name: evaluation-and-metrics
description: "Evaluate DeTikZify outputs with metric wrappers, score generated
TikZ programs, and reason about the repository's evaluation pipeline."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
Evaluation and Metrics
Use this sub-skill when the task is about examples/eval.py, metric wrappers, score computation, comparison against references, or the evaluation side of compiled TikZ generation.
Route away from this sub-skill when the task is mostly about producing one new program, launching the web UI, or changing training loops.
Fast Path
- Confirm the package and compile helpers are available:
python scripts/api_smoke.py
python scripts/tikz_smoke.py
- Read the metric reference before trying a scoring run:
references/metrics.md
- Read the workflow reference if you need the model-input and caching behavior:
references/workflows.md
What This Sub-Skill Owns
- the
detikzify.evaluate metric wrappers
- score aggregation over generated and reference TikZ
ImageSim and the perceptual scoring path
ClipScore, CrystalBLEU, DreamSim, KernelInceptionDistance, and TexEditDistance
- redacted compile / rasterize metrics and token-efficiency style summaries
- the repo's evaluation pipeline and its compile-backed checks
Common Decisions
- Use
ImageSim / model-based scoring when you want perceptual similarity.
- Use the faster compiler-diagnostics path when you only need a cheap evaluation pass.
- Remember that some metrics are lazily imported and need optional dependencies.
- Keep the compile/rasterize path in mind: evaluation is not just string comparison.
- If the task asks about redacted outputs, inspect the compileable document and the redaction step separately.
Bundled References
Guardrails
- A metric import succeeding does not prove the model or render path works.
- Do not ignore compile failures when the score depends on compiled TikZ.
- Do not assume optional metric dependencies are present in a minimal install.