"Validate and interpret CustomConcept101 benchmark artifacts for
Scanned 9/8/2026
Install to Claude Code
npx -y skills add VectorSpaceLab/AREX-Skill --skill benchmarking --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: benchmarking
description: "Validate and interpret CustomConcept101 benchmark artifacts for
CLIP text alignment, CLIP image alignment, and DINO image alignment."
disable-model-invocation: true
metadata:
disco-role: operating
license: NOASSERTION
---
# CustomConcept101 benchmarking
Use this sub-skill when you need to validate generated sample layouts or interpret CustomConcept101 evaluation results.
It covers:
- `sample_root/samples/*.png` layout checks
- `sample_root/prompts.json` prompt-stem coverage
- `+`-separated `target_paths` parsing
- CLIP text alignment, CLIP image alignment, and DINO image alignment outputs
It does not cover image generation or retraining. Route those tasks to [`../inference/SKILL.md`](../inference/SKILL.md) and [`../training/SKILL.md`](../training/SKILL.md).
## Start here
1. Read [`references/data-formats.md`](references/data-formats.md).
2. Run [`scripts/validate_benchmark_layout.py`](scripts/validate_benchmark_layout.py) before any expensive metric run.
3. Read [`references/workflows.md`](references/workflows.md) for the evaluation contract.
4. Check [`references/troubleshooting.md`](references/troubleshooting.md) when PNG counts, prompt stems, CUDA, CLIP/DINO downloads, or pickle updates fail.
## Constraints
- The source evaluator is CUDA-only and may download CLIP/DINO weights.
- The validator is layout-only and does not load models.
- Benchmark target images stay external to the runtime tree.
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