Is us-census-data safe to install?

SaferSkills independently audited us-census-data (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

skills/us-census-data/SKILL.md · 1 file

skills/us-census-data/SKILL.md7.6 KB · Markdown

name: us-census-data

description: This skill should be used when the user asks to "get Census data", "query American Community Survey", "find ACS data", "get population by state", "query Decennial Census", "find Census variables", "get median income data", "download demographic data", "Census API query", "get housing data from Census", or mentions US Census Bureau data (demographics, income, poverty, education, housing, population estimates, etc.).

version: 1.1.0

US Census Data Skill

Query demographic, economic, housing, and population data from the US Census Bureau API. Supports American Community Survey (ACS), Decennial Census, and Population Estimates.

Prerequisites

Before querying, check if the user has an API key:

Look for CENSUS_API_KEY in the user's .env file
If found, append to requests: &key=<api_key>
If not found, instruct the user to:

Register at https://api.census.gov/data/key_signup.html
Add to .env: CENSUS_API_KEY=your_key_here

The API works without a key for testing but is rate-limited and may block requests.

Quick Start

The Census API is at api.census.gov. Queries require:

Dataset path: /data/{year}/{dataset} (e.g., /data/2022/acs/acs5)
Variables: ?get=NAME,B01001_001E (variable codes)
Geography: &for=county:*&in=state:17 (FIPS codes)
API Key: &key=YOUR_KEY (required for production use)

Workflow

Step 1: Clarify the Data Need

Ask the user:

Topic: What data? (population, income, housing, education, poverty, etc.)
Geography: What level? (nation, state, county, tract, block group, ZCTA?)
Time period: Which year(s)?
Dataset: ACS 5-year (most common), ACS 1-year, or Decennial Census?

Step 2: Choose the Dataset

Need	Dataset	Endpoint	Notes
Detailed demographics (small areas)	ACS 5-Year	`/data/{year}/acs/acs5`	Block group+, most reliable
Recent data (large areas only)	ACS 1-Year	`/data/{year}/acs/acs1`	65k+ population areas only
Exact population counts	Decennial	`/data/2020/dec/dhc`	Every 10 years, block level
Annual population change	Pop Estimates	`/data/{year}/pep/population`	County+, intercensal

See references/datasets.md for complete dataset documentation.

Step 3: Find Variables

Census variables use codes like B19013_001E (median household income). The E suffix = estimate, M suffix = margin of error.

Option A - Popular Variables Reference: See references/popular-variables.md for curated list of common variables.

Option B - Variable Search API:

GET https://api.census.gov/data/2022/acs/acs5/variables.json

Option C - Groups (Tables) API:

GET https://api.census.gov/data/2022/acs/acs5/groups.json

See references/datasets.md for common table prefixes and naming conventions.

Step 4: Build the Query

Basic structure:

https://api.census.gov/data/{year}/{dataset}?get={variables}&for={geography}&key={api_key}

Example - Median income by county in Illinois:

https://api.census.gov/data/2022/acs/acs5?get=NAME,B19013_001E&for=county:*&in=state:17&key=YOUR_KEY

Geography predicates:

for=state:* - All states
for=county:*&in=state:17 - All counties in Illinois
for=tract:*&in=state:17&in=county:031 - All tracts in Cook County, IL
for=block%20group:*&in=state:17&in=county:031&in=tract:010100 - Block groups in tract

See references/geographies.md for complete geography hierarchy and FIPS codes.

Step 5: Handle Response

Census API returns JSON array where first row is column headers:

[
  ["NAME", "B19013_001E", "state", "county"],
  ["Cook County, Illinois", "62088", "17", "031"],
  ["DuPage County, Illinois", "99007", "17", "043"]
]

For large queries, the API has no built-in pagination. Request specific geographies or use &in= filters.

Query Syntax Reference

Parameters

Parameter	Purpose	Example
`get`	Variables to return	`get=NAME,B01001_001E,B19013_001E`
`for`	Target geography	`for=county:*` or `for=county:031`
`in`	Parent geography filter	`in=state:17`
`key`	API key	`key=abc123`

Variable Syntax

Request multiple: get=NAME,B01001_001E,B19013_001E (comma-separated)
Request table: get=group(B19013) (all variables in table)
Include MOE: get=B19013_001E,B19013_001M

Geography Wildcards

Use * for "all" at that level:

for=state:* - All 50 states + DC + territories
for=county:*&in=state:06 - All counties in California
for=tract:*&in=state:06&in=county:037 - All tracts in LA County

Common Query Patterns

State-level totals:

curl "https://api.census.gov/data/2022/acs/acs5?get=NAME,B01001_001E&for=state:*&key=$CENSUS_API_KEY"

County-level within a state:

curl "https://api.census.gov/data/2022/acs/acs5?get=NAME,B19013_001E&for=county:*&in=state:17&key=$CENSUS_API_KEY"

Tract-level (requires state + county):

curl "https://api.census.gov/data/2022/acs/acs5?get=NAME,B01001_001E&for=tract:*&in=state:17&in=county:031&key=$CENSUS_API_KEY"

Multiple variables:

curl "https://api.census.gov/data/2022/acs/acs5?get=NAME,B01001_001E,B19013_001E,B25001_001E&for=county:*&in=state:17&key=$CENSUS_API_KEY"

Output Format

Provide the user with:

Dataset: Name + endpoint + year
Variables: Code + label for each
Geography: Level + filters
Query: Full URL or curl command
Caveats: MOE notes, update frequency, limitations

Example Response Format

Dataset: ACS 5-Year 2022
Endpoint: https://api.census.gov/data/2022/acs/acs5

Variables:
- NAME: Geography name
- B19013_001E: Median Household Income (estimate)
- B19013_001M: Median Household Income (margin of error)

Geography: All counties in Illinois (state FIPS 17)

Query:
curl "https://api.census.gov/data/2022/acs/acs5?get=NAME,B19013_001E,B19013_001M&for=county:*&in=state:17&key=$CENSUS_API_KEY"

Note: ACS data are estimates with margins of error. 5-year estimates are most reliable for small geographies.

Troubleshooting

Issue	Cause	Fix
400 Bad Request	Invalid variable or geography	Check variable exists for that dataset/year
204 No Content	Valid query, no matching data	Geography may not exist or have data
Empty array `[]`	No data for that geography	Try different year or geography level
"error: unknown variable"	Variable doesn't exist	Use variables.json endpoint to verify
Missing data for small areas	ACS 1-year limitation	Use ACS 5-year for areas <65k population

Margins of Error (ACS Data)

ACS estimates have sampling error. Always consider:

E suffix = estimate, M suffix = margin of error (90% confidence)
Small geographies have larger MOEs
5-year estimates are more reliable than 1-year
Decennial Census has no MOE (100% count)

See references/datasets.md for MOE calculation guidance.

Geometry (TIGERweb)

Census data API returns tabular data only. For geographic boundaries, use TIGERweb:

See references/tigerweb.md for geometry retrieval and data joining.

Additional Resources

Reference Files

`references/datasets.md` - Complete dataset documentation (ACS, Decennial, PEP)
`references/popular-variables.md` - Curated list of common variables by topic
`references/geographies.md` - Geography hierarchy, FIPS codes, nesting rules
`references/tigerweb.md` - Boundary/geometry retrieval via TIGERweb API

Example Files

`examples/python-query.py` - Python code with requests library
`examples/curl-examples.sh` - curl command templates

us-census-data— agent skill

Is us-census-data safe to install?

US Census Data Skill

Prerequisites

Quick Start

Workflow

Step 1: Clarify the Data Need

Step 2: Choose the Dataset

Step 3: Find Variables

Step 4: Build the Query

Step 5: Handle Response

Query Syntax Reference

Parameters

Variable Syntax

Geography Wildcards

Common Query Patterns

Output Format

Example Response Format

Troubleshooting

Margins of Error (ACS Data)

Geometry (TIGERweb)

Additional Resources

Reference Files

Example Files

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