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開発・技術 · SKILL.md

sampling-and-generation

sampling-and-generation

Validate pix2code trained artifacts and generate DSL from screenshots using greedy or beam search workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: sampling-and-generation description: "Validate pix2code trained artifacts and generate DSL from screenshots using greedy or beam search workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Sampling and Generation

Use this sub-skill when the user wants to turn one screenshot or a directory of screenshots into pix2code DSL output, or when they need to check that a trained artifact directory is complete before sampling.

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Quick workflow

  1. Verify the artifact directory first:
python sub-skills/sampling-and-generation/scripts/check_pix2code_artifacts.py --artifacts bin
  1. Sample a single screenshot when the artifacts are complete by adapting the legacy argument order described in inference-workflows.md.

  2. Sample a directory of screenshots when you want a batch of .gui outputs by using the same artifact-directory and search-mode conventions.

  3. If the task is only to diagnose missing files or stale artifacts, use the bundled checker and stop there.

Boundaries

This sub-skill owns artifact validation and screenshot-to-DSL generation guidance. It does not own dataset preparation; route that to ../data-and-training/SKILL.md. It does not own DSL compilation; route that to ../dsl-compilation/SKILL.md.

Validation checklist

  • The artifact directory contains pix2code.json, pix2code.h5, meta_dataset.npy, and words.vocab.
  • meta_dataset.npy holds the input shape and output size expected by the sampler.
  • The screenshot input exists and is readable by OpenCV.
  • Greedy search is the default path; beam search is selected by passing an integer beam width.
  • The generated DSL output is a .gui file with the original tokens stripped of <START> and <END>.
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