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Update Data Dictionary

skill-danielrosehill-claude-data-wrangler-plugin-update-data-dictionary · by danielrosehill

Update an existing data dictionary to reflect new columns, changed types, renamed fields, dropped columns, or new transformations/provenance entries. Use after running any operation that modifies a dataset's schema (add-iso3166, enrich-with-currency, text-to-numeric, json-restructure, etc.).

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Install

$ agentstack add skill-danielrosehill-claude-data-wrangler-plugin-update-data-dictionary

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Update Data Dictionary

Keep an existing data dictionary in sync with the current dataset.

When to invoke

  • A schema-changing operation just ran and the dataset's folder contains a data_dictionary.{md,yaml,json,csv}.
  • User says "update the data dictionary", "re-document", "re-profile".
  • Another wrangler skill needs to log a transformation.

Procedure

  1. Find the dictionary — look for data_dictionary.* in the dataset's folder. If none exists, suggest add-data-dictionary instead.
  2. Load the current dataset and profile columns.
  3. Diff against the existing dictionary:
  • New columns — present in data, missing from dictionary → add row with stub description, ask user to fill in.
  • Removed columns — present in dictionary, missing from data → mark as "REMOVED " or offer to delete the row (ask user).
  • Type changes — update the type column; log in provenance.
  • Renamed columns — heuristic only; ask the user to confirm rename mappings rather than guessing.
  1. Append to the provenance / transformations section with today's date and a one-line summary of what changed.
  2. Preserve user-written descriptions — never overwrite a description the user has filled in. Only touch auto-generated fields (type, examples, nullability).
  3. Write the updated dictionary back to the same file, preserving its format (md/yaml/json/csv).
  4. Report a summary diff: N columns added, M removed, K types changed.

Dependencies

pip install pandas pyarrow openpyxl pyyaml

Edge cases

  • Markdown round-trip — parse the existing columns table carefully; preserve any additional text sections (notes, known issues) that the user has added by hand.
  • Multiple dictionary files — if both data_dictionary.md and data_dictionary.yaml exist, ask the user which is canonical; optionally update both.
  • Dictionary out of date with multiple intermediate changes — the provenance log may have gaps. Log today's transformation; flag the gap but don't fabricate history.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.