
Failure Recovery
- 87 installs
- 153 repo stars
- Updated June 9, 2026
- owl-listener/ai-design-skills
Helps with ai & agent building tasks during AI-assisted development.
About
failure-recovery is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- failure-recovery
- AI & Agent Building
- AI-coding skill
Failure Recovery by the numbers
- 87 all-time installs (skills.sh)
- +4 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #4,949 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/owl-listener/ai-design-skills --skill failure-recoveryAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 87 |
|---|---|
| repo stars | ★ 153 |
| Last updated | June 9, 2026 |
| Repository | owl-listener/ai-design-skills ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
Failure Recovery
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.
Failure Types in Multi-Agent Systems
- Agent failure: A single agent crashes, times out, or produces invalid output
- Handoff failure: Context is lost or corrupted during transfer between agents
- Coordination failure: Agents conflict, deadlock, or produce inconsistent results
- Resource failure: External tools, APIs, or data sources are unavailable
- Cascading failure: One agent's failure causes downstream agents to fail
Recovery Strategies
- Retry: Try the same operation again. Works for transient errors (network timeouts, rate limits). Set a retry limit to avoid infinite loops.
- Fallback: Switch to an alternative approach. A different agent, a simpler method, or a cached result.
- Escalation: Pass the problem to a more capable agent or to a human. Used when the failure is beyond the current agent's ability to resolve.
- Graceful degradation: Deliver a partial result rather than nothing. Tell the user what worked and what didn't.
- Compensation: Undo the effects of a partially completed workflow before retrying or escalating.
Designing Recovery Paths
For each point in the workflow where failure is possible:
- What could fail? List the failure modes
- What's the first recovery strategy? Usually retry for transient errors
- What's the fallback? If retry fails, what's the alternative?
- When do you escalate? After how many retries or what type of failure?
- What does the user see? Transparent about the failure or silently recovered?
- What's the worst case? If all recovery fails, what's the graceful degradation?
User Experience of Failures
- Invisible recovery: The system retries or falls back without the user noticing. Best for minor, quickly resolved failures.
- Transparent recovery: The system tells the user something went wrong and how it's handling it. "This is taking longer than usual — trying an alternative approach."
- Participatory recovery: The system asks the user to help. "I couldn't access your calendar. Can you check the connection?"
- Honest failure: The system tells the user it can't complete the task and explains why. Offers alternatives.
Design Artefacts
- Failure mode inventory per agent and per handoff
- Recovery strategy specifications (retry limits, fallback paths, escalation triggers)
- Cascading failure analysis
- User experience specifications for each failure scenario
- Recovery testing protocols
Related skills
AI & Agent Buildingagents