Our review
This skill helps debug stuck Hawk/Inspect AI evaluations by providing a quick checklist, error pattern analysis, direct API testing, and recovery commands.
Strengths
- Quick checklist to identify common issues
- Error pattern table mapping log patterns to resolutions
- Direct API testing procedure via curl
- Recovery steps leveraging S3 buffer to resume evals
Limitations
- Requires access to Metr infrastructure and hawk CLI
- Some rare error patterns may not be covered
- Recovery may lose sample progress if buffer is not used
When an evaluation is not progressing, hanging on a sample, or showing infinite retries.
For simple errors that are already clearly reported in logs without signs of stalling.
Security analysis
CautionThe skill instructs users to retrieve access tokens and make curl requests to internal APIs, which could lead to token exposure if logs are shared. However, it's for legitimate debugging purposes and does not instruct exfiltration or destructive actions.
- •Uses curl commands with access tokens to internal APIs; token exposure risk if logs are shared.
Examples
My eval set is stuck — samples are not completing and I see 'Retrying request' messages repeatedly.The evaluation is hanging with '500 Internal Server Error' logs. How can I debug this?I see high HTTP retry counts in the task progress logs, the eval has been running for hours with no progress.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
- Verify auth:
hawk auth access-token > /dev/null || echo "Run 'hawk login' first" - Get eval-set-id from user
- Check status:
hawk status <eval-set-id>- JSON report with pod state, logs, metrics - View logs:
hawk logs <eval-set-id>orhawk logs -ffor follow mode - List samples:
hawk list samples <eval-set-id>- see completion status - Look for error patterns (see below)
- 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
- 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. - FAIL-OK patterns are fine - Alternating failures and successes mean the eval IS progressing. Only worry about consistent FAIL-FAIL-FAIL patterns.
- Use S3 for buffer access - Download
.buffer/from S3 rather than accessing the runner pod directly. - Read .eval files with inspect_ai - Use
from inspect_ai.log import read_eval_loginstead 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
- Inspect AI Model Providers - Model configuration
- Inspect AI Eval Logs - .eval file format
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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