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Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statisti
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill datacommons-client -a cursor -y
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
Provides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.
Use this skill when the user explicitly asks for Data Commons/datacommons, Data Commons statistical variables, Data Commons entities/DCIDs, or population/economic indicators that fit the Data Commons knowledge graph.
Generic public data, open data, public dataset search, or public download-link collection by itself is not enough to select this skill. Those requests need a clearer Data Commons/statistical-graph signal before this skill becomes the route owner.
Install the Data Commons Python client with Pandas support:
uv pip install "datacommons-client[Pandas]"
For basic usage without Pandas:
uv pip install datacommons-client
The Data Commons API consists of three main endpoints, each detailed in dedicated reference files:
Query time-series statistical data for entities. See references/observation.md for comprehensive documentation.
Primary use cases:
Common patterns:
from datacommons_client import DataCommonsClient
client = DataCommonsClient()
# Get latest population data
response = client.observation.fetch(
variable_dcids=["Count_Person"],
entity_dcids=["geoId/06"], # California
date="latest"
)
# Get time series
response = client.observation.fetch(
variable_dcids=["UnemploymentRate_Person"],
entity_dcids=["country/USA"],
date="all"
)
# Query by hierarchy
response = client.observation.fetch(
variable_dcids=["MedianIncome_Household"],
entity_expression="geoId/06<-containedInPlace+{typeOf:County}",
date="2020"
)
Explore entity relationships and properties within the knowledge graph. See references/node.md for comprehensive documentation.
Primary use cases:
Common patterns:
# Discover properties
labels = client.node.fetch_property_labels(
node_dcids=["geoId/06"],
out=True
)
# Navigate hierarchy
children = client.node.fetch_place_children(
node_dcids=["country/USA"]
)
# Get entity names
names = client.node.fetch_entity_names(
node_dcids=["geoId/06", "geoId/48"]
)
Translate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs). See references/resolve.md for comprehensive documentation.
Primary use cases:
Common patterns:
# Resolve by name
response = client.resolve.fetch_dcids_by_name(
names=["California", "Texas"],
entity_type="State"
)
# Resolve by coordinates
dcid = client.resolve.fetch_dcid_by_coordinates(
latitude=37.7749,
longitude=-122.4194
)
# Resolve Wikidata IDs
response = client.resolve.fetch_dcids_by_wikidata_id(
wikidata_ids=["Q30", "Q99"]
)
Most Data Commons queries follow this pattern:
Resolve entities (if starting with names):
resolve_response = client.resolve.fetch_dcids_by_name(
names=["California", "Texas"]
)
dcids = [r["candidates"][0]["dcid"]
for r in resolve_response.to_dict().values()
if r["candidates"]]
Discover available variables (optional):
variables = client.observation.fetch_available_statistical_variables(
entity_dcids=dcids
)
Query statistical data:
response = client.observation.fetch(
variable_dcids=["Count_Person", "UnemploymentRate_Person"],
entity_dcids=dcids,
date="latest"
)
Process results:
# As dictionary
data = response.to_dict()
# As Pandas DataFrame
df = response.to_observations_as_records()
Statistical variables use specific naming patterns in Data Commons:
Common variable patterns:
Count_Person - Total populationCount_Person_Female - Female populationUnemploymentRate_Person - Unemployment rateMedian_Income_Household - Median household incomeCount_Death - Death countMedian_Age_Person - Median ageDiscovery methods:
# Check what variables are available for an entity
available = client.observation.fetch_available_statistical_variables(
entity_dcids=["geoId/06"]
)
# Or explore via the web interface
# https://datacommons.org/tools/statvar
All observation responses integrate with Pandas:
response = client.observation.fetch(
variable_dcids=["Count_Person"],
entity_dcids=["geoId/06", "geoId/48"],
date="all"
)
# Convert to DataFrame
df = response.to_observations_as_records()
# Columns: date, entity, variable, value
# Reshape for analysis
pivot = df.pivot_table(
values='value',
index='date',
columns='entity'
)
For datacommons.org (default):
export DC_API_KEY="your_key"client = DataCommonsClient(api_key="your_key")For custom Data Commons instances:
client = DataCommonsClient(url="https://custom.datacommons.org")Comprehensive documentation for each endpoint is available in the references/ directory:
references/observation.md: Complete Observation API documentation with all methods, parameters, response formats, and common use casesreferences/node.md: Complete Node API documentation for graph exploration, property queries, and hierarchy navigationreferences/resolve.md: Complete Resolve API documentation for entity identification and DCID resolutionreferences/getting_started.md: Quickstart guide with end-to-end examples and common patternsfetch_available_statistical_variables() to see what's queryablefilter_facet_domains to ensure data from the same sourcereferences/ directory