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From design-agent-orchestration
Guides failure recovery design for multi-agent systems, covering types like agent crashes and handoffs, plus strategies: retry, fallback, escalation, graceful degradation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/design-agent-orchestration:failure-recoveryThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Agents fail. Networks time out, models hallucinate, tools error, and edge cases surprise. Failure recovery design determines whether a failure becomes a dead end or a graceful detour.
Agents fail. Networks time out, models hallucinate, tools error, and edge cases surprise. Failure recovery design determines whether a failure becomes a dead end or a graceful detour.
For each point in the workflow where failure is possible:
npx claudepluginhub owl-listener/ai-design-skills --plugin design-agent-orchestrationImplements circuit breaker logic for agentic tool calls: tracking tool health, transitioning states, reducing scope on failure, and routing to alternatives via capability maps. Use when building fault-tolerant agents with multiple tools.
Provides a structured four-phase workflow for AI agents to self-debug failures including capture, diagnosis, contained recovery, and introspection reports. Useful for loop-limit errors, repeated retries, context drift, and state mismatches.
Guides building reliable autonomous AI agents with ReAct/Plan-Execute loops, reflection patterns, goal decomposition, and frameworks like LangGraph/CrewAI. For production agent reliability.