reactive-multi-agent

name	reactive-multi-agent
description	Use when orchestrating work with CP.Reactive.Multi.Agent.MCP.Server, including creating durable multi-agent sessions, selecting specialist agents, spawning meaningful named sub-agents, recording checkpoints/results/heartbeats, applying recovery policies, finalizing sessions, and closing completed sub-agents.

Reactive Multi Agent MCP Server

Use this skill when the Reactive Multi Agent MCP server is available and the task benefits from durable, dependency-aware specialist orchestration.

Core Workflow

1. Discover specialists when needed with multiagent_agent_catalog_list, multiagent_agent_catalog_search, or multiagent_agent_catalog_get.
2. Keep the orchestration/control-plane role on GPT-5.5 or an equivalent highest-capacity model. Use smaller agents only for bounded specialist worker tasks after the session exists.
3. Create a session with multiagent_orchestrate_request. If the host surfaces creation tools more reliably than orchestration tools, call multiagent_create_session; it is an explicit alias with the same parameters and payload. This is the required first write tool before any worker tool. Pass the user request, constraints, desired artifacts, preferred agents, and a sensible maxParallelAgents.
4. Capture the returned sessionId, plan.tasks[*].taskId, plan.tasks[*].agentId, and plan.tasks[*].agentToolName; every worker tool call requires the relevant sessionId and taskId.
5. Read session.plan.executionWaves and work through waves by ascending phaseOrder.
6. Within one wave, dispatch ready independent tasks in parallel when the host supports parallel tool calls.
7. For each task, call the assigned worker tool to get an AgentTaskPacket.
8. Spawn or continue the sub-agent using agentName as the visible name and agentSessionId as the stable correlation id.
9. Give the sub-agent the packet executionPrompt, acceptanceCriteria, suggestedSkills, suggestedTools, artifact schema hint, and current nextSteps.
10. During long work, record progress with multiagent_record_heartbeat.
11. When the sub-agent has output, call the same worker tool with workSummary, artifacts, handoffItems, risks, and markComplete: true.
12. If the returned packet has shutdownRequired: true, close that named sub-agent immediately. Do not continue work in that agent context.
13. Periodically call multiagent_supervisor_plan and multiagent_get_maintenance_report.
14. When all required work is complete, call multiagent_finalize_session and use the unified response.

If a worker tool is available but no orchestration session exists yet, do not invent a sessionId. Call multiagent_orchestrate_request or multiagent_create_session first, then use the returned task metadata to call the worker tool.

Agent Lifecycle Rules

• Always use the server-provided agentName when spawning or displaying a sub-agent.
• Keep agentSessionId with all notes, checkpoints, handoffs, and close operations for that specialist task.
• Treat lifecycleInstruction as authoritative.
• If shutdownRequired is false, spawn or continue the named sub-agent.
• If shutdownRequired is true, close the named sub-agent and release the session. Start a fresh sub-agent only after a new task packet asks for it.
• Never reuse a completed sub-agent for unrelated work.
• Preserve task boundaries. Each specialist should produce artifacts and handoffs only for its assigned objective.

Worker Tools

Use the worker tool matching the task agentId:

• architect: multiagent_architect_agent
• csharp: multiagent_csharp_agent
• reactive: multiagent_reactive_agent
• reactiveui: multiagent_reactiveui_agent
• mcp: multiagent_mcp_agent
• ci: multiagent_ci_agent
• docs: multiagent_docs_agent
• migration: multiagent_migration_agent
• wpf: multiagent_wpf_agent
• winforms: multiagent_winforms_agent
• avalonia: multiagent_avalonia_agent
• maui: multiagent_maui_agent
• blazor: multiagent_blazor_agent
• tester: multiagent_test_agent
• reviewer: multiagent_reviewer_agent

Every worker tool supports the same operational pattern: activate task context, submit progress, record artifacts and handoffs, create checkpoints, report failures, apply completion, and return an updated AgentTaskPacket.

Recovery

Use the worker tool with createCheckpoint: true and checkpointSummary when a sub-agent needs to pause or preserve state. Include memoryReloadItems, currentEstimatedTokens, and remainingSubscriptionTokens when known.

Report failures through the worker tool with:

• failureKind: ContextWindowLimit
• failureKind: NetworkLoss
• failureKind: TokenBudgetLow
• failureKind: SubscriptionTokensExhausted
• failureKind: Unknown

Then call multiagent_apply_automatic_policy or use multiagent_supervisor_plan with autoApplyPolicies: true when the host should apply checkpoint, retry, or resume guidance. Use multiagent_resume_task to continue a checkpointed task and multiagent_resume_orchestration when the orchestration-level resume state requires attention.

Supervision And Maintenance

Use:

• multiagent_session_status to inspect session state, ready tasks, blocked tasks, and supervisor status.
• multiagent_record_heartbeat to keep long-running tasks and supervisor actions alive.
• multiagent_update_supervisor_action to acknowledge or complete supervisor actions.
• multiagent_apply_supervisor_action_escalation to escalate stale actions.
• multiagent_run_maintenance_sweep for an explicit diagnostic pass.
• multiagent_get_maintenance_report for trend-aware health output and optional automatic policy application.
• multiagent_get_maintenance_history to review prior reports.
• multiagent_search_sessions to find earlier sessions.

Resources And Prompts

Use resources for read-only context:

• multiagent://catalog
• multiagent://session/{sessionId}
• multiagent://history/recent
• multiagent://architecture/hub-and-spoke
• multiagent://schemas/artifacts

Use prompts for guided orchestration:

• create_multi_agent_plan
• create_specialist_agent_prompt
• merge_multi_agent_results

Result Shape

Return structured AgentArtifact objects with artifactId, kind, title, summary, and optional filePath, uri, mediaType, and content.

Return HandoffItem objects with itemId, category, title, details, and isBlocking.

For tests in this repository, use TUnit and TUnit assertions. When the Mtpunittestmcp MCP server is available, use it to run or inspect TUnit coverage.

Safe Error Handling

The server returns safe JSON error envelopes for tool failures with ok: false, operation, error.type, error.message, and guidance. Treat those as actionable responses rather than transport failures. Ask the user for missing required values when the safe error reports blank input.

Packaging

The MCP server package ships this skill at skills/Reactive-Multi-Agent/SKILL.md along with README.md and .mcp/server.json.