---

name: dst-list-tables description: List all Danmarks Statistik tables currently stored in DuckDB with metadata. Use when user wants to know what data is available locally or explore stored tables.

DST List Tables Skill

Purpose

Discover what DST data is currently stored in the local DuckDB database. This is typically the first step in any analysis workflow - finding out what data you have to work with.

When to Use

• User asks "what data do we have?"
• Starting an analysis session
• Checking if specific data exists locally
• Exploring available tables
• Before deciding what to analyze
• Verifying data was stored successfully after fetch

How to Use

List All Tables

Show all tables stored locally:

python scripts/db/query_metadata.py --list-all

Get Specific Table Metadata

Get detailed metadata for one table:

python scripts/db/query_metadata.py --table-id <TABLE_ID>

JSON Output

Get machine-readable output:

python scripts/db/query_metadata.py --list-all --format json

Expected Output

List All Tables Format

Console-friendly table view:

================================================================================
DST TABLES IN LOCAL DATABASE
================================================================================

Found 3 table(s):

ID              Name                                Records    Age
--------------------------------------------------------------------------------
FOLK1A          Population at first day             45231      5 days ago
AUP01           Employment statistics               12450      2 weeks ago
NABB3           Business statistics                 8900       1 month ago
================================================================================

Metadata Fields

For each table, you'll see:

• table_id: Unique identifier (use in queries as dst_{id})
• table_name: Human-readable description
• record_count: Number of rows in the table
• last_updated: When DST last updated the source data
• fetch_timestamp: When we downloaded it
• age: How old our local copy is (human-readable)

Example Metadata Output

======================================================================
METADATA FOR TABLE: FOLK1A
======================================================================
Table ID:        FOLK1A
Table Name:      Population at the first day of the quarter
Record Count:    45231
Last Updated:    2025-10-15T10:30:00
Fetch Timestamp: 2025-10-25T09:15:00
Data Age:        5 days ago
======================================================================

Interpreting Results

Table ID

• Use this for all queries
• Format in DuckDB: dst_{table_id} (lowercase)
• Example: FOLK1A → query as dst_folk1a

Record Count

• Indicates dataset size
• Larger counts mean more data to analyze
• Compare with expected size to verify completeness

Fetch Timestamp

• When data was downloaded from DST
• Indicates freshness of local copy
• Compare with last_updated to see if source changed

Age

• Human-readable freshness indicator
• "5 days ago" means data is 5 days old locally
• Consider refreshing if too old for your needs

Next Steps

If Data Exists

Use query and analysis skills:

# Get table summary
python scripts/db/table_summary.py --table-id <TABLE_ID>

# Run SQL query
python scripts/db/query_data.py --sql "SELECT * FROM dst_<table_id> LIMIT 10"

If Data Missing

Switch to Fetcher Agent to download:

python scripts/fetch_and_store.py --table-id <TABLE_ID>

If Data Stale

Use dst-check-freshness skill to determine if refresh needed:

python scripts/db/query_metadata.py --table-id <TABLE_ID> --check-freshness --max-age-days 30

Examples

Example 1: List all stored tables

python scripts/db/query_metadata.py --list-all

Example 2: Get specific table metadata

python scripts/db/query_metadata.py --table-id FOLK1A

Example 3: JSON output for programmatic use

python scripts/db/query_metadata.py --list-all --format json

Example 4: Check multiple tables

# List all
python scripts/db/query_metadata.py --list-all

# Get details for each interesting table
python scripts/db/query_metadata.py --table-id FOLK1A
python scripts/db/query_metadata.py --table-id AUP01

Tips

At Session Start

• Always run --list-all first to see what you have to work with
• Note table IDs for tables you want to analyze
• Check ages to identify stale data

Data Quality

• Verify record counts seem reasonable
• Check fetch timestamps for freshness
• Compare last_updated vs fetch_timestamp

Table Naming

• Remember: table IDs become dst_{id} in lowercase
• FOLK1A → dst_folk1a
• AUP01 → dst_aup01

Empty Database

If no tables shown:

1. Verify database file exists
2. Use Fetcher Agent to download data
3. Check fetch operations completed successfully

Common Workflows

Workflow 1: Start Analysis

# 1. See what's available
python scripts/db/query_metadata.py --list-all

# 2. Get details on interesting table
python scripts/db/query_metadata.py --table-id FOLK1A

# 3. Check data age
python scripts/db/query_metadata.py --table-id FOLK1A --check-freshness

# 4. Proceed with analysis
python scripts/db/table_summary.py --table-id FOLK1A

Workflow 2: Verify After Fetch

# 1. Fetch data
python scripts/fetch_and_store.py --table-id FOLK1A

# 2. Verify it's there
python scripts/db/query_metadata.py --table-id FOLK1A

# 3. Check record count is reasonable

Troubleshooting

"No tables found"

• Database may be empty
• Verify database file exists at path in .env
• Use Fetcher Agent to download data

"Table not found"

• Check spelling of table ID (case-sensitive)
• Run --list-all to see what exists
• Table may not have been fetched yet

Unexpected Record Counts

• May indicate partial fetch
• Check fetch logs for errors
• Consider re-fetching with --overwrite

Old Fetch Timestamps

• Data may be stale
• Use dst-check-freshness to evaluate
• Consider refreshing with Fetcher Agent