Model Eval Error Analysis
- 41 installs
- 1 repo stars
- Updated July 31, 2026
- jurgendn/agent-skills
Helps with ai & agent building tasks.
About
model-eval-error-analysis is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- model-eval-error-analysis
- AI & Agent Building
- AI-coding skill
Model Eval Error Analysis by the numbers
- 41 all-time installs (skills.sh)
- Ranked #8,148 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 41 |
|---|---|
| repo stars | ★ 1 |
| Last updated | July 31, 2026 |
| Repository | jurgendn/agent-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
Model Eval Error Analysis
Do not stop at one headline metric.
Workflow
1. Verify the metric implementation and data split. 2. Report the headline result with provenance. 3. Slice performance by relevant factors such as:
- difficulty
- length / scale
- class imbalance
- domain shift
- prompt family
- robustness setting
4. Inspect failures manually and cluster them. 5. Separate:
- random noise
- systematic failure modes
- evaluator bugs
6. Compare against the strongest baseline on the same slices. 7. End with concrete next actions.
Rules
- Never smooth away inconvenient variation.
- If confidence intervals or multiple seeds matter, say so.
- A higher average may still hide a worse model for important slices.
Output shape
Return:
- Metrics checked
- Slice analysis
- Failure taxonomy
- Suspected causes
- Recommended fixes or follow-up experiments
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