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name: data-and-config-workflows description: "Operate OpenPrompt data processors, dataset layouts, few-shot sampling, and YAML config validation without starting training." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

OpenPrompt Data And Config Workflows

Use this sub-skill when the task involves OpenPrompt dataset processors, InputExample payloads, FewShotSampler, experiment YAMLs, get_user_config, or safe inspection of experiments/cli.py configuration behavior.

Load First

• references/api-reference.md for the supported PROCESSORS map, config APIs, and sampler contracts.
• references/data-formats.md before preparing or validating local dataset directories.
• references/workflows.md for safe config-inspection and example YAML patterns.
• references/troubleshooting.md when config loading, dataset names, asset paths, or split files fail.

Operating Boundaries

This sub-skill owns:

• Selecting an OpenPrompt DataProcessor by config.dataset.name and explaining the expected dataset layout.
• Reading, merging, and validating YAML config intent at the same level of safety as openprompt.config.get_user_config without starting model loading, training, testing, or dataset downloads.
• Explaining parent_config selector patterns for task, template, verbalizer, learning_setting, and few-shot sampling branches.
• Diagnosing local paths for dataset directories and prompt assets referenced by config keys such as dataset.path, manual_template.file_path, and manual_verbalizer.file_path.

This sub-skill does not own:

• PLM loading, tokenizer wrapper smoke tests, or top-level pipeline construction; route those to ../pipeline-basics/.
• Template/verbalizer grammar and prompt asset authoring; route those to ../template-verbalizer-design/.
• Runner selection, checkpoint behavior, GPU execution, or actual training/generation; route those to ../training-and-generation/.
• Running datasets/download_*.sh or any network download without explicit user approval.

Safe Config Inspection

Prefer the bundled script whenever a task asks whether an OpenPrompt YAML is structurally valid or whether referenced local files are present:

python scripts/inspect_openprompt_config.py \
  --config /path/to/config.yaml \
  --base-dir /path/used-for-relative-assets \
  --check-paths

The script is self-contained: it embeds the OpenPrompt processor catalog and config selectors, does not import openprompt, does not call experiments/cli.py, and does not start training or download datasets.

Default Procedure

1. Identify the config file, intended base directory for relative paths, and whether paths should be checked or only summarized.
2. Summarize dataset.name, dataset.path, task, learning_setting, template, verbalizer, PLM fields, batch sizes, and selected branches.
3. Check that dataset.name.lower() is in the known processor names and that known local layouts match the expected family in references/data-formats.md.
4. Validate selector branches: if a YAML selects template: manual_template, a corresponding branch should either come from OpenPrompt defaults or be supplied in the YAML; user-added prompt components should declare an appropriate parent_config.
5. Validate prompt asset and dataset paths only as filesystem references. Never open a model, instantiate a runner, or call load_dataset for a benchmark directory unless the user explicitly asks and supplies data.
6. Report any paths that appear copied from OpenPrompt examples and need replacement with project-local dataset or asset locations.