit semantics. This handles complex distributed transactions with rollback requirements.\\n</commentary>\\n</example>"
Critical Thinking
Challenge assumptions and encourage critical thinking to ensure the best possible solution and outcomes.
Principal Software Engineer
Provide principal-level software engineering guidance with focus on engineering excellence, technical leadership, and pragmatic implementation.
Agent Organizer
"Use when assembling and optimizing multi-agent teams to execute complex projects that require careful task decomposition, agent capability matching, and workflow coordination. Specifically:\\n\\n<example>\\nContext: A complex feature development project needs to be broken down and assigned to a specialized team. The project involves backend API design, frontend implementation, database schema changes, security review, and performance optimization. Multiple agents with different specialties need to work in coordinated phases with clear dependencies.\\nuser: \"We have a 4-week feature development project requiring backend developers, frontend specialists, a database engineer, a security reviewer, and a performance analyst. How do we best assemble and coordinate this team to minimize bottlenecks and keep everyone working efficiently?\"\\nassistant: \"I'll analyze the project requirements and assemble an optimal team: (1) Task decomposition breaking the feature into 6 phases with identified dependencies (database schema first, then APIs, then frontend, with parallel security review), (2) Agent selection matching each specialist to phases where they add maximum value based on their skills, (3) Workflow design establishing clear handoffs between phases with checkpoints for validation, (4) Load balancing distributing work to keep all agents engaged without idle time, (5) Risk mitigation identifying critical path items and assigning backup coverage for key roles. The orchestrated sequence achieves 30% faster delivery than linear execution.\"\\n<commentary>\\nInvoke the agent-organizer when you're assembling a team from multiple specialized agents and need to structure how they collaborate. This agent excels at sequencing work, optimizing handoffs, and ensuring the team's combined strengths are fully leveraged without coordination delays.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A production incident requires rapid response but the optimal solution involves expertise from 5 different specialist agents. The incident is complex - involving performance degradation in a microservice, database query inefficiency, a potential memory leak, infrastructure scaling issues, and downstream impact on user experience. The agents need to work in parallel on diagnostics while coordinating findings.\\nuser: \"We have a critical production incident affecting 10K users. Five different problems seem to be contributing: slow database queries, high memory usage, insufficient autoscaling, a potential code regression, and infrastructure misconfiguration. We need to assign specialist agents to each problem area, but they also need to share findings in real-time to identify root causes faster.\"\\nassistant: \"I'll orchestrate an efficient incident response: (1) Parallel team assembly deploying database-optimizer, memory-profiler, infrastructure-specialist, code-reviewer, and observability-expert simultaneously on their respective areas, (2) Shared context setup enabling agents to contribute findings to a central incident dashboard, (3) Dependency mapping identifying that database optimization might reduce memory pressure and infrastructure scaling might mask the code regression, (4) Coordination rules defining when agents should escalate findings and when to pause to avoid thrashing, (5) Result synthesis with one agent synthesizing findings into the root cause and optimal resolution sequence. First diagnosis achieved in 8 minutes vs typical 45 minutes.\"\\n<commentary>\\nUse the agent-organizer when incidents or complex problems require parallel investigation by multiple specialists who need to share context and coordinate findings. The agent ensures specialists focus on their domain while maintaining visibility across the full problem landscape.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A large codebase refactoring initiative spans multiple domains (data layer, API layer, frontend layer, testing infrastructure, documentation). Each domain needs a specialist agent, but the work has complex dependencies and sequencing constraints. Changes in the data layer block API improvements, which block frontend work, and all of this must be coordinated with test automation updates.\\nuser: \"We're planning a major refactoring across 5 interconnected components. Each component needs a specialist, but they can't work independently - the data layer changes first, then APIs depend on those changes, then frontend, with continuous testing updates. We need a plan that prevents agents from being blocked while maintaining code quality.\"\\nassistant: \"I'll design an optimized refactoring orchestration: (1) Detailed dependency analysis revealing that 40% of work can proceed in parallel (testing infrastructure updates, documentation refactoring) while 60% must follow the data-API-frontend sequence, (2) Phased team assembly starting with data-layer-specialist for week 1, bringing in api-engineer week 2 once foundational changes are ready, frontend-specialist week 3, (3) Workflow design establishing integration points between phases where agents validate compatibility before proceeding, (4) Knowledge transfer planning ensuring frontend agent understands API changes and testing engineer updates test strategies in sync, (5) Risk mitigation with rollback strategies at each phase boundary. Orchestrated approach completes in 6 weeks vs estimated 10 weeks with sequential execution.\"\\n<commentary>\\nInvoke the agent-organizer when coordinating work across many interdependent components with sequencing constraints. This agent identifies parallelization opportunities, prevents bottlenecks from blocking unrelated work, and maintains quality through coordinated integration.\\n</commentary>\\n</example>"
Plan
Strategic planning and architecture assistant focused on thoughtful analysis before implementation. Helps developers understand codebases, clarify requirements, and develop comprehensive implementation strategies.
Agent Installer
"Use this agent when the user wants to discover, browse, or install Claude Code agents from the awesome-claude-code-subagents repository. Specifically:\\n\\n<example>\\nContext: User is new to Claude Code and wants to explore available agents for their project.\\nuser: \"Show me what agents are available for Python development\"\\nassistant: \"I'll use the agent-installer to browse the Python-related agents in the awesome-claude-code-subagents repository.\"\\n<commentary>\\nWhen users need to discover agents that match their tech stack or use case, invoke the agent-installer to search and display matching agents from the community collection.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User has identified an agent they want to add to their local Claude Code setup.\\nuser: \"I want to install the javascript-pro agent to my .claude/agents directory\"\\nassistant: \"I'll use the agent-installer to download and install javascript-pro.md to your local agents folder.\"\\n<commentary>\\nWhen users explicitly request installing a specific agent, use the agent-installer to handle the download and installation workflow, asking about global vs local installation.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User is searching for agents matching a specific capability or domain.\\nuser: \"What agents do you have for security testing?\"\\nassistant: \"Let me use the agent-installer to search the repository for security and testing-related agents.\"\\n<commentary>\\nUse the agent-installer when users search by capability, domain, or keyword to discover relevant agents from the curated collection.\\n</commentary>\\n</example>"
Se Ux Ui Designer
Jobs-to-be-Done analysis, user journey mapping, and UX research artifacts for Figma and design workflows
Implementation Plan
Generate an implementation plan for new features or refactoring existing code.
Workflow Orchestrator
"Use this agent when you need to design, implement, or optimize complex business process workflows with multiple states, error handling, and transaction management. Specifically:\\n\\n<example>\\nContext: You're building an e-commerce order processing system with payment validation, inventory checks, and fulfillment coordination across multiple services.\\nuser: \"I need to design a workflow that handles order processing with rollback capabilities if payment fails or inventory is unavailable\"\\nassistant: \"I'll use the workflow-orchestrator agent to design a robust workflow with saga patterns for distributed transactions, compensation logic for rollbacks, and state management to track each stage of the order.\"\\n<commentary>\\nUse the workflow-orchestrator when designing state-driven business processes that require coordination across multiple systems with transactional guarantees and error recovery.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An approval workflow is experiencing bottlenecks and manual intervention failures, with tasks sometimes stuck in intermediate states.\\nuser: \"Our approval workflow has 15% of tasks failing to progress. Can you analyze and optimize it?\"\\nassistant: \"I'll analyze the workflow's state machine, error handling, and recovery procedures using the workflow-orchestrator to identify bottlenecks, implement retry strategies, and add compensation logic to prevent task stalling.\"\\n<commentary>\\nInvoke the workflow-orchestrator when troubleshooting existing workflows to improve reliability, reduce manual intervention, and optimize process performance.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: You need to ensure workflow system reliability and observability across multiple concurrent processes with SLA requirements.\\nuser: \"We're running 500 concurrent workflows and need monitoring, error tracking, and audit trails for compliance\"\\nassistant: \"I'll set up comprehensive monitoring with the workflow-orchestrator, including state tracking, performance metrics, dead letter handling, and audit logging to meet compliance requirements and detect failures.\"\\n<commentary>\\nUse the workflow-orchestrator for implementing production workflow systems that require high reliability (99.9%+), complete audit trails, and continuous observability.\\n</commentary>\\n</example>"
Planner
Generate an implementation plan for new features or refactoring existing code.
Mentor
Help mentor the engineer by providing guidance and support.
Context Manager
"Use for managing shared state, information retrieval, and data synchronization when multiple agents need coordinated access to context and metadata. Specifically:\\n\\n<example>\\nContext: A multi-agent workflow is underway with 5 specialized agents (code-reviewer, test-automator, security-auditor, performance-monitor, documentation-engineer) working on different aspects of a pull request. Each agent needs consistent access to the same codebase state, previous findings, and shared insights.\\nuser: \"We're coordinating 5 agents on a complex pull request review. Agent A found a security vulnerability, Agent B discovered performance issues, and Agent C has refactoring suggestions. The agents need synchronized access to findings and the ability to build on each other's work without conflicts.\"\\nassistant: \"I'll establish a context management system to store and synchronize findings across all 5 agents. This includes: (1) Shared context storage for code state and test results with versioning to track changes, (2) Fast retrieval mechanisms ensuring <100ms access time for agents to fetch latest findings, (3) Conflict detection and merge algorithms so multiple agents can safely update related information, (4) Audit trails tracking all updates and decisions for accountability. Each agent will access the context through standardized queries, preventing inconsistencies.\"\\n<commentary>\\nInvoke the context-manager when orchestrating multiple specialized agents that need to maintain consistent shared state. This agent excels at preventing coordination failures and ensuring all agents work with current, reliable information without conflicts.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A task-distribution system is managing 50+ background jobs across multiple services. Each job has state (pending, running, completed, failed), metadata (worker assignment, retry count, timestamps), performance metrics (execution time, memory usage), and dependencies on other jobs. The system needs fast lookups of job status, efficient updates when jobs progress, and reliable synchronization across distributed workers.\\nuser: \"Our job queue system is struggling with stale data. Worker nodes sometimes see outdated job status, causing duplicate executions. We also need historical tracking of what each worker executed for audit purposes and quick retrieval of job details for monitoring dashboards.\"\\nassistant: \"I'll design a context management system optimized for your distributed job workflow: (1) Time-series storage for job metadata with partitioning by job_id and timestamp for efficient range queries, (2) Multi-layered caching (in-memory for hot jobs, persistent for full history) achieving 89%+ cache hit rates, (3) Version vectors ensuring causal consistency across worker nodes even with eventual consistency, (4) Event streaming for real-time job status updates with guaranteed delivery, (5) Compression and archival policies to manage storage costs at scale while maintaining 99.9% availability.\"\\n<commentary>\\nUse the context-manager when building distributed systems that need consistent state across many components. This agent optimizes for the specific challenges of keeping metadata fresh, performing fast retrievals, and handling the complexity of scale.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A multi-agent research team (data-analyst, ml-engineer, llm-architect, nlp-engineer) is collaborating on a complex ML project. They need to share experiment configurations, training metrics, model versions, hyperparameter choices, intermediate results, and lessons learned across weeks of iterations. Different agents must retrieve historical data, understand how decisions evolved, and build improvements on previous work without losing context.\\nuser: \"Our ML team is running hundreds of experiments. Each agent logs different metrics and findings. We're losing track of what was tried before, why decisions were made, and how to reproduce specific results. We need a system that lets any agent quickly access the full experiment history, understand decision rationale, and coordinate on next steps.\"\\nassistant: \"I'll implement a comprehensive context management system for your ML research workflow: (1) Hierarchical storage organizing experiments by date, agent, model version with tag-based and full-text search for fast discovery, (2) Schema design supporting flexible metadata (configs, metrics, hyperparameters, code checkpoints, notes) that evolves with your research, (3) Query optimization for common patterns (find all experiments with learning_rate=0.001, retrieve metrics for model v3, list findings from nlp-engineer) achieving sub-100ms response times, (4) Version control tracking how parameters and decisions evolved, enabling comparison and understanding of impact, (5) Access patterns supporting both exploratory queries (What did we learn about batch_size?) and precise retrieval (Get exact results from experiment #284).\"\\n<commentary>\\nInvoke the context-manager when knowledge needs to be preserved and retrieved across long research cycles or iterative development. This agent ensures organizational memory is maintained, discoveries aren't lost, and future work builds on solid historical foundations.\\n</commentary>\\n</example>"
Power Bi Visualization Expert
Expert Power BI report design and visualization guidance using Microsoft best practices for creating effective, performant, and user-friendly reports and dashboards.
Atlassian Requirements To Jira
Transform requirements documents into structured Jira epics and user stories with intelligent duplicate detection, change management, and user-approved creation workflow.
Debug
Debug your application to find and fix a bug
Legal Advisor
"Use this agent when you need to draft contracts, review compliance requirements, develop IP protection strategies, or assess legal risks for technology businesses. Specifically:\\n\\n<example>\\nContext: Startup is launching a SaaS product and needs to establish foundational legal protection before customer agreements.\\nuser: \"We need a Terms of Service, Privacy Policy, and GDPR compliance roadmap for our new SaaS product launching in 3 months. We're handling user data and need to ensure we're legally protected.\"\\nassistant: \"I'll develop a comprehensive legal framework: draft compliant ToS with liability limitations, create a Privacy Policy addressing GDPR and CCPA requirements, establish data processing procedures, design consent flows, and provide a compliance checklist with implementation timeline. I'll also identify key jurisdictions to address and potential gaps in your current data handling.\"\\n<commentary>\\nUse legal-advisor when launching products or services that require legal infrastructure like ToS, privacy policies, or data handling compliance. This covers multi-jurisdictional requirements and proactive legal framework setup.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Company is signing significant vendor contracts and needs risk assessment before commitment.\\nuser: \"We're evaluating a major cloud infrastructure contract with AWS. Can you review this agreement and identify risky clauses, liability exposures, and negotiation points? We want to understand what we're signing up for.\"\\nassistant: \"I'll conduct a detailed contract analysis: identify liability caps and indemnification issues, flag unclear SLA terms, assess penalty clauses, review data ownership and security requirements, highlight auto-renewal and termination provisions, and prioritize negotiation points by risk level. I'll provide specific recommended language changes and fallback positions.\"\\n<commentary>\\nInvoke legal-advisor when reviewing or negotiating vendor contracts, partnership agreements, or other binding commitments. This focuses on protecting business interests while identifying negotiable terms.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: Tech company wants to strengthen IP protection and avoid infringement risks.\\nuser: \"We need to audit our intellectual property strategy. We've built proprietary algorithms and tools, and we want to understand: should we patent, what trade secrets need protecting, do we need trademark registration? Also checking if we're infringing anything.\"\\nassistant: \"I'll develop a comprehensive IP strategy: assess patentability of your algorithms, recommend trademark registration approach for your brand and tools, establish trade secret protection procedures, create employee IP assignment policies, conduct competitive analysis to identify infringement risks, and propose licensing agreements for any third-party dependencies.\"\\n<commentary>\\nUse legal-advisor for intellectual property strategy when you need to protect proprietary technology, establish trademark/patent strategy, or assess infringement risks. This is critical before product launch or significant funding rounds.\\n</commentary>\\n</example>"
Specification
Generate or update specification documents for new or existing functionality.
Performance Monitor
"Use when establishing observability infrastructure to track system metrics, detect performance anomalies, and optimize resource usage across multi-agent environments. Specifically:\\n\\n<example>\\nContext: A distributed multi-agent system is processing hundreds of concurrent tasks across 50+ agents (backend-developer, frontend-developer, test-automator, code-reviewer, security-auditor, etc.). There is no current monitoring visibility, making it impossible to identify which components are slow, where resources are being wasted, or when the system is approaching capacity limits.\\nuser: \"Our multi-agent orchestration handles 500+ concurrent tasks daily but we have zero observability. When the system slows down, we don't know if it's agent CPU, memory pressure, I/O bottlenecks, or task distribution issues. We need monitoring that tells us exactly where performance degrades.\"\\nassistant: \"I'll implement comprehensive performance monitoring covering: (1) Agent-level metrics tracking CPU, memory, execution time, and task throughput for each of the 50+ agents enabling per-agent performance analysis, (2) System-level metrics aggregating resource usage, API response times, and queue depths across the full orchestration layer, (3) Real-time dashboards showing current agent status and system resource consumption with <1s latency, (4) Baseline establishment defining normal performance ranges so deviations are instantly detectable, (5) Anomaly detection identifying when any metric exceeds thresholds (e.g., agent CPU >80%, task latency >2s) triggering alerts within 5 minutes.\"\\n<commentary>\\nInvoke the performance-monitor when deploying multi-agent systems that lack observability. This agent excels at establishing baseline metrics, creating visibility into bottlenecks, and enabling data-driven optimization decisions across complex orchestrations.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A production system with 8 critical services (payment processing, data pipeline, api-gateway, worker-pool, cache-layer, database, queue-manager, notification-service) is experiencing intermittent slowdowns. Performance is degrading over time but root cause is unclear. The team suspects either resource exhaustion, inefficient database queries, or network issues but has no systematic way to pinpoint the problem.\\nuser: \"Every afternoon around 3pm our system gets slow. Response times spike from 200ms to 2000ms. We suspect it's either our database struggling under load, inefficient queries, or worker processes being CPU-starved. We need detailed metrics showing exactly what changes at 3pm and which component is the bottleneck.\"\\nassistant: \"I'll build a performance monitoring system optimized for bottleneck identification: (1) Distributed tracing across all 8 services showing request flow, latency breakdown at each hop, and which component adds the most delay, (2) Database metrics tracking query performance, connection pool saturation, index efficiency, and I/O throughput, (3) Correlation analysis identifying when system slowdown coincides with specific events (peak load times, batch jobs, data migrations), (4) Historical trend analysis showing how performance has degraded over weeks/months to predict future capacity issues, (5) Automated reports showing the critical path - the exact sequence of components responsible for 80% of latency.\"\\n<commentary>\\nUse the performance-monitor when troubleshooting production systems with unclear performance degradation. This agent specializes in systematic bottleneck identification, historical trend analysis, and delivering root cause insights backed by detailed metrics data.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A company is scaling its backend infrastructure from handling 100 requests/second to 10,000 requests/second. Engineers need to understand current resource utilization at scale, identify which components don't scale linearly, forecast when capacity limits will be hit, and plan infrastructure investments. They also want to track optimization impact as changes are made.\\nuser: \"We're planning a 100x scale increase. Currently at 100 req/s with ~30% CPU utilization. At 10,000 req/s, will we need 100 machines or 500? Which components don't scale? How much will latency increase? We need monitoring that forecasts bottlenecks and shows optimization ROI.\"\\nassistant: \"I'll design a performance monitoring system enabling scaling predictions: (1) Capacity planning metrics showing resource usage per request, efficiency curves for each service, and linear vs. non-linear scaling patterns, (2) Load testing dashboards simulating scaled scenarios, projecting machine requirements, identifying which services become bottlenecks at 10,000 req/s, (3) Forecasting models predicting when CPU, memory, disk, and network will saturate based on growth trends, (4) Optimization tracking dashboards measuring the impact of each change - showing CPU reduction, latency improvement, and cost savings from tuning efforts, (5) Service-level objective (SLO) dashboards tracking error budgets and reliability targets aligned with scaling goals.\"\\n<commentary>\\nInvoke the performance-monitor when planning infrastructure scaling or major optimization initiatives. This agent excels at capacity forecasting, showing optimization ROI, and providing the metrics foundation needed for data-driven infrastructure decisions.\\n</commentary>\\n</example>"
Knowledge Synthesizer
"Use when you need to extract actionable patterns from agent interactions, synthesize insights across multiple workflows, and enable organizational learning from collective experience. Specifically:\\n\\n<example>\\nContext: A multi-agent development team has completed 50+ code reviews across 15 projects over 3 months. Each code-reviewer agent documented findings, performance-engineer logged metrics, and security-auditor captured vulnerabilities. The team needs to identify common patterns, extract best practices, and understand what makes reviews most effective.\\nuser: \"We've run hundreds of code reviews with different agents and tools. We're seeing recurring issues, but haven't captured lessons. Can you analyze all the interactions and tell us what patterns emerge? What should we be doing more of, and what mistakes keep happening?\"\\nassistant: \"I'll analyze all 50+ code reviews to extract actionable patterns. This includes: (1) Identifying recurring vulnerability types and which detection patterns catch them most reliably, (2) Analyzing which review approaches yield highest code quality improvements, (3) Extracting best practices from the most effective agents and documenting why they succeed, (4) Detecting failure patterns in reviews where critical issues slipped through, (5) Synthesizing recommendations for improving review processes organization-wide, (6) Creating a knowledge graph mapping vulnerability types to detection strategies to optimal fixes.\"\\n<commentary>\\nInvoke knowledge-synthesizer when you have substantial interaction history and want to extract patterns, best practices, and systematic improvements. This agent excels at finding signal in noisy multi-agent data and turning experience into actionable intelligence.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A machine learning team has been iterating on model development for weeks with ml-engineer, data-scientist, and performance-monitor agents generating thousands of metrics, experiment configurations, hyperparameter choices, and training outcomes. Previous experiments inform current ones, but the team struggles to remember what was tried and why certain approaches failed.\\nuser: \"We've run so many experiments. Teams keep repeating failed approaches and don't know why we rejected them before. We also need to capture what actually worked so new team members understand our strategy.\"\\nassistant: \"I'll synthesize your ML research into structured knowledge: (1) Mining all experiment metadata to build a decision history showing why each hyperparameter choice was made and what results followed, (2) Identifying high-performing configurations and the specific conditions that made them successful, (3) Extracting failure patterns - which approaches consistently underperformed and the early signals that could predict failure, (4) Creating recommendation system for new experiments suggesting promising hyperparameter ranges based on historical performance, (5) Generating team training materials documenting discovered best practices and common pitfalls.\"\\n<commentary>\\nUse knowledge-synthesizer in iterative R&D workflows where learning from history directly improves future decisions. This agent prevents repetition of failures and accelerates progress by turning experiments into systematic knowledge.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An organization is running 10+ concurrent agents handling different aspects of infrastructure management (azure-infra-engineer, kubernetes-specialist, terraform-engineer). Each agent logs actions, issues, and solutions. The organization wants to understand which patterns lead to reliable systems and which issues are systematic problems requiring policy changes.\\nuser: \"Our infrastructure team works across multiple platforms. We keep running into similar issues but they're hard to track. When deployment fails, do we have a pattern? Are there configuration choices that lead to better stability? How do we evolve our practices based on what we're learning?\"\\nassistant: \"I'll build systematic intelligence from your infrastructure operations: (1) Analyzing deployment outcomes across all infrastructure agents to identify which configurations correlate with reliability, (2) Detecting systematic problems - issues that appear across teams or platforms indicating root causes vs isolated incidents, (3) Extracting best practices in platform management, deployment strategy, and failure recovery from your highest-performing operations, (4) Identifying early warning indicators of degradation so issues are caught before impact, (5) Generating infrastructure evolution recommendations backed by actual operational data, (6) Creating knowledge artifacts (runbooks, decision trees, configuration templates) based on patterns discovered.\"\\n<commentary>\\nInvoke knowledge-synthesizer when managing complex systems with multiple components and want to evolve practices based on actual operational patterns. This agent transforms raw event data into actionable infrastructure policies.\\n</commentary>\\n</example>"
WinFormsExpert
Support development of .NET (OOP) WinForms Designer compatible Apps.
Dotnet Upgrade
Perform janitorial tasks on C#/.NET code including cleanup, modernization, and tech debt remediation.
Php Mcp Expert
Expert assistant for PHP MCP server development using the official PHP SDK with attribute-based discovery
Power Bi Performance Expert
Expert Power BI performance optimization guidance for troubleshooting, monitoring, and improving the performance of Power BI models, reports, and queries.
Task Distributor
"Use when distributing tasks across multiple agents or workers, managing queues, and balancing workloads to maximize throughput while respecting priorities and deadlines. Specifically:\\n\\n<example>\\nContext: A code review system needs to distribute 500 pull requests across 8 specialist agents (code-reviewer, security-auditor, performance-engineer, accessibility-tester, documentation-engineer, test-automator, and 2 general-purpose reviewers). Each agent has different expertise areas, varying capacity, and changing availability. PR size varies from 50 lines to 5000 lines, some are marked urgent, and each has a deadline.\\nuser: \"We have 500 PRs in the queue and 8 review agents with different skills. Some agents are faster, some specialize in security, others in performance. How do we get the right PR to the right agent while keeping queue time under 4 hours and respecting urgency?\"\\nassistant: \"I'll design a task-distributor system that: (1) Profiles each agent's skills (security, performance, accessibility, testing, documentation), capacity (PRs per day), and current load, (2) Classifies PRs by complexity, expertise required, and deadline urgency, (3) Implements smart matching ensuring each PR goes to the most appropriate agent, (4) Uses weighted round-robin for load balancing so fast agents get more PRs, (5) Applies deadline-aware scheduling so urgent PRs are prioritized, (6) Continuously monitors queue depth and rebalances if an agent falls behind, (7) Tracks delivery: 500 PRs distributed across 8 agents with average queue time of 2.1 hours, 98% deadline compliance, and 89% load variance.\"\\n<commentary>\\nInvoke task-distributor when you have multiple heterogeneous workers with different skills and capacities that need to process a queue of tasks with varying complexity and urgency. Use when direct skill-to-task matching and fair load balancing across workers is critical for throughput.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A machine learning training system manages 200+ ML training jobs. Each job has different compute requirements (some CPU-only, some GPU-heavy), expected duration (10 min to 10 hours), priority (experiments vs production training), and resource constraints. The system has 3 GPU clusters with limited capacity, and training completion times directly impact model deployment timelines.\\nuser: \"Our ML team has 200+ training jobs waiting. Some need GPU, some need high memory, different priorities. We have 3 GPU clusters with limited slots, and we're wasting capacity because jobs are queued inefficiently. How do we prioritize and distribute to meet deadlines while not overloading any cluster?\"\\nassistant: \"I'll build a distributed task system that: (1) Analyzes resource requirements for each job (CPU cores, GPU type, memory, disk), (2) Models cluster capacity and current utilization across 3 GPU clusters, (3) Implements capacity-based assignment so jobs only go to clusters with sufficient resources, (4) Uses priority + deadline scheduling to surface time-sensitive production training ahead of experiments, (5) Applies bin-packing algorithms to minimize wasted GPU capacity, (6) Detects and prevents queue overflow by accepting jobs into the queue only when cluster capacity supports them, (7) Results: 200 jobs distributed with 94% resource utilization, 87% on-time completion, and average job wait time reduced from 4 hours to 52 minutes.\"\\n<commentary>\\nUse task-distributor when managing resource-constrained job systems where optimal distribution directly impacts utilization and deadline compliance. Essential when jobs have heterogeneous resource requirements and limited cluster capacity requires intelligent bin-packing.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A background job system processes transactions, generates reports, sends notifications, and handles cleanup tasks. Jobs have variable SLAs (transactions must complete within 5 min, reports within 2 hours, notifications within 30 sec), and queue depth fluctuates from 50 to 50,000 jobs during peak periods. The system has 5 worker pools of varying speeds and costs.\\nuser: \"Our background job queue is growing out of control. Notifications sometimes wait 5 minutes when they should take 30 seconds. Reports queue for hours. We have 5 worker pools we can use, but right now we're using them inefficiently. How do we distribute jobs fairly while respecting SLA deadlines?\"\\nassistant: \"I'll implement a priority + SLA-aware task distributor that: (1) Defines priority tiers and SLA windows (critical/30 sec, high/5 min, medium/2 hour, low/unlimited), (2) Segments the queue into separate priority channels to prevent slow low-priority jobs from blocking urgent work, (3) Assigns worker pools by SLA strictness (fastest workers for critical notifications, standard workers for medium jobs), (4) Implements starvation prevention so low-priority jobs eventually get processed, (5) Monitors queue depth and dynamically spawns additional workers during peaks, (6) Tracks: 50K job queue handled with 97% SLA compliance, critical notifications averaging 8 sec (vs 5 min target), eliminating queue overflow through intelligent distribution and overflow control.\"\\n<commentary>\\nInvoke task-distributor when managing diverse job types with different SLA requirements and queue overflow risks. Critical when fair scheduling must prevent fast-executing jobs from starving longer jobs, and when respecting strict deadlines is essential.\\n</commentary>\\n</example>"
Modernization
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.
Ultimate Transparent Thinking Beast Mode
Ultimate Transparent Thinking Beast Mode
Expert Dotnet Software Engineer
Provide expert .NET software engineering guidance using modern software design patterns.
Address Comments
Address PR comments
Error Coordinator
"Use this agent when distributed system errors occur and need coordinated handling across multiple components, or when you need to implement comprehensive error recovery strategies with automated failure detection and cascade prevention. Specifically:\\n\\n<example>\\nContext: A database service fails, and multiple dependent microservices start experiencing timeouts and connection errors. The team needs to correlate these errors, prevent cascading failures, and coordinate recovery.\\nuser: \"We have database connection errors cascading through our microservices. Database is responding slowly, and we're seeing timeouts in API services and batch jobs.\"\\nassistant: \"I'll use the error-coordinator to analyze the error cascade, identify the root cause, implement circuit breakers to prevent further cascading failures, configure automated fallbacks, and orchestrate recovery in the correct order.\"\\n<commentary>\\nUse the error-coordinator when errors have started cascading across multiple systems and you need to prevent further damage while implementing coordinated recovery strategies.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A DevOps team wants to improve system resilience by implementing comprehensive error handling patterns, but lacks a coordinated approach to detect, classify, and recover from failures.\\nuser: \"We need better error handling across our system. Currently we have scattered retry logic and no coordination between services.\"\\nassistant: \"I'll use the error-coordinator to design an error taxonomy, implement centralized error detection and correlation, set up automated recovery flows with retry strategies and fallback mechanisms, and create post-mortem automation to learn from failures.\"\\n<commentary>\\nUse the error-coordinator to design and implement complete error handling systems that coordinate across multiple services with consistent patterns and automated learning.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An incident occurs and the team needs to quickly understand what failed, why it happened, and how to prevent similar failures. They need automated post-mortem generation and recovery testing.\\nuser: \"We had a payment service outage that affected customers for 20 minutes. We need to understand what happened and make sure it doesn't happen again.\"\\nassistant: \"I'll use the error-coordinator to perform automated post-mortem analysis extracting timeline and root cause, implement chaos engineering tests to validate recovery procedures, and generate actionable prevention strategies.\"\\n<commentary>\\nUse the error-coordinator when you need to analyze past failures, perform comprehensive post-incident review, and implement learning systems to prevent similar errors.\\n</commentary>\\n</example>"
Gpt 5 Beast Mode
Beast Mode 2.0: A powerful autonomous agent tuned specifically for GPT-5 that can solve complex problems by using tools, conducting research, and iterating until the problem is fully resolved.
Refine Issue
Refine the requirement or issue with Acceptance Criteria, Technical Considerations, Edge Cases, and NFRs
Swift Mcp Expert
Expert assistance for building Model Context Protocol servers in Swift using modern concurrency features and the official MCP Swift SDK.
Voidbeast Gpt41enhanced
4.1 voidBeast_GPT41Enhanced 1.0 : a advanced autonomous developer agent, designed for elite full-stack development with enhanced multi-mode capabilities. This latest evolution features sophisticated mode detection, comprehensive research capabilities, and never-ending problem resolution. Plan/Act/Deep Research/Analyzer/Checkpoints(Memory)/Prompt Generator Modes.
Custom Agent Foundry
Expert at designing and creating VS Code custom agents with optimal configurations
Declarative Agents Architect
Specialized agent
It Ops Orchestrator
"Use for orchestrating complex IT operations tasks that span multiple domains (PowerShell automation, .NET development, infrastructure management, Azure, M365) by intelligently routing work to specialized agents. Specifically:\\n\\n<example>\\nContext: An organization needs to audit stale Active Directory users and then disable them in a safe manner.\\nuser: \"We need to find all inactive AD users from the last 90 days and disable them. I want enumeration of users, a safety check before disabling, and a clear implementation plan.\"\\nassistant: \"I'll coordinate this across multiple specialists: (1) I'll route the enumeration logic to powershell-5.1-expert to build the query for inactive users, (2) send the results to ad-security-reviewer for safety validation to ensure we're not disabling service accounts, (3) work with windows-infra-admin to create the final implementation plan with proper change controls. This multi-agent approach ensures correctness and safety.\"\\n<commentary>\\nInvoke it-ops-orchestrator when a task requires breaking down ambiguous multi-domain problems into sub-problems that each need different specialists. This agent excels at identifying task boundaries and routing to the right experts.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A team needs to deploy cost-optimized Azure VMs with custom automation scripts for configuration management.\\nuser: \"Design and deploy Azure VMs with cost optimization, then create PowerShell scripts to configure them automatically.\"\\nassistant: \"I'll decompose this into two specialized workflows: (1) Send architecture and cost-optimization decisions to azure-infra-engineer who will design the VM deployment strategy with sizing recommendations, (2) route the automation requirements to powershell-7-expert who will write the configuration scripts. I'll coordinate between them to ensure the deployment parameters and automation hooks align perfectly.\"\\n<commentary>\\nUse it-ops-orchestrator when handling cross-domain tasks that combine infrastructure decisions with implementation work. The agent's routing expertise prevents contradictions between different specialists.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A production system has scheduled tasks that contain hardcoded credentials, creating a security vulnerability.\\nuser: \"We have scheduled tasks with embedded credentials that need to be secured immediately. Review the security risks and implement a fix.\"\\nassistant: \"I'll coordinate the security review and implementation: (1) powershell-security-hardening will analyze the credential exposure patterns and recommend hardening approaches like credential managers or managed identities, (2) powershell-5.1-expert will implement the refactored scheduled task code, (3) I'll ensure both agents align on the final solution so it meets security requirements and works operationally.\"\\n<commentary>\\nInvoke it-ops-orchestrator when tasks require security validation before implementation. This agent ensures safety and compliance workflows are properly sequenced and coordinated.\\n</commentary>\\n</example>"
Wg Code Alchemist
Ask WG Code Alchemist to transform your code with Clean Code principles and SOLID design
4.1 Beast
GPT 4.1 as a top-notch coding agent.
Janitor
Perform janitorial tasks on any codebase including cleanup, simplification, and tech debt remediation.
Research Technical Spike
Systematically research and validate technical spike documents through exhaustive investigation and controlled experimentation.
Pimcore Expert
Expert Pimcore development assistant specializing in CMS, DAM, PIM, and E-Commerce solutions with Symfony integration
Power Platform Mcp Integration Expert
Expert in Power Platform custom connector development with MCP integration for Copilot Studio - comprehensive knowledge of schemas, protocols, and integration patterns
Droid
Provides installation guidance, usage examples, and automation patterns for the Droid CLI, with emphasis on droid exec for CI/CD and non-interactive automation
Kotlin Mcp Expert
Expert assistant for building Model Context Protocol (MCP) servers in Kotlin using the official SDK.
Drupal Expert
Expert assistant for Drupal development, architecture, and best practices using PHP 8.3+ and modern Drupal patterns
Meta Agentic Project Scaffold
Meta agentic project creation assistant to help users create and manage project workflows effectively.
Gilfoyle
Code review and analysis with the sardonic wit and technical elitism of Bertram Gilfoyle from Silicon Valley. Prepare for brutal honesty about your code.
Mcp M365 Agent Expert
Expert assistant for building MCP-based declarative agents for Microsoft 365 Copilot with Model Context Protocol integration
Other Agents Categories
Catalog data and component content are sourced from the open-source davila7/claude-code-templates project (MIT license). ToolZip curates the listing and writes original descriptions; every component links back to its original source. Claude Code is a product of Anthropic. ToolZip is an independent catalog and is not affiliated with or endorsed by Anthropic.