inspect-eval-debugging Skill
Guide for debugging inspectai evaluation failures, score issues, and model behavior. Use this when eval results are unexpected, scores are wrong, scoring fails, or model output appears corrupted. Published by microsoft in ACESEvals.
What is inspect-eval-debugging Skill?
Guide for debugging inspectai evaluation failures, score issues, and model behavior. Use this when eval results are unexpected, scores are wrong, scoring fails, or model output appears corrupted. Published by microsoft in ACESEvals. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Deployment
- Deployment use cases
Technical details
- Install or run with Copy skill directory
When to use inspect-eval-debugging Skill
- Use it for deployment.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/ACESEvals
- Skill file: .github/skills/inspect-eval-debugging/SKILL.md
What it does
Guide for debugging inspectai evaluation failures, score issues, and model behavior. Use this when eval results are unexpected, scores are wrong, scoring fails, or model output appears corrupted.
Skill instructions
To debug inspectai evaluation issues in SABER, follow this systematic process: 1. Triage: Identify the Failure Category Check the eval summary output. Common categories: | Symptom | Likely Cause | Section | |---------|-------------|---------| | Score is nan for all scorers | Agent never reached scoring (sandbox error, tool failure) | §2 | | Score is 0.0 but expected higher | Scorer ran but agent's work was incorrect or scorer has a bug | §3 | | Score differs across runs of same model | Non-deterministic model behavior | §4 | | Eval crashes with traceback | Code error in domain scoring/tools or inspectai | §5 | | 0/0 trailing brace or garbage in tool args | Model produces malformed tool call arguments | §4 | | Docker/sandbox errors | Container issues | §6 | 2. Debug Agent Behavior (Score is nan or Unexpected) Parse the eval log to see exactly what the agent did: python import json, zipfile with zipfile.ZipFile("logs/<file.eval", "r") as zf: for name in zf.namelist(): if not name.startsw
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Frequently asked questions
What is inspect-eval-debugging?
inspect-eval-debugging is a open-source AI agent skill with Copy skill directory. Guide for debugging inspectai evaluation failures, score issues, and model behavior. Use this when eval results are unexpected, scores are wrong, scoring fails, or model output appears corrupted.
Who is inspect-eval-debugging best for?
inspect-eval-debugging is best for reusing agent instructions, scripts, and references, deployment workflows.
How do I install inspect-eval-debugging?
Install or run inspect-eval-debugging using Copy skill directory. Check inspect-eval-debugging for the latest setup command.
Is inspect-eval-debugging actively maintained?
inspect-eval-debugging may need a closer maintenance check before production use.
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