error-recovery
Strategies for handling subagent failures with retry logic and escalation patterns.
Best use case
error-recovery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Strategies for handling subagent failures with retry logic and escalation patterns.
Teams using error-recovery should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/error-recovery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How error-recovery Compares
| Feature / Agent | error-recovery | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Strategies for handling subagent failures with retry logic and escalation patterns.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Error Recovery Skill
Pattern for handling subagent failures gracefully with appropriate retry strategies.
## When to Load This Skill
- You are spawning subagents that may fail
- A subagent returned an error or unexpected output
- You need to decide whether to retry, escalate, or abort
## Failure Categories
| Category | Symptoms | Strategy |
|----------|----------|----------|
| **Transient** | Timeout, malformed output, parsing error | Simple Retry |
| **Context Gap** | "I don't have enough information", unclear task | Context Enhancement |
| **Complexity** | Partial completion, scope creep, tangents | Scope Reduction |
| **Boundary/Contract** | `status: blocked`, boundary_violation, contract_change | Escalation |
| **Fatal** | Repeated failures (3+), fundamental misunderstanding | Abort with Report |
## Retry Strategies
### Strategy 1: Simple Retry
For transient failures. Same prompt, up to 3 attempts.
```
# Track attempts
attempts: 0
max_attempts: 3
# On failure
IF attempts < max_attempts:
attempts += 1
Task(same_subagent_type, same_model, same_prompt)
ELSE:
Mark as FAILED, move on
```
**Use when:**
- Output was malformed or truncated
- Timeout occurred
- Agent returned empty/null response
### Strategy 2: Context Enhancement
Add more information to help the agent succeed.
```
Task(
subagent_type: "implementer",
model: "sonnet",
prompt: |
## PREVIOUS ATTEMPT FAILED
Error: {error_message}
Output received: {partial_output}
## ADDITIONAL CONTEXT
Here is more information that may help:
- Related file: @{additional_file_path}
- Pattern to follow: {example_pattern}
- Specific guidance: {clarification}
## ORIGINAL TASK
{original_task_description}
Output to: {output_path}
)
```
**Use when:**
- Agent said "I don't understand" or "unclear requirements"
- Agent made incorrect assumptions
- Agent asked questions in output
**Context to add:**
- Related code files the agent might need
- Similar implementations as examples
- Explicit clarification of ambiguous points
- Error message from previous attempt
### Strategy 3: Scope Reduction
Break the failing task into smaller, more manageable pieces.
```
# Original task failed
Task: "Implement full authentication system"
# Split into subtasks
Task(implementer, "Implement password hashing utility")
Task(implementer, "Implement session token generation")
Task(implementer, "Implement login endpoint")
Task(implementer, "Implement logout endpoint")
```
**Use when:**
- Agent completed partial work then failed
- Task description was too broad
- Agent went off on tangents
- Output shows confusion about scope
**Splitting guidelines:**
- Each subtask should be independently completable
- Each subtask should have clear boundaries
- Subtasks can run in parallel if no dependencies
- Recombine outputs after all subtasks complete
### Strategy 4: Escalation
Route to specialized agent for resolution.
```
# For boundary violations
Task(
subagent_type: "contract-resolver",
model: "sonnet",
prompt: |
A task is blocked due to boundary/contract issues.
Blocked task output: memory/tasks/{task_id}/output.json
Blocked reason: {blocked_reason}
Current contracts: {contract_paths}
Analyze impact and provide resolution.
Output to: memory/contracts/resolution_{task_id}.json
)
```
**Escalation paths:**
| Failure Type | Escalate To | Action |
|--------------|-------------|--------|
| `blocked_reason: boundary_violation` | contract-resolver | Expand boundaries or redesign |
| `blocked_reason: contract_change` | contract-resolver | Modify contract, re-verify dependents |
| `blocked_reason: dependency_issue` | executor (self) | Re-check dependency status |
| Repeated implementation failures | architect | Reconsider design approach |
### Strategy 5: Abort with Report
When recovery is not possible, fail gracefully.
```json
{"tasks":[{"id":"{task_id}","status":"failed","failure_reason":"{specific reason}","attempts_made":3,"recovery_attempted":[{"strategy":"simple_retry","result":"same_error"},{"strategy":"context_enhancement","result":"different_error"},{"strategy":"scope_reduction","result":"subtasks_also_failed"}],"recommendation":"Task may need architectural redesign"}]}
```
**Use when:**
- 3+ retry attempts failed
- Different strategies all failed
- Fundamental misunderstanding of requirements
- Task is actually impossible given constraints
## Decision Tree
```
On Subagent Failure:
│
├─ Is output malformed/empty/timeout?
│ └─ YES → Strategy 1: Simple Retry (up to 3x)
│
├─ Did agent say "unclear" or ask questions?
│ └─ YES → Strategy 2: Context Enhancement
│
├─ Did agent complete partial work?
│ └─ YES → Strategy 3: Scope Reduction
│
├─ Is status "blocked" with boundary/contract reason?
│ └─ YES → Strategy 4: Escalation to contract-resolver
│
├─ Have we tried 3+ strategies already?
│ └─ YES → Strategy 5: Abort with Report
│
└─ Unknown error
└─ Try Strategy 2 first, then escalate
```
## Retry State Tracking
Track retry attempts in the execution state file:
```json
{"tasks":[{"id":"task-001","status":"running","attempts":2,"last_error":"Timeout after 120s","retry_strategy":"simple_retry"},{"id":"task-002","status":"running","attempts":1,"last_error":"Needs access to src/config/db.ts","retry_strategy":"context_enhancement","context_added":["src/config/db.ts","src/types/config.ts"]}]}
```
## Integration with Executor Loop
```
# Enhanced execution loop
WHILE tasks remain incomplete:
1. Read state file
2. Find ready tasks
3. Spawn ready tasks
4. Check completed tasks:
FOR each completed task:
IF status == pre_complete:
spawn verifier
ELIF status == blocked:
apply Strategy 4 (Escalation)
ELIF status == failed:
determine_failure_category()
apply_appropriate_strategy()
update_retry_state()
5. Update state file
6. IF all verified: EXIT
7. IF all failed with no recovery: EXIT with failure report
```
## Principles
1. **Fail fast, recover smart** - Don't retry blindly; analyze the failure first
2. **Preserve partial work** - If agent completed 50%, don't discard it
3. **Escalate early** - Boundary/contract issues need resolver, not retries
4. **Track everything** - Log all attempts for reflection phase
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