---name: datapackagedescription: >license: CC-BY-4.0compatibility: |Required CLI tools: jq >= 1.8Optional CLI tools: frictionless >= 5.19 (with fastparquet for Parquet support)Required skills: install-duckdb, query, attach-dbOptional Python packages: pandas, polars, duckdb (for DataFrame work)metadata: - author: Catalyst Cooperative- email: hello@catalyst.coop- last-updated: 2026-08-15---

Frictionless Data Package Guide

This skill covers any dataset described by a Frictionless Data Package descriptor file (datapackage.json). It is intentionally generic — it works for any conforming datapackage, regardless of who published it or what the data contains.

For PUDL-specific knowledge (S3 bucket paths, table tier conventions, data source context, usage warnings), also use the pudl skill on top of this one.

What is a datapackage.json?

A datapackage.json is a JSON file that describes a collection of tabular data resources. Each resource represents one table (or file) and includes:

• name: machine-readable identifier
• description: human-readable description, often including processing notes, primary keys, and usage warnings
• path: filename or URL of the actual data file
• schema.fields: list of columns, each with a name and description
• schema.primaryKey: the field or fields that uniquely identify a row in this resource
• schema.foreignKeys: declared links from this resource's fields to another resource's primary key — check these before joining or aggregating (see Metadata Querying)

The file can be large (hundreds of resources, megabytes of JSON). Always query it selectively — never load it whole into context.

Dependency check

Before querying metadata, verify jq is available:

command -v jq

If not found, tell the user how to install it:

• macOS: brew install jq
• Linux (apt): sudo apt install jq
• Linux (conda): conda install jq
• Windows: winget install jqlang.jq

For data loading and SQL queries, the attach-db and query skills must be installed (optionally install-duckdb too). Install them from duckdb/duckdb-skills.

Workflow overview

1. Locate the descriptor — find or download datapackage.json (see below).
2. Query metadata selectively — use jq to extract only what you need. See Metadata Querying.
3. Surface warnings — always check for usage warnings before presenting a resource.
4. Check keys before joining or aggregating — if the task combines two resources, or rolls one up, look up schema.primaryKey and schema.foreignKeys on each first, rather than joining on a same-named or similar-looking column. See Metadata Querying: Joining resources.
5. Validate (optional) — if the user wants to know whether the data actually matches the descriptor, or if you're diagnosing a suspicious package, use frictionless validate. See Frictionless Validate.
6. Load the data (optional) — only if the user explicitly wants to query or explore the actual data. Data files can be large and remote access can be slow or costly. Don't initiate data loading as a follow-on to a metadata lookup without confirming the user wants it. See Storage Backends.

Reference index

• Metadata Querying — locate the descriptor, query it selectively with jq, surface usage warnings
• Storage Backends — load data from Parquet, DuckDB, SQLite, or CSV files referenced by the descriptor
• Frictionless Validate — use the frictionless CLI to validate packages, check data quality, infer schemas, and diagnose unfamiliar descriptors; read when the user wants to validate a descriptor, check if data matches its schema, or understand what the frictionless tool can tell them about a package

Community patterns and recipes

The datapackage standard is permissive: publishers frequently add non-standard fields. Two conventions are worth knowing immediately:

• Custom fields — non-standard keys added by publishers are common and valid. The _ prefix convention marks system-generated or platform-specific keys (e.g. _cache, _platformVersion). Some publishers add custom keys without the prefix (e.g. a package-level unit registry, or per-resource provenance metadata). Treat unknown fields as informational metadata, not errors.
• Compressed resources — a resource with a .gz or .zip path may have an explicit "compression": "gz" field. The bytes and hash fields apply to the compressed file, not the uncompressed original.

For other patterns (catalogs, versioning, external foreign keys, translation support, field relationships, etc.), fetch the relevant page on demand:

• v1 patterns: https://specs.frictionlessdata.io/patterns/
• v2 recipes: https://datapackage.org/recipes/caching-of-resources/ (navigate via sidebar or next/previous links — no index page exists)

Both pages cover largely the same set of community conventions; consult whichever matches the descriptor version you're working with.

Companion skills

This skill delegates actual data querying to:

• /attach-db — attach a .duckdb or .sqlite database file and set up a persistent session for querying
• /query — run SQL or natural language queries against attached databases, ad-hoc files (Parquet, CSV, remote HTTPS/S3), and JSON files including datapackage.json itself (via DuckDB's read_json)

These skills must be installed. See skills-lock.json in the project root.

Key constraints

• Golden rule: never load the full datapackage.json into context. It may be megabytes with hundreds of resources. Always query selectively.
• Read the full description before presenting a resource. Descriptions often contain important context: processing notes, primary key conventions, data provenance, or caveats about known limitations. Don't skip them.
• Use uv to install Python packages — prefer uv add <package> over pip install <package>. uv is faster and installs into a virtual environment rather than globally. Fall back to pip only if uv is not available (command -v uv returns nothing).
• Do not use Python to query descriptor metadata. Python is not the right tool here — it loads the full JSON into memory (violating the golden rule above), adds unnecessary dependencies, and can't easily handle remote descriptors. Use jq for metadata-only tasks; use DuckDB when you need to combine metadata queries with data queries. Python is only appropriate for loading data (via pandas or polars) after you already know which table and columns you need.

Schema reference and version detection

Two versions of the Frictionless Data Package standard are in common use. Identify the version from the top-level descriptor before parsing:

Field present	Version	Example value
"$schema"	v2.0	"https://datapackage.org/profiles/2.0/datapackage.json"
"profile"	v1.0	"tabular-data-package" or "data-package"
neither	ambiguous (treat as v1 baseline)	—

Key differences between versions that affect parsing:

• Contributors — v1 has "role": "author" (singular string); v2 has "roles": ["author"] (array). Both may appear in the wild.
• Name pattern — v1 enforces strictly lowercase [-a-z0-9._/]; v2 is unrestricted.
• version field — present in v2, absent in v1.

Bundled schemas:

• assets/datapackage-v1.schema.json — v1.0 (JSON Schema draft-04). Used by FERC XBRL packages and many older datasets.
• assets/datapackage-v2.schema.json — v2.0 (JSON Schema draft-07). The current standard. Canonical version always at: https://datapackage.org/profiles/2.0/datapackage.json

Read the appropriate schema when you need to understand which fields are valid in a descriptor or validate one programmatically.