Error Recovery

Error Recovery

Version: 1.2.0 Purpose: Patterns for handling failures in multi-agent workflows Status: Production Ready

Overview

Error recovery is the practice of handling failures gracefully in multi-agent workflows, ensuring that temporary errors, timeouts, or partial failures don't derail entire workflows. In production systems with external dependencies (AI models, APIs, network calls), failures are inevitable. The question is not "will it fail?" but "how will we handle it when it does?"

This skill provides battle-tested patterns for:

• User escalation (STOP and report before fallback — DEFAULT)
• Timeout handling (external models taking >30s)
• API failure recovery (401, 500, network errors)
• Partial success strategies (some agents succeed, others fail)
• User cancellation (graceful Ctrl+C handling)
• Missing tools (claudish not installed)
• Out of credits (payment/quota errors)
• Retry strategies (exponential backoff, max retries)

With proper error recovery, workflows become resilient and production-ready.

Core Patterns

Pattern 0: User Escalation (DEFAULT — Read First)

This is the MOST IMPORTANT pattern. It overrides all other patterns when the user has requested a specific model.

Rule: NEVER silently substitute, retry with a different model, or fall back to embedded Claude when the user requested a specific model. STOP and REPORT the failure first.

When this applies:

• User explicitly requested a model (e.g., "use Gemini to redesign X")
• User selected specific models for a task
• Any workflow where the model choice was intentional

When graceful degradation (Patterns 1-7) applies instead:

• Automated pipelines where any result > no result
• User explicitly said "use whatever works"
• /team workflows where the command already handles failure reporting

The Protocol:

Step 1: Model fails (non-zero exit, empty output, API error, rate limit, binary crash)

Step 2: STOP immediately. Do NOT:
  ❌ Silently launch a different model
  ❌ Retry with a different provider prefix
  ❌ Fall back to embedded Claude without asking
  ❌ Run `claudish --top-models` or invent model IDs — use the live catalog (`list_models`) instead
  ❌ Substitute a "similar" model

Step 3: REPORT to the user with:
  - What model was requested
  - What happened (exact error message)
  - How many attempts were made and what was tried
  - Actionable options for the user to choose from

Step 4: WAIT for user's decision before proceeding

Report Template:

"{Model Name} failed — {error category}.

What happened:
1. Attempt 1: {what was tried} — {exact error}
2. Attempt 2: {what was tried} — {exact error}

Options:
(1) {Fix and retry} — {specific fix description}
(2) Use a different model — {suggest alternatives if known}
(3) Skip this model and continue without it
(4) Cancel the workflow
(5) Report this error to claudish developers

Which do you prefer?"

Why this matters: When a user says "use Gemini", they've made a deliberate choice — for its 1M context, its reasoning style, or for model diversity. Silently substituting GPT-5 defeats the purpose. The user should always be in control of model selection decisions.

Pattern 1: Timeout Handling

Scenario: External Model Takes >30s

External AI models via Claudish may take >30s due to:

• Model service overloaded (high demand)
• Network latency (slow connection)
• Complex task (large input, detailed analysis)
• Model thinking time (GPT-5, Grok reasoning models)

Detection:

Monitor execution time and set timeout limits:

const TIMEOUT_THRESHOLD = 30000; // 30 seconds

startTime = Date.now();
executeClaudish(model, prompt);

setInterval(() => {
  elapsedTime = Date.now() - startTime;
  if (elapsedTime > TIMEOUT_THRESHOLD && !modelResponded) {
    handleTimeout();
  }
}, 1000);

Recovery Strategy:

Step 1: Detect Timeout
  Log: "Timeout: grok after 30s with no response"

Step 2: Notify User
  Present options:
    "Model 'Grok' timed out after 30 seconds.
     Options:
     1. Retry with 60s timeout
     2. Skip this model and continue with others
     3. Cancel entire workflow

     What would you like to do? (1/2/3)"

Step 3a: User selects RETRY
  Increase timeout to 60s
  Re-execute claudish with longer timeout
  If still times out: Offer skip or cancel

Step 3b: User selects SKIP
  Log: "Skipping Grok review due to timeout"
  Mark this model as failed
  Continue with remaining models
  (Graceful degradation pattern)

Step 3c: User selects CANCEL
  Exit workflow gracefully
  Save partial results (if any)
  Log cancellation reason

Graceful Degradation:

Multi-Model Review Example:

Requested: 5 models (Claude, Grok, Gemini, GPT-5, DeepSeek)
Timeout: Grok after 30s

Result:
  - Claude: Success ✓
  - Grok: Timeout ✗ (skipped)
  - Gemini: Success ✓
  - GPT-5: Success ✓
  - DeepSeek: Success ✓

Successful: 4/5 models (80%)
Threshold: N ≥ 2 for consolidation ✓

Action:
  Proceed with consolidation using 4 reviews
  Notify user: "4/5 models completed (Grok timeout). Proceeding with 4-model consensus."

Benefits:
  - Workflow completes despite failure
  - User gets results (4 models better than 1)
  - Timeout doesn't derail entire workflow

Example Implementation:

# Via create_session MCP tool (timeout handled by the tool)
create_session(model="grok", prompt=PROMPT, timeout_seconds=30)

# React to channel events:
# - completed → get_output(session_id) → process result
# - failed → inspect error content:
#   - Content contains "timeout" → timeout occurred
#   - Content contains "401" → API key issue
#   - Other → general failure

# Via team MCP tool. There is NO per-model timeout any more — the parameter was
# removed, and passing one is silently ignored. You bound the wait yourself by
# bounding the poll loop.
team(mode="run", path=SESSION_DIR, models=["grok"], input_file=INPUT_MD,
  require_pattern=<regex for the shape PROMPT mandates>)
team(mode="status", path=SESSION_DIR)   # poll until no slot has state === "RUNNING"
# Check per-model status in the SETTLED status response, not the run response — run
# returns before any model has answered. A slot reported EMPTY with reason
# shape_mismatch is a FAILURE to recover from, not a short answer to accept — the
# model finished without producing the required shape.
#
# Before treating a quiet slot as hung, read idle_seconds_by_slot together with
# activity_by_slot: 90s idle in "tool_executing" is a build or test suite and is
# normal; 90s idle in "running" is a model that stopped mid-answer.

Pattern 2: API Failure Recovery

Common API Failure Scenarios:

401 Unauthorized:
  - Invalid API key (OPENROUTER_API_KEY incorrect)
  - Expired API key
  - API key not set in environment

500 Internal Server Error:
  - Model service temporarily down
  - Server overload
  - Model deployment issue

Network Errors:
  - Connection timeout (network slow/unstable)
  - DNS resolution failure
  - Firewall blocking request

429 Too Many Requests:
  - Rate limit exceeded
  - Too many concurrent requests
  - Quota exhausted for time window

Recovery Strategies by Error Type:

401 Unauthorized:

Pattern 0 guard: If the user requested a specific model, apply Pattern 0 (stop and report with options) instead of auto-fallback. The fallback below applies only to automated pipelines or when the user pre-authorized graceful degradation.

Detection:
  API returns 401 status code

Recovery:
  1. Log: "API authentication failed (401)"
  2. Check if OPENROUTER_API_KEY is set:
     if [ -z "$OPENROUTER_API_KEY" ]; then
       notifyUser("OpenRouter API key not found. Set OPENROUTER_API_KEY in .env")
     else
       notifyUser("Invalid OpenRouter API key. Check .env file")
     fi
  3. Skip all external models
  4. Fallback to embedded Claude only
  5. Notify user:
     "⚠️ API authentication failed. Falling back to embedded Claude.
      To fix: Add valid OPENROUTER_API_KEY to .env file."

No retry (authentication won't fix itself)

500 Internal Server Error:

Detection:
  API returns 500 status code

Recovery:
  1. Log: "Model service error (500): grok"
  2. Wait 5 seconds (give service time to recover)
  3. Retry ONCE
  4. If retry succeeds: Continue normally
  5. If retry fails: Skip this model, continue with others

Example:
  try {
    result = await claudish(model, prompt);
  } catch (error) {
    if (error.status === 500) {
      log("500 error, waiting 5s before retry...");
      await sleep(5000);

      try {
        result = await claudish(model, prompt); // Retry
        log("Retry succeeded");
      } catch (retryError) {
        log("Retry failed, skipping model");
        skipModel(model);
        continueWithRemaining();
      }
    }
  }

Max retries: 1 (avoid long delays)

Network Errors:

Detection:
  - Connection timeout
  - ECONNREFUSED
  - ETIMEDOUT
  - DNS resolution failure

Recovery:
  Retry up to 3 times with exponential backoff:

  async function retryWithBackoff(fn, maxRetries = 3) {
    for (let i = 0; i < maxRetries; i++) {
      try {
        return await fn();
      } catch (error) {
        if (!isNetworkError(error)) throw error;  // Not retriable
        if (i === maxRetries - 1) throw error;     // Max retries reached

        const delay = Math.pow(2, i) * 1000;  // 1s, 2s, 4s
        log(`Network error, retrying in ${delay}ms (attempt ${i+1}/${maxRetries})`);
        await sleep(delay);
      }
    }
  }

  result = await retryWithBackoff(() => claudish(model, prompt));

Rationale: Network errors are often transient (temporary)

429 Rate Limiting:

Detection:
  API returns 429 status code
  Response may include Retry-After header

Recovery:
  1. Check Retry-After header (seconds to wait)
  2. If present: Wait for specified time
  3. If not present: Wait 60s (default)
  4. Retry ONCE after waiting
  5. If still rate limited: Skip model

Example:
  if (error.status === 429) {
    const retryAfter = error.headers['retry-after'] || 60;
    log(`Rate limited. Waiting ${retryAfter}s before retry...`);
    await sleep(retryAfter * 1000);

    try {
      result = await claudish(model, prompt);
    } catch (retryError) {
      log("Still rate limited after retry. Skipping model.");
      skipModel(model);
    }
  }

Note: Respect Retry-After header (avoid hammering API)

Graceful Degradation for All API Failures:

Pattern 0 guard: If the user requested specific models, apply Pattern 0 (stop and report with options) instead of auto-fallback. The fallback below applies only to automated pipelines or when the user pre-authorized graceful degradation.

Fallback Strategy:

If ALL external models fail (401, 500, network, etc.):
  1. Log all failures
  2. Notify user:
     "⚠️ All external models failed. Falling back to embedded Claude.
      Errors:
      - Grok: Network timeout
      - Gemini: 500 Internal Server Error
      - GPT-5: Rate limited (429)
      - DeepSeek: Authentication failed (401)

      Proceeding with Claude Sonnet (embedded) only."

  3. Run embedded Claude review
  4. Present results with disclaimer:
     "Review completed using Claude only (external models unavailable).
      For multi-model consensus, try again later."

Benefits:
  - User still gets results (better than nothing)
  - Workflow completes (not aborted)
  - Clear error communication (user knows what happened)

Pattern 3: Partial Success Strategies

Scenario: 2 of 4 Models Complete Successfully

In multi-model workflows, it's common for some models to succeed while others fail.

Tracking Success/Failure:

const results = await Promise.allSettled([
  Agent({ subagent: "reviewer", model: "claude" }),
  Agent({ subagent: "reviewer", model: "grok" }),
  Agent({ subagent: "reviewer", model: "gemini" }),
  Agent({ subagent: "reviewer", model: "gpt-5" })
]);

const successful = results.filter(r => r.status === 'fulfilled');
const failed = results.filter(r => r.status === 'rejected');

log(`Success: ${successful.length}/4`);
log(`Failed: ${failed.length}/4`);

Decision Logic:

If N ≥ 2 successful:
  → Proceed with consolidation
  → Use N reviews (not all 4)
  → Notify user about failures

If N < 2 successful:
  → Insufficient data for consensus
  → Offer user choice:
    1. Retry failures
    2. Abort workflow
    3. Proceed with embedded Claude only

Example:

successful.length = 2 (Claude, Gemini)
failed.length = 2 (Grok timeout, GPT-5 500 error)

Action:
  notifyUser("2/4 models completed successfully. Proceeding with consolidation using 2 reviews.");

  consolidateReviews([
    "ai-docs/claude-review.md",
    "ai-docs/gemini-review.md"
  ]);

  presentResults({
    totalModels: 4,
    successful: 2,
    failureReasons: {
      grok: "Timeout after 30s",
      gpt5: "500 Internal Server Error"
    }
  });

Communication Strategy:

Be transparent with user about partial success:

❌ WRONG:
  "Multi-model review complete!"
  (User assumes all 4 models ran)

✅ CORRECT:
  "Multi-model review complete (2/4 models succeeded).

   Successful:
   - Claude Sonnet ✓
   - Gemini 2.5 Flash ✓

   Failed:
   - Grok: Timeout after 30s
   - GPT-5 Codex: 500 Internal Server Error

   Proceeding with 2-model consensus.
   Top issues: [...]"

User knows:
  - What succeeded (Claude, Gemini)
  - What failed (Grok, GPT-5)
  - Why they failed (timeout, 500 error)
  - What action was taken (2-model consensus)

Consolidation Adapts to N Models:

Consolidation logic must handle variable N:

✅ CORRECT - Flexible N:
  function consolidateReviews(reviewFiles) {
    const N = reviewFiles.length;
    log(`Consolidating ${N} reviews`);

    // Consensus thresholds adapt to N
    const unanimousThreshold = N;           // All N agree
    const strongThreshold = Math.ceil(N * 0.67);  // 67%+ agree
    const majorityThreshold = Math.ceil(N * 0.5); // 50%+ agree

    // Apply consensus analysis with dynamic thresholds
    ...
  }

❌ WRONG - Hardcoded N:
  // Assumes always 4 models
  const unanimousThreshold = 4;  // Breaks if N = 2!

Pattern 4: User Cancellation Handling (Ctrl+C)

Scenario: User Presses Ctrl+C During Workflow

Users may cancel long-running workflows for various reasons:

• Taking too long
• Realized they want different configuration
• Accidentally triggered workflow
• Need to prioritize other work

Cleanup Strategy:

process.on('SIGINT', async () => {
  log("⚠️ User cancelled workflow (Ctrl+C)");

  // Step 1: Stop all running processes gracefully
  await stopAllAgents();

  // Step 2: Save partial results to files
  const partialResults = await collectPartialResults();
  await writeFile('ai-docs/partial-review.md', partialResults);

  // Step 3: Log what was completed vs cancelled
  log("Workflow cancelled");
  log("Completed:");
  log("  - PHASE 1: Requirements gathering ✓");
  log("  - PHASE 2: Architecture planning ✓");
  log("Cancelled:");
  log("  - PHASE 3: Implementation (in progress)");
  log("  - PHASE 4: Testing (not started)");
  log("  - PHASE 5: Review (not started)");

  // Step 4: Notify user
  console.log("\n⚠️ Workflow cancelled by user.");
  console.log("Partial results saved to ai-docs/partial-review.md");
  console.log("Completed phases: 2/5");

  // Step 5: Clean exit
  process.exit(0);
});

Save Partial Results:

Partial Results Format:

# Workflow Cancelled by User

**Status:** Cancelled during PHASE 3 (Implementation)
**Completed:** 2/5 phases (40%)
**Duration:** 8 minutes (of estimated 20 minutes)
**Timestamp:** 2025-11-22T14:30:00Z

## Completed Phases

### PHASE 1: Requirements Gathering ✓
- User requirements documented
- See: ai-docs/requirements.md

### PHASE 2: Architecture Planning ✓
- Architecture plan generated
- See: ai-docs/architecture-plan.md

## Cancelled Phases

### PHASE 3: Implementation (IN PROGRESS)
- Status: 30% complete
- Files created: src/auth.ts (partial)
- Files pending: src/routes.ts, src/services.ts

### PHASE 4: Testing (NOT STARTED)
- Pending: Test suite creation

### PHASE 5: Code Review (NOT STARTED)
- Pending: Multi-model review

## How to Resume

To resume from PHASE 3:
1. Review partial implementation in src/auth.ts
2. Complete remaining implementation
3. Continue with PHASE 4 (Testing)

Or restart workflow from beginning with updated requirements.

Resumable Workflows (Advanced):

Save workflow state for potential resume:

// During workflow execution
await saveWorkflowState({
  currentPhase: 3,
  totalPhases: 5,
  completedPhases: [1, 2],
  pendingPhases: [3, 4, 5],
  partialResults: {
    phase1: "ai-docs/requirements.md",
    phase2: "ai-docs/architecture-plan.md",
    phase3: "src/auth.ts (partial)"
  }
}, '.claude/workflow-state.json');

// On next invocation
const state = await loadWorkflowState('.claude/workflow-state.json');
if (state) {
  askUser("Found incomplete workflow from previous session. Resume? (Yes/No)");

  if (userSaysYes) {
    resumeFromPhase(state.currentPhase);
  } else {
    deleteWorkflowState();
    startFresh();
  }
}

Pattern 5: Claudish Not Installed

Pattern 0 guard: If the user requested a specific external model (e.g., "use Gemini"), apply Pattern 0 (stop and report with options) instead of auto-fallback to embedded Claude. The fallback below applies only to automated pipelines or when the user pre-authorized graceful degradation.

Scenario: User Requests Multi-Model Review but Claudish Missing

Detection:

Check if claudish CLI is installed:

Bash: which claudish
Exit code 0: Installed ✓
Exit code 1: Not installed ✗

Or:

Bash: claudish --version
Output: "claudish version 2.2.1" → Installed ✓
Error: "command not found" → Not installed ✗

Recovery Strategy:

Step 1: Detect Missing Claudish
  hasClaudish = checkCommand('which claudish');

  if (!hasClaudish) {
    log("Claudish CLI not found");
    notifyUser();
  }

Step 2: Notify User with Installation Instructions
  "⚠️ Claudish CLI not found. External AI models unavailable.

   To enable multi-model review:
   1. Install: npm install -g claudish
   2. Configure: Set OPENROUTER_API_KEY in .env
   3. Re-run this command

   For now, falling back to embedded Claude Sonnet only."

Step 3: Fallback to Embedded Claude
  log("Falling back to embedded Claude review");
  runEmbeddedReviewOnly();

Benefits:
  - Workflow doesn't fail (graceful degradation)
  - User gets results (Claude review)
  - Clear instructions for enabling multi-model (future use)

Example Implementation:

Phase 2: Model Selection

Bash: which claudish
if [ $? -ne 0 ]; then
  # Claudish not installed
  echo "⚠️ Claudish CLI not found."
  echo "Install: npm install -g claudish"
  echo "Falling back to embedded Claude only."

  # Skip external model selection
  selectedModels=["claude-sonnet"]
else
  # Claudish available
  echo "Claudish CLI found ✓"
  # Proceed with external model selection
  selectedModels=["claude-sonnet", "grok", "gemini", "gpt-5"]
fi

Pattern 6: Out of OpenRouter Credits

Pattern 0 guard: If the user requested a specific external model, apply Pattern 0 (stop and report with options) instead of auto-fallback. The fallback below applies only to automated pipelines or when the user pre-authorized graceful degradation.

Scenario: External Model API Call Fails Due to Insufficient Credits

Detection:

API returns:
  - 402 Payment Required (HTTP status)
  - Or error message contains "credits", "quota", "billing"

Example error messages:
  - "Insufficient credits"
  - "Credit balance too low"
  - "Quota exceeded"
  - "Payment required"

Recovery Strategy:

Step 1: Detect Credit Exhaustion
  if (error.status === 402 || error.message.includes('credits')) {
    handleCreditExhaustion();
  }

Step 2: Log Event
  log("OpenRouter credits exhausted");

Step 3: Notify User
  "⚠️ OpenRouter credits exhausted. External models unavailable.

   To fix:
   1. Visit https://openrouter.ai
   2. Add credits to your account
   3. Re-run this command

   For now, falling back to embedded Claude Sonnet."

Step 4: Skip All External Models
  skipAllExternalModels();

Step 5: Fallback to Embedded Claude
  runEmbeddedReviewOnly();

Benefits:
  - Workflow completes (doesn't fail)
  - User gets results (Claude review)
  - Clear instructions for adding credits

Proactive Credit Check (Advanced):

Before expensive multi-model operation:

Step 1: Check OpenRouter Credit Balance
  Bash: curl -H "Authorization: Bearer $OPENROUTER_API_KEY" \
        https://openrouter.ai/api/v1/auth/key

  Response: { "data": { "usage": 1.23, "limit": 10.00 } }

Step 2: Estimate Cost
  estimatedCost = 0.008  // From cost estimation pattern

Step 3: Check if Sufficient Credits
  remainingCredits = 10.00 - 1.23 = 8.77
  if (estimatedCost > remainingCredits) {
    warnUser("Insufficient credits ($8.77 remaining, $0.008 needed)");
  }

Benefits:
  - Warn before operation (not after failure)
  - User can add credits first (avoid wasted time)

Pattern 7: Retry Strategies

Exponential Backoff:

Retry with increasing delays to avoid overwhelming services:

Retry Schedule:
  1st retry: Wait 1 second
  2nd retry: Wait 2 seconds
  3rd retry: Wait 4 seconds
  Max retries: 3

Formula: delay = 2^attempt × 1000ms

async function retryWithBackoff(fn, maxRetries = 3) {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await fn();
    } catch (error) {
      if (!isRetriable(error)) {
        throw error;  // Don't retry non-retriable errors
      }

      if (attempt === maxRetries - 1) {
        throw error;  // Max retries reached
      }

      const delay = Math.pow(2, attempt) * 1000;
      log(`Retry ${attempt + 1}/${maxRetries} after ${delay}ms`);
      await sleep(delay);
    }
  }
}

When to Retry:

Retriable Errors (temporary, retry likely to succeed):
  ✓ Network errors (ETIMEDOUT, ECONNREFUSED)
  ✓ 500 Internal Server Error (service temporarily down)
  ✓ 503 Service Unavailable (overloaded, retry later)
  ✓ 429 Rate Limiting (wait for reset, then retry)

Non-Retriable Errors (permanent, retry won't help):
  ✗ 401 Unauthorized (bad credentials)
  ✗ 403 Forbidden (insufficient permissions)
  ✗ 404 Not Found (model doesn't exist)
  ✗ 400 Bad Request (invalid input)
  ✗ User cancellation (SIGINT)

Function:
  function isRetriable(error) {
    const retriableCodes = [500, 503, 429];
    const retriableTypes = ['ETIMEDOUT', 'ECONNREFUSED', 'ENOTFOUND'];

    return (
      retriableCodes.includes(error.status) ||
      retriableTypes.includes(error.code)
    );
  }

Max Retry Limits:

Set appropriate max retries by operation type:

Network requests: 3 retries (transient failures)
API calls: 1-2 retries (avoid long delays)
User input: 0 retries (ask user to retry manually)

Example:
  result = await retryWithBackoff(
    () => claudish(model, prompt),
    maxRetries: 2  // 2 retries for API calls
  );

Pattern 8: Claudish Error Reporting

Purpose: After handling an error with Patterns 0–7, optionally report it to claudish developers via the report_error MCP tool. This helps improve claudish reliability. Always ask the user first — data is sanitized before sending.

The report_error Tool:

Parameters:
  error_type (required): "provider_failure" | "team_failure" | "stream_error" | "adapter_error" | "other"
  model (optional):            Model ID that failed (e.g., "grok")
  stderr_snippet (optional):   First 500 chars of error output (from MCP result or channel event)
  exit_code (optional):        Process exit code (if CLI fallback was used)
  error_log_path (optional):   Path to full error log file
  session_path (optional):     Path to team session directory
  additional_context (optional): Extra context (what was attempted, retry count, etc.)
  auto_send (optional):        Mention auto-reporting option to user (bool)

Consent Requirement:

ALWAYS ask user before calling report_error.

Ask:
  "Would you like to report this error to claudish developers?
   Data is sanitized before sending (API keys, paths, and emails are stripped)."

Only call report_error if user says yes.
Never call report_error silently or automatically unless CLAUDISH_TELEMETRY=1 is set.

Error Type Mapping:

Timeout (Pattern 1)                → "provider_failure"
500 / 503 errors (Pattern 2)       → "provider_failure"
Stream interrupted                 → "stream_error"
Model adapter crash                → "adapter_error"
Team session failure               → "team_failure"
Unknown / other                    → "other"

When to Offer Error Reporting:

Offer after:
  ✅ Pattern 0 (user escalation) — add option (5) to the report template
  ✅ Pattern 1 (timeout) — if model times out repeatedly
  ✅ Pattern 2 (API failures) — for 500/503 provider errors
  ✅ Pattern 3 (partial success) — for models that failed in multi-model runs

Do NOT offer after:
  ❌ Pattern 5 (claudish not installed) — MCP tool is unavailable
  ❌ Pattern 6 (out of credits) — billing issue, not a claudish bug

Example Flow:

1. Model fails (detected by Pattern 0–7)
2. Handle error per the appropriate pattern (retry, skip, escalate to user)
3. Ask user:
     "Would you like to report this error to claudish developers?
      Data is sanitized before sending."
4. If yes:
     call report_error(
       error_type: "provider_failure",
       model: "grok",
       stderr_snippet: "connection timed out after 30000ms...",
       exit_code: 124,
       additional_context: "Timed out on attempt 2 of 2"
     )
5. If user reports errors frequently:
     mention auto_send option:
     "You can enable automatic reporting via claudish config → Privacy → Telemetry
      or by setting CLAUDISH_TELEMETRY=1."

Automatic Reporting:

When CLAUDISH_TELEMETRY=1 is set (or enabled via claudish config → Privacy → Telemetry):
  - Errors are reported automatically without asking
  - Skip the consent step in the flow above
  - Still log that a report was sent: "Error reported to claudish developers."

To inform users:
  "Tip: Set CLAUDISH_TELEMETRY=1 to enable automatic error reporting."

Integration with Other Skills

error-recovery + multi-model-validation:

Use Case: Handling external model failures in parallel execution

Step 1: Parallel Execution (multi-model-validation)
  Launch 5 models simultaneously

Step 2: Error Recovery (error-recovery)
  Model 1: Success ✓
  Model 2: Timeout → Skip (timeout handling pattern)
  Model 3: 500 error → Retry once, then skip
  Model 4: Success ✓
  Model 5: Success ✓

Step 3: Partial Success Strategy (error-recovery)
  3/5 successful (≥ 2 threshold)
  Proceed with consolidation using 3 reviews

Step 4: Consolidation (multi-model-validation)
  Consolidate 3 successful reviews
  Notify user about 2 failures

error-recovery + quality-gates:

Use Case: Test-driven loop with error recovery

Step 1: Run Tests (quality-gates TDD pattern)
  Bash: bun test

Step 2: If Test Execution Fails (error-recovery)
  Error type: Syntax error in test file

  Recovery:
    - Fix syntax error
    - Retry test execution
    - If still fails: Notify user, skip TDD phase

Step 3: If Tests Pass (quality-gates)
  Proceed to code review

error-recovery + multi-agent-coordination:

Use Case: Agent selection with fallback

Step 1: Agent Selection (multi-agent-coordination)
  Preferred: ui-developer-codex (external validation)

Step 2: Check Tool Availability (error-recovery)
  Bash: which claudish
  Result: Not found

Step 3: Fallback Strategy (error-recovery)
  Log: "Claudish not installed, falling back to embedded ui-developer"
  Use: ui-developer (embedded)

Step 4: Execution (multi-agent-coordination)
  Agent: dev:frontend

Best Practices

Do:

• ✅ Set timeout limits (30s default, 60s for complex tasks)
• ✅ Retry transient errors (network, 500, 503)
• ✅ Use exponential backoff (avoid hammering services)
• ✅ Skip non-retriable errors (401, 404, don't retry)
• ✅ Provide graceful degradation (fallback to embedded Claude)
• ✅ Save partial results on cancellation
• ✅ Communicate transparently (tell user what failed and why)
• ✅ Adapt to partial success (N ≥ 2 reviews is useful)
• ✅ Offer error reporting for provider/stream failures (report_error tool)

Don't:

• ❌ Silently substitute a different model than the user requested (Pattern 0 violation)
• ❌ Fall back to embedded Claude without asking when user requested a specific model
• ❌ Retry indefinitely (set max retry limits)
• ❌ Retry non-retriable errors (waste time on 401, 404)
• ❌ Fail entire workflow for single model failure (graceful degradation)
• ❌ Hide errors from user (be transparent)
• ❌ Discard partial results on failure (save what succeeded)
• ❌ Ignore user cancellation (handle SIGINT gracefully)
• ❌ Retry without delay (use backoff)
• ❌ Call report_error without user consent (privacy requirement)

Performance:

• Exponential backoff: Prevents overwhelming services
• Max retries: Limits wasted time (3 retries = <10s overhead)
• Graceful degradation: Workflows complete despite failures

Examples

Example 1: Timeout with Retry

Scenario: Grok model times out, user retries with longer timeout

Execution:

Attempt 1:
  create_session(model="grok", prompt=PROMPT, timeout_seconds=30)
  Result: failed channel event — timeout after 30s

  Notify user:
    "⚠️ Grok timed out after 30s.
     Options:
     1. Retry with 60s timeout
     2. Skip Grok
     3. Cancel workflow"

  User selects: 1 (Retry)

Attempt 2:
  create_session(model="grok", prompt=PROMPT, timeout_seconds=60)
  Result: completed channel event after 45s

  Log: "Grok review completed on retry (45s)"
  get_output(session_id) → process result
  Continue with workflow

Example 2: Partial Success (2/4 Models)

Scenario: 4 models selected, 2 fail, proceed with 2

Execution:

Launch 4 models in parallel:
  Task: Claude (embedded)
  Task: Grok (external)
  Task: Gemini (external)
  Task: GPT-5 (external)

Results:
  Claude: Success ✓ (2 min)
  Grok: Timeout ✗ (30s)
  Gemini: 500 error ✗ (retry failed)
  GPT-5: Success ✓ (3 min)

successful.length = 2 (Claude, GPT-5)
2 ≥ 2 ✓ (threshold met)

Notify user:
  "2/4 models completed successfully.

   Successful:
   - Claude Sonnet ✓
   - GPT-5 Codex ✓

   Failed:
   - Grok: Timeout after 30s
   - Gemini: 500 Internal Server Error (retry failed)

   Proceeding with 2-model consensus."

Consolidate:
  consolidateReviews([
    "ai-docs/claude-review.md",
    "ai-docs/gpt5-review.md"
  ]);

Present results with 2-model consensus

Example 3: User Cancellation

Scenario: User presses Ctrl+C during PHASE 3

Execution:

Workflow starts:
  PHASE 1: Requirements ✓ (30s)
  PHASE 2: Architecture ✓ (2 min)
  PHASE 3: Implementation (in progress, 3 min elapsed)

User presses Ctrl+C:
  Signal: SIGINT received

Handler executes:
  Log: "User cancelled workflow (Ctrl+C)"

  Stop agents:
    - backend-developer (currently executing)
    - Terminate gracefully

  Collect partial results:
    - ai-docs/requirements.md ✓
    - ai-docs/architecture-plan.md ✓
    - src/auth.ts (30% complete)

  Save to file:
    Write: ai-docs/partial-implementation.md
      "# Workflow Cancelled
       Completed: PHASE 1, PHASE 2
       Partial: PHASE 3 (30%)
       Pending: PHASE 4, PHASE 5"

  Notify user:
    "⚠️ Workflow cancelled by user.
     Partial results saved to ai-docs/partial-implementation.md
     Completed: 2/5 phases (40%)"

  Exit: process.exit(0)

Troubleshooting

Problem: Workflow fails after single model timeout

Cause: No graceful degradation

Solution: Continue with remaining models

❌ Wrong:
  if (timeout) {
    throw new Error("Model timed out");
  }

✅ Correct:
  if (timeout) {
    log("Model timed out, skipping");
    skipModel();
    continueWithRemaining();
  }

Problem: Retrying 401 errors indefinitely

Cause: Retrying non-retriable errors

Solution: Check if error is retriable

❌ Wrong:
  for (let i = 0; i < 10; i++) {
    try { return await fn(); }
    catch (e) { /* retry all errors */ }
  }

✅ Correct:
  for (let i = 0; i < 3; i++) {
    try { return await fn(); }
    catch (e) {
      if (!isRetriable(e)) throw e;  // Don't retry 401
      await sleep(delay);
    }
  }

Problem: No visibility into what failed

Cause: Not communicating errors to user

Solution: Transparently report all failures

❌ Wrong:
  "Review complete!" (hides 2 failures)

✅ Correct:
  "Review complete (2/4 models succeeded).
   Failed: Grok (timeout), Gemini (500 error)"

Summary

Error recovery ensures resilient workflows through:

• Timeout handling (detect, retry with longer timeout, or skip)
• API failure recovery (retry transient, skip permanent)
• Partial success strategies (N ≥ 2 threshold, adapt to failures)
• User cancellation (graceful Ctrl+C, save partial results)
• Missing tools (claudish not installed, fallback to embedded)
• Out of credits (402 error, fallback to free models)
• Retry strategies (exponential backoff, max 3 retries)
• Error reporting (report_error MCP tool, user consent required)

With these patterns, workflows are production-ready and resilient to inevitable failures.

Extracted From:

• /review command error handling (external model failures)
• /implement command PHASE 2.5 (test-driven loop error recovery)
• Production experience with Claudish proxy failures
• Multi-model validation resilience requirements