Data Dictionary Creator
Generate a data dictionary for a dataset, combining automatic profiling with the user's description of what the data represents. Use when the user wants documentation of columns — names, types, semantic meaning, units, allowed values, and nullability — for a CSV/Parquet/Excel file.
Install
$ agentstack add skill-danielrosehill-claude-data-analyst-plugin-data-dictionary-creator ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-danielrosehill-claude-data-analyst-plugin-data-dictionary-creator) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Data Dictionary Creator
Produce a data dictionary by merging schema inspection with the user's semantic description of the dataset.
Inputs
- Path to a dataset file or folder.
- The user's description of the dataset: what it represents, how it was collected, what each column means (can be partial — infer the rest).
- Optional: output format (
markdowndefault,csv, orjson).
Recommended CLI tooling
duckdb -c "DESCRIBE SELECT * FROM ''"— fast schema + inferred types.csvstat— null counts, uniqueness, min/max per column.uv run --with pandas python -c '...'— for dtype coercion and sampling.
Procedure
Step 1 — Auto-profile every column
For each column, collect:
- Inferred data type (int, float, string, date, boolean, category)
- Null count and percentage
- Unique count (and full value list if -dictionary.md`:
# Data Dictionary —
## Overview
...
## Columns
### `column_name`
- **Type**: ...
- **Description**: ...
- **Unit**: ...
- **Nullable**: ...
- **Allowed values**: ...
- **Sample**: ...
- **Notes**: ...For csv output, flatten to one row per column with standard dictionary columns. For json, emit a structured schema object compatible with JSON Schema / Frictionless Data.
End with a [NEEDS REVIEW] section listing columns the user should clarify.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: danielrosehill
- Source: danielrosehill/Claude-Data-Analyst-plugin
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.