SDMX Explorer — Guided Dataset Discovery

This skill uses the opensdmx CLI to explore any SDMX 2.1 REST endpoint: Eurostat, OECD, ECB, World Bank, ISTAT, and others. The primary reference provider is Eurostat (default in the opensdmx CLI). All examples use Eurostat unless stated otherwise.

Every

opensdmx

--help

opensdmx --help                  # list all commands
opensdmx search --help           # options and examples for search
opensdmx constraints --help      # options and examples for constraints
opensdmx get --help              # options and examples for get
# ... same for info, values, constraints, embed, blacklist, plot

This skill runs a four-phase interactive loop. Always follow the phases in order. The goal is to help the user understand the data landscape and make informed choices, not to fetch data immediately.

Phase 1 — Discovery: find candidate dataflows

Identify which SDMX provider is relevant (ISTAT for Italian statistics, Eurostat for European statistics, OECD for international comparisons, etc.). If unclear, ask.

Extract 2–4 meaningful keywords from the user's question. Then search for dataflows:

• Eurostat (default provider — no

  --provider

  flag needed):

  opensdmx search "<keyword>"

• ISTAT:

  opensdmx search "<keyword>" --provider istat

• Other providers:

  opensdmx search "<keyword>" --provider <name>

  (available:

  oecd

  ,

  ecb

  ,

  worldbank

  ,

  insee

  ,

  bundesbank

  ,

  abs

  )

From the results, select 3–5 candidates that are genuinely relevant (not just keyword matches). For each, write a short explanation of what it contains and why it might answer the user's question.

Present them like this (use the conversation language; adapt as needed):

I found these datasets that could answer your question:

1. **demo_gind** — Demographic balance and crude rates (Eurostat)
   Contains births, deaths, migration balance and demographic rates for EU countries,
   with annual time series from 1960. Best for European comparisons.

2. **demo_nsinagec** — Live births by mother's age (Eurostat)
   Births broken down by mother's age group and country. Useful if you want
   age-specific fertility analysis rather than totals.

3. **APRO_CPNH1** — Crop production in national humidity (Eurostat)
   Agricultural production data by crop type, country and structure indicator
   (area, harvested production, yield). Covers 40+ European countries annually.

Which one would you like to explore? You can also say "the first one" or
describe more precisely what you need.

Wait for the user's choice before proceeding.

Phase 2 — Schema: explore the chosen dataflow

Once the user has chosen, retrieve the structure and available codes for the dataflow.

Default flow (Eurostat, OECD, ECB, etc.)

Step 1 — get the codes actually present in the dataflow (real constraints):

opensdmx constraints PRC_HICP_MANR
# shows all dimensions with count and sample of codes

opensdmx constraints PRC_HICP_MANR coicop
# shows full list of codes present in that dimension, with labels

opensdmx constraints

availableconstraint

Step 2 — get dimension order and structure:

opensdmx info PRC_HICP_MANR
# (no --provider needed for Eurostat, it's the default)

opensdmx values

opensdmx constraints

Never use

opensdmx values

ISTAT fast flow (recommended)

ISTAT's

availableconstraint

Step 1 — get dimension order and structure:

opensdmx info <dataflow_id> --provider istat

Step 2 — explore codelist values for the dimensions you need to filter:

opensdmx values <dataflow_id> REF_AREA --provider istat
opensdmx values <dataflow_id> DATA_TYPE --provider istat

values

grep -i

Step 3 — go directly to

get

opensdmx get <dataflow_id> --provider istat --REF_AREA <code> --last-n 1

If the query returns a 404 or empty result, the code may not be present in this dataflow. Only then fall back to

opensdmx constraints

Extract from both flows

Parse the output and extract:

• Dimension list in order (position matters for URL construction later)
• Available codes for each dimension, with descriptions
• Time range (StartPeriod / EndPeriod)
• Dimensions with more than one available value (these are the meaningful filters)

Phase 3 — Presentation: explain the dataset to the user

Synthesize what you learned in Phase 2 into a clear, human-readable summary. The goal is for the user to understand the dataset without knowing SDMX.

Structure your summary like this:

What the dataset contains

Describe the subject matter in plain language.

Granularity

• Geographic: national only? Regions? EU countries? Global?
• Temporal: what years are available? Annual, monthly, quarterly?

Key dimensions to filter

List only the dimensions with more than one available value that are meaningful for the user's question. For each, show the options in plain language:

- **Country** (GEO): IT (Italy), DE (Germany), FR (France)… 35 countries
- **Indicator** (INDIC_DE): live births (GBIRTHS), deaths (DEATH),
  crude birth rate (CNBIRTHS), total fertility rate (TOTFERRT)…
- **Period**: 1960 to 2024, annual frequency

For dimensions with only one available value, mention them briefly: "Other dimensions have a single fixed value and are included automatically."

Estimated size

Give a rough sense of scale: "Downloading everything (all countries + all indicators + all years) would give you approximately X rows." This helps the user decide how to filter.

Invitation to choose

End with a clear prompt:

How would you like to proceed?
- Do you want data for a specific country or a European comparison?
- Which time period are you interested in?
- Are there any dimensions you want to filter?

Tell me what you want and I'll build the query.

Phase 4 — Data retrieval: after the user decides

Once the user has specified their choices, build the query and fetch the data.

Building the query — critical rules

1. Dimension order must match the

   opensdmx info

   output exactly. Never guess the order.
2. Use only codes confirmed by

   opensdmx constraints

   , never codes from

   opensdmx values

   or other sources. Providers often return 404 or empty results for invalid codes.
3. For dimensions with a single available value, include that value — don't skip them.
4. For unfiltered dimensions (user wants all values), use

   .

   as wildcard.

Note: Eurostat dimension flags are lowercase (

--geo

--coicop

--freq

--REF_AREA

--DATA_TYPE

--FREQ

Step 1 — Verify with a preview (last observation)

Before fetching everything, do a quick sanity check with

--last-n 1

opensdmx get PRC_HICP_MANR --coicop CP00 --geo IT+DE+FR --start-period 2020 --end-period 2023 --last-n 1

--last-n 1

--last-n 1

--first-n N

For ISTAT: use a narrow time range (1–2 years) as preview.

Show the user those few rows and confirm the data makes sense (right columns, right units, no unexpected flags). A one-line comment is enough: "Query works — here is the latest observation per series."

Step 2 — Provide the download URL

Build and show the equivalent curl command so the user can download the full dataset independently, without relying on the CLI:

Eurostat URL pattern:

https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/{dataflow_id}/{dim1.dim2...}/ALL/?startPeriod={start}&endPeriod={end}&format=SDMX-CSV

Dimension values in the path must follow the exact order from

opensdmx info

.

+

Example:

curl "https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/APRO_CPNH1/A.I2200.AR./ALL/?startPeriod=2014&endPeriod=2023&format=SDMX-CSV"

ISTAT URL pattern:

https://esploradati.istat.it/SDMXWS/rest/data/{dataflow_id}/{dim1.dim2...}?startPeriod={start}&endPeriod={end}

Step 3 — Ask the user what to do next

End with a short, clear question:

Would you like me to download and save the full dataset locally for analysis?
If yes, tell me where to save it (e.g. /tmp/data.csv) or I'll use a default path.
I can then run a quick analysis: row count, top values, time range, flagged records.

If the user says yes, download with

--out <path>

Step 4 — Offer metadata and README

After downloading, always ask the user two optional extras:

Two optional extras:
1. **Full metadata**: do you want the complete list of codes and labels for each
   dimension (e.g. all country names, all flag meanings)? I can extract them from
   the opensdmx cache and save them as a companion file (e.g. `metadata.csv`).
2. **README**: do you want a `README.md` that documents the dataset schema —
   columns, dimension codes with labels, flag meanings, units, and source URL?
   Useful if you plan to share the data or revisit it later.

If the user says yes to metadata:

• Run

  opensdmx constraints <dataflow_id> <dim>

  for each dimension with more than one value to get the full code → label mapping.
• Combine all dimensions into a single metadata file with columns:

  dimension

  ,

  code

  ,

  label

  .
• Save it alongside the data file (e.g.

  tomato_production_metadata.csv

  ).

If the user says yes to README:

• Generate a

  README.md

  in the same folder as the data file.
• Include:
  • Dataset name and ID
  • Provider and source URL (the curl URL from Step 2)
  • Last update timestamp (from the

    LAST UPDATE

    column in the data)
  • Column descriptions: for each column in the CSV, explain what it contains
  • Dimension tables: for each dimension, list codes and labels (from constraints or the metadata file if already generated)
  • Flag legend: list all

    OBS_FLAG

    values found in the data with their meaning
  • Units: clearly state the unit of measurement for

    OBS_VALUE

  • Any caveats noted during analysis (gaps, estimated values, provisional data)

Step 5 — Visualization

After downloading data, offer to create charts using

opensdmx plot

For the complete visualization reference — Grammar of Graphics concepts, data preparation rules, DuckDB examples, iterative chart quality loop, and common fixes — see references/visualization.md.

Supported chart types (via

--geom

• line

  (default): line chart with points — best for time series

• bar

  : vertical bar chart — best for comparing values across categories over time; with

  --color

  produces stacked bars

• barh

  : horizontal bar chart — best for rankings; bars are automatically sorted by value (lowest at bottom, highest at top)

• point

  : scatter plot — best for correlations between two numeric variables

For other chart types (heatmaps, faceted plots), write a short Python script using plotnine directly.

Key points:

• Always prepare data with DuckDB before plotting (separate units, limit series, remove aggregates, use year strings for annual data)
• After generating a chart, read the image and evaluate it — if it's not good, fix it yourself before showing the user
• Multiple focused charts are better than one overloaded chart

Key principles

Constraints vs codelists — always use constraints Use

opensdmx constraints

opensdmx values

Proposals, not lists When presenting dataflow candidates, reason about each one: explain why it might or might not answer the question, what its limitations are, and which one you'd recommend. The user should feel guided, not overwhelmed.

Explain dimensions in plain language Translate SDMX dimension IDs into human concepts:

• CITIZENSHIP_MOTHER

  → "mother's citizenship"

• DATA_TYPE: LBIRTH

  → "live births (absolute count)"

• GEO: IT

  → "Italy"

• INDIC_DE: GBIRTHS

  → "live births"

• INDIC_DE: CNBIRTHS

  → "crude birth rate (per 1,000 inhabitants)" Never show raw codes without an explanation.

Explore all columns, not just the value column When the preview arrives (Step 1 of Phase 4), look at all columns in the response, not just the observation value. SDMX datasets often include extra columns that affect interpretation: quality flags, confidentiality markers, unit multipliers, notes. For each non-obvious column, check what values are present and explain their meaning to the user. For example:

• OBS_FLAG

  or

  OBS_STATUS

  : quality/availability flags — look up what each code means in the context of that provider (

  b

  = break in series,

  e

  = estimated,

  n

  = not significant,

  u

  = unreliable,

  p

  = provisional, etc.)

• UNIT_MULT

  : multiplier applied to the value (e.g.

  3

  means values are in thousands)

• CONF_STATUS

  : confidentiality status

• NOTE_*

  : free-text annotations attached to specific dimensions

Don't hardcode these — inspect what columns are actually present in the data and explain the ones that are populated. Skip columns that are entirely empty.

Provider-specific quirks

--provider istat

25_74

25_74_DF_DCIS_NATI2_1

availableconstraint

--provider

--geo

--coicop

EU27_2020

--provider oecd

--provider ecb

--provider worldbank

Territory resolution (Eurostat) Country codes follow ISO 3166-1 alpha-2:

IT

DE

FR

ES

EU27_2020

EA20

opensdmx constraints

Territory resolution (ISTAT) ISTAT uses numeric REF_AREA codes (6-digit municipal codes, province codes, region codes, and aggregate codes like

ITG12

SLL_*

opensdmx values <dataflow_id> REF_AREA --provider istat

grep -i

opensdmx values <dataflow_id> REF_AREA --provider istat 2>&1 | grep -i "palermo\|matera"

Avoid

opensdmx constraints <dataflow_id> REF_AREA --provider istat

availableconstraint

values

grep

get