Debug Stuck Hawk/Inspect AI Evaluations

VerifiedCaution

Diagnoses why a Hawk/Inspect AI evaluation is stuck or not progressing. Provides a checklist, error pattern lookup, and direct API testing to identify issues like API retries, pod crashes, or token limits. Helps recover by deleting and restarting the eval or inspecting logs.

Sby Skills Guide Bot
TestingIntermediate
6/2/2026
Claude Code
#debugging#stuck-evals#hawk#inspect-ai#evaluation

Recommended for

Automated summary

Provides a systematic approach to debug stuck Hawk/Inspect AI evaluations, covering auth checks, pod status, logs, and direct API testing.

Strengths

  • Offers a structured checklist to quickly identify the root cause of stuck evals.
  • Includes advanced techniques such as direct API calls and retry pattern analysis.
  • Provides concrete resolutions for each documented error pattern.

Limitations

  • Requires access to internal Hawk tools and S3 infrastructure.
  • Solutions depend on the specific environment configuration (middleman, providers).
  • Does not cover underlying infrastructure issues (network, API quotas).
When to use it

When a Hawk/Inspect AI evaluation is not progressing, samples remain incomplete, or 500 errors occur.

When not to use it

For evaluation issues not related to stalling, such as task logic or scoring errors.

Security analysis

Caution
Quality score92/100

The skill instructs authenticated access to internal services and deletion of resources, but within a legitimate debugging context. It does not exfiltrate data, run destructive system commands, or disable safety. Risk is limited to users with existing authorization.

Findings
  • •Uses `curl` to test internal API endpoints with access tokens.
  • •Instructs deletion of evaluation sets via `hawk delete`.
  • •References internal infrastructure (middleman.internal.metr.org) and proprietary tools.
How this analysis is made

Examples

Debug stuck eval
My Inspect eval is stuck. The eval-set ID is evalset-abc123. Can you help me debug it?
Investigate high retry count
I see 'HTTP retries: 45' in my eval logs. What does that mean and how do I fix the stuck eval?
Test API directly
I'm getting 500 errors from the middleman during an eval. How do I test the API directly to see the real error?

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

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/download' --output '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md.part' && mv '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md.part' '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md' && test -s '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/debug-stuck-eval' && cp -R '.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v' '.claude/skills/debug-stuck-eval'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v'

openskills read 'debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/debug-stuck-hawk-inspect-ai-evaluations-mmbizj1v/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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