Update to Unix Copilot

{
  "description": "Work with CSV, TSV, JSON, encoding and CLI data transforms while preserving quoted fields and record integrity.",
  "included_files": [],
  "name": "structured-data-cli",
  "skill_md_contents": "---\nname: structured-data-cli\ndescription: Work with CSV, TSV, JSON, encoding and CLI data transforms while preserving quoted fields and record integrity.\n---\n\n# Structured data and CLI pipelines\n\n## Scope and execution truth\nThis is a skills-only assistant workflow. Do not claim to execute system commands, inspect the user's host, install packages, or complete side effects without actual authorized tools and observed results. User-pasted output is evidence, not a machine connection.\n\n## Procedure\n1. Determine format, delimiter, encoding, headers, null semantics, identifier types, row counts, quoted fields and multiline records.\n2. For general CSV, prefer Python `csv`, Miller, csvkit or DuckDB; never assume `cut -d,` or `awk -F,` parses quoted commas or embedded newlines.\n3. Treat identifier-like strings such as postal codes and account keys as strings to preserve leading zeros.\n4. Stage output to a new file; validate header/schema, row count, invalid records, duplicates, and rejected rows before replacement.\n5. Label transformations that might alter data (invalid-byte removal, locale decimals, date parsing, normalization), and avoid lossy defaults.\n6. Provide an idempotent approach and a reconciliation report for repeatable pipelines.\n\n## Relevant references\n- Read `../../references/structured-data.md` for domain-specific guidance.\n- Read `../../references/text-processing.md` for domain-specific guidance.\n- Read `../../references/automation-and-recovery.md` for domain-specific guidance.\n\n## Completion checks\n- CSV quoting and multiline cases handled\n- Data type and null semantics preserved\n- Row-level reconciliation included\n\n## Deliverable\nUse the minimum format that makes the answer actionable: the actual command or patch, platform assumptions, a preview before risky changes, and a reproducible validation step. Clearly label proposed actions versus observed execution.\n"
}