executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
structured-exportGuides NLP-progress structured JSON export, Markdown parser assumptions, output schema, bundled export script use, and export troubleshooting.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when a user asks to convert NLP-progress Markdown pages into machine-readable JSON, inspect the structured export schema, debug structured.json, or preserve the behavior of the repository's Markdown parser.
NLP-progress is not an installable Python package. The export workflow is a standard-library Python script that reads Markdown files or directories and writes JSON. No ML backend, dataset download, accelerator, Ruby runtime, or package install is required.
Identify the NLP-progress content root or the exact Markdown files/directories the user wants to export.
Use the bundled script from this sub-skill directory or copy it to a working area:
python3 scripts/export_nlp_progress.py <file-or-directory> --output structured.json
python3 scripts/export_nlp_progress.py english vietnamese/vietnamese.md --output subset.json
Validate the JSON shape with references/json-schema.md. Expect a top-level list of task objects; nested subtasks, datasets, sota, and subdatasets appear only when the source headings/tables support them.
Check references/parser-assumptions.md before relying on nested H4 sections, unusual table headers, multiple paper links, or multiple SOTA tables in one section.
If output is empty, lossy, or noisy, triage with references/troubleshooting.md.
structured/export.py.benchmark-catalog first.content-maintenance first.When finishing an export task, report: