Debug Stuck Hawk/Inspect Evaluations

VerifiedSafe

Diagnose and resolve stuck or frozen evaluations using Hawk commands, log analysis, and direct API testing to identify timeout, memory, or API errors.

Sby Skills Guide Bot
TestingAdvanced
306/2/2026
Claude Code
#debugging#inspect-ai#stuck-eval#api-testing#eval-recovery

Recommended for

Our review

This skill provides tools and techniques to diagnose and resolve Hawk/Inspect AI evaluations that are stuck or not progressing.

Strengths

  • Structured checklist with clear steps for identifying causes of stalls.
  • Coverage of common error patterns with specific resolutions.
  • Ready-to-use commands for direct API testing through middleman.
  • Recovery procedure that allows resuming evaluation via S3 buffer.

Limitations

  • Requires access to Hawk CLI and internal S3 buckets.
  • Some steps depend on Metr-specific infrastructure (middleman).
  • May not cover all stall cases, particularly configuration issues.
When to use it

Use this skill when the user reports that an AI evaluation is stuck, not progressing, or showing 500 errors or retry loops.

When not to use it

Do not use it if the user is asking about general evaluation design or result interpretation.

Security analysis

Safe
Quality score92/100

The skill provides diagnostic and recovery commands for debugging stuck evaluations using Hawk/Inspect AI tools. No destructive or exfiltrating commands are included; all operations are standard debugging steps within the evaluation platform.

No concerns found

Examples

Stuck evaluation
My evaluation set is stuck and not progressing. Can you help me debug it?
High retry count
I see that my eval has a high retry count. What should I do?
Direct API test
The logs show retries but no clear error. Can you help me test the API directly?

name: debug-stuck-eval description: Debug stuck Hawk/Inspect AI evaluations. Use when user mentions "stuck eval", "eval not progressing", "eval hanging", "samples not completing", "eval set frozen", "runner stuck", "500 errors in eval", "retry loop", "eval timeout", or asks why an evaluation isn't finishing.

Quick Checklist

  1. Verify auth: hawk auth access-token > /dev/null || echo "Run 'hawk login' first"
  2. Get eval-set-id from user
  3. Check status: hawk status <eval-set-id> - JSON report with pod state, logs, metrics
  4. View logs: hawk logs <eval-set-id> or hawk logs -f for follow mode
  5. List samples: hawk list samples <eval-set-id> - see completion status
  6. Look for error patterns (see below)
  7. Test API directly if logs show retries without clear errors

Error Patterns

| Log Pattern | Meaning | Resolution | |-------------|---------|------------| | [uuid task/id/epoch model] Retrying request to /responses | OpenAI SDK retry with sample context | Test API directly with curl to see real error | | [uuid task/id/epoch model] -> model retry N ... [ErrorType code] | Inspect retry with error summary | Check error type; use curl for full details | | 500 - Internal server error | API issue | Download buffer, find failing request, test through middleman AND directly to provider | | 400 - invalid_request_error | Token/context limit exceeded | Check message count and model context window | | Pod UID mismatch | Sandbox pod was killed and restarted | No fix needed—sample errored out, Inspect will retry | | Empty output, pending: true | API returned malformed response | Restart eval (buffer resumes) | | OOMKilled in pod status | Memory exhaustion | Increase pod memory limits |

Key Techniques

  1. Retry messages have sample context - All retry messages include a [sample_uuid task/sample_id/epoch model] prefix. Inspect's own retries also include a compact error summary suffix like [RateLimitError 429 rate_limit_exceeded]. The OpenAI SDK's internal retry messages still don't show the actual error — use curl for full details.
  2. FAIL-OK patterns are fine - Alternating failures and successes mean the eval IS progressing. Only worry about consistent FAIL-FAIL-FAIL patterns.
  3. Use S3 for buffer access - Download .buffer/ from S3 rather than accessing the runner pod directly.
  4. Read .eval files with inspect_ai - Use from inspect_ai.log import read_eval_log instead of manually extracting zips.

Test API Directly

Middleman is the auth proxy. If middleman fails but direct provider calls work, it's a middleman issue.

TOKEN=$(hawk auth access-token)

# Test through middleman
curl --max-time 300 -X POST https://middleman.internal.metr.org/anthropic/v1/messages \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"model": "claude-sonnet-4-20250514", "max_tokens": 100, "messages": [{"role": "user", "content": "Say hello"}]}'

# Test OpenAI-compatible
curl --max-time 300 -X POST https://middleman.internal.metr.org/openai/v1/chat/completions \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Say hello"}], "max_tokens": 100}'

Recovery

# Delete stuck eval and restart
hawk delete <eval-set-id>
hawk eval-set <config.yaml>

The sample buffer in S3 allows Inspect to resume from where it left off (unless you use --no-resume).

HTTP Retry Count

Task progress logs include "HTTP retries: X". High retry counts indicate API instability even while tasks complete.

Severity: Retry count × wait time = stuck duration. E.g., 45 retries × 1800s = 22+ hours stuck.

More Details

See docs/debugging-stuck-evals.md for:

  • Sample buffer SQL queries
  • Detailed API testing examples
  • Escalation checklist

References

Filing Issues

  • Middleman: https://github.com/metr-middleman/middleman-server/issues
  • Hawk: Linear issue on Evals Execution team
  • Inspect AI: https://github.com/UKGovernmentBEIS/inspect_ai/issues
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