rale

name	rale
description	Use when working with Reactive Agentic Loop Engineer MCP Server, including persisted prompt loops, agent-card registration, capacity discovery, capacity-fit master-plan decomposition, dispatch gates, pause/resume lifecycle, completion, and SQLite-backed audit state.

RALE MCP Server

Use this skill when an agent needs to operate or extend the Reactive Agentic Loop Engineer MCP server.

Workflow

1. Create a loop with rale_create_loop and a conservative tokenLimit.
2. Treat tokenLimit as a hard prompt-length ceiling. RALE must never emit a goal whose Prompt.Length exceeds that limit.
3. List or inspect state with rale_get_loop and rale_list_goals.
4. Claim a goal with rale_claim_next_goal before executing it. Only one executor should succeed because claims use optimistic concurrency.
5. Pause or resume active work with rale_pause_goal and rale_resume_goal when work must stop before final completion.
6. Complete work with rale_complete_goal; include result metadata that helps reconstruct decisions and downstream dependencies.

Multi-Agent Orchestration Workflow

1. Register agents with rale_register_agent, including capabilities, supported task types, max concurrent goals, max token capacity, trust level, endpoint, and tool scopes.
2. Use rale_discover_agent_capacity before planning or when a task profile changes. RALE queries GET /agents/{id}/capacity?taskProfile=... when an endpoint is configured and falls back to cached/profile capacity when needed.
3. Create capacity-fit plans with rale_create_master_plan. Choose serial when generated subtasks must run in dependency order and parallel when they can run independently.
4. Dispatch with rale_assign_next_task. Assignment enforces dependencies, agent load, policy state, and human approval gates.
5. Clear human gates with rale_approve_goal; policy violations remain visible on each goal for audit.
6. Record liveness/provenance with rale_record_goal_heartbeat while agents iterate.
7. Use rale_resplit_goal when an agent reports capacity mismatch. Re-splitting is bounded by the goal iteration limit and records audit events.

Operational Rules

• Keep MCP transport on stdio and logs on stderr.
• Store intermediate outputs in GoalResults; rely on LoopEvents for audit history.
• Split prompts early when accounting is uncertain.
• Sanitize prompts and restrict tool scope before delegating goal execution.
• Treat agent endpoints as open-world calls. Use cached capacity and human approval gates when discovery fails or trust/tool-scope checks are not satisfied.
• Prefer TUnit tests and TUnit assertions when changing decomposition, goal status transitions, or persistence behavior.