Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
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NHGIS — census geography crosswalks via Portal: links schools (ncessch) and colleges (unitid) to census tracts, block groups, CBSAs, and regions (census-geography vintages 1990-2020; mirror file-year/linkage coverage 1980-2023). Portal provides geography linkage tables ONLY; browse census demographics in the public NHGIS Data Finder, then sign in with a free IPUMS NHGIS account to submit, manage, or download extracts. API extract requests also need an API key. Use for linking education or institutional data to census geography.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
IPUMS NHGIS — census geography crosswalks and demographic data for education research. Via the Education Data Portal: geographic crosswalk tables linking K-12 schools (ncessch) and colleges (unitid) to census tracts, block groups, CBSAs, and regions. The 1990-2020 here denotes the census-geography vintages the crosswalks resolve to; the mirror files themselves span 1980-2023 file-years (the education/institution linkage years). See datasets-reference.md for per-file year ranges. Census demographic variables (income, poverty, race, educational attainment) are NOT in the Portal. Browse those variables publicly in the NHGIS Data Finder; sign in with a free IPUMS NHGIS account to submit, manage, or download extracts, and use an API key for API extract requests. Use when linking school or institutional data to census geography for contextual analysis.
Census geography and demographic data source for education research. NHGIS provides the foundation for linking schools to community characteristics via census tracts, block groups, and school district boundaries.
CRITICAL: Value Encoding
When accessing NHGIS data through the Education Data Portal (not NHGIS directly), categorical variables use integer encodings, not string labels. Always verify the exact codes in the mirror codebook.
Variable Integer Code Meaning census_region1Northeast census_region2Midwest census_region3South census_region4West cbsa_type1Metropolitan cbsa_type2Micropolitan geocode_accuracy4Did not geocode See
./references/variable-catalog.mdfor complete encoding tables.
CRITICAL: Portal Data Scope
The Education Data Portal provides ONLY geographic crosswalk tables that link schools and colleges to census geography (tracts, block groups, regions, CBSAs). These contain geographic identifiers and assignment columns — approximately 35-47 columns per file.
The Portal does NOT provide census demographic data (population, income, poverty, race, education attainment, housing, language, etc.). For demographic variables, browse the public NHGIS Data Finder, then use a free IPUMS NHGIS account to submit, manage, or download an extract. API extract requests additionally require an API key. See
./references/data-access.mdfor direct access methods.This skill documents both contexts: Portal crosswalk data (with integer encodings above) and direct NHGIS census variables (in
./references/variable-catalog.md). Direct NHGIS products remain subject to separate IPUMS terms, citation requirements, and redistribution restrictions; the Portal's ODC-By terms do not automatically apply to them.
NHGIS (from IPUMS, University of Minnesota) provides free access to census geography and demographic data.
| File | Purpose | When to Read |
|---|---|---|
geographic-units.md | Census geography hierarchy (tracts, blocks, districts) | Understanding census geography |
school-geography-links.md | Linking schools to census areas | Connecting school data to demographics |
time-series.md | Historical data, harmonization methods | Longitudinal analysis |
variable-catalog.md | Key demographic variables, codes, special values | Selecting census variables or interpreting encodings |
boundary-changes.md | How boundaries change between censuses | Handling geographic inconsistencies |
data-access.md | Direct NHGIS access methods (registration, Data Finder, ipumspy) | Custom census analysis beyond Portal |
Research question about...
├─ Individual schools
│ ├─ School's immediate neighborhood → Census tract or block group
│ ├─ School attendance zone → SABINS (limited years) or block-to-school crosswalk
│ └─ School district overall → School district boundaries
├─ School districts
│ ├─ District-level demographics → School district geographic level
│ ├─ Within-district variation → Census tracts within district
│ └─ District poverty estimates → SAIPE (via Education Data Portal)
├─ Regional patterns
│ ├─ County-level → County boundaries
│ ├─ Metro area → CBSA (Core Based Statistical Area)
│ └─ State-level → State boundaries
└─ Historical analysis
├─ Consistent boundaries needed → Geographically standardized tables
└─ Original boundaries OK → Nominally integrated tables
Linking schools to census demographics?
├─ Have school coordinates (lat/lon)
│ ├─ Point-in-polygon → Spatial join to tract/block group boundaries
│ └─ Need tract ID only → Geocoding service or FCC API
├─ Have school NCES ID only
│ ├─ Use NCES EDGE files → School District Geographic Relationship Files
│ └─ Use Education Data Portal → NHGIS source provides tract links
├─ Need school attendance zones
│ ├─ 2009-2012 data → SABINS school areas
│ └─ Current data → Contact school district (no national source)
└─ See ./references/school-geography-links.md for details
Time period?
├─ Single recent year
│ ├─ Tract/block group level → ACS 5-year (most recent)
│ ├─ Larger areas (65K+ pop) → ACS 1-year
│ └─ Full census count → 2020 Decennial Census
├─ Historical comparison
│ ├─ Same boundaries across time → Geographically standardized tables (to 2010)
│ ├─ Original boundaries → Nominally integrated time series
│ └─ Custom standardization → Use geographic crosswalks
├─ Long time series (1970+)
│ └─ See ./references/time-series.md
└─ Pre-1970
└─ Limited tract coverage; county/state more complete
| Level | Typical Size | Education Use | NHGIS Coverage |
|---|---|---|---|
| Block | ~40 people | Point locations | 1990-2020 |
| Block Group | ~1,500 people | School neighborhoods | 1990-2020 |
| Census Tract | ~4,000 people | Community context | 1910-2020 |
| County Subdivision | Varies | Rural areas | 1980-2020 |
| Place | City/town | Urban context | 1980-2020 |
| School District | Varies | District analysis | 2000-2020 |
| County | ~100,000 people | Regional patterns | 1790-2020 |
| State | Varies | Policy analysis | 1790-2020 |
| ID | Format | Level | Example | Notes |
|---|---|---|---|---|
ncessch | Int64 | School | 10000201704 | NCES school ID (schools Portal data) |
unitid | Int64 | College | 100654 | IPEDS institution ID (colleges Portal data) |
GISJOIN | String with prefix | Any | G0600010 | NHGIS internal ID; use for direct NHGIS joins (not in Portal data) |
GEOID | Numeric string | Any | 06001402100 | Census Bureau standard; use for non-NHGIS joins (not in Portal data) |
tract | Int64 | Tract | 402100 | Census tract number (in Portal data) |
block_group | Int64 | Block Group | 1 | Block group within tract (1-9; 0=unassigned) |
geoid_block | Int64 | Block | 60014021001001 | Full block FIPS code (in Portal data — stored as Int64, not String) |
cbsa | Int64 | Metro area | 41860 | Core Based Statistical Area code (2000+ census files only) |
| Topic | Example Variables | Source |
|---|---|---|
| Child population | Under 18, 5-17 school-age | Decennial, ACS |
| Race/ethnicity | Hispanic, White, Black, Asian, etc. | Decennial, ACS |
| Poverty | Persons below poverty, SNAP receipt | ACS (sample) |
| Education attainment | HS diploma, BA+ (adults) | ACS (sample) |
| Language | English proficiency, language at home | ACS (sample) |
| Housing | Owner/renter, median value, crowding | Decennial, ACS |
| Family structure | Single-parent, grandparent households | ACS (sample) |
| Immigration | Foreign-born, recent immigrants | ACS (sample) |
| Source | Years | Geographic Detail | Content |
|---|---|---|---|
| Decennial Census | 1790-2020 | Block (1990+) | 100% count: age, sex, race, housing |
| ACS 5-Year | 2005-2023 | Block group | Sample: income, education, language |
| ACS 1-Year | 2010-2023 | Areas 65K+ pop | Sample: same as 5-year |
| Time Series | 1790-2020 | Varies | Harmonized across years |
| Geographic Crosswalks | 1990-2020 | Block+ | Interpolation weights |
Key geographic and identifying columns in the schools NHGIS datasets. Census 2020 files have 47 columns; earlier census years have fewer (e.g., 1990 has 35 columns — no CBSA or legislative district fields).
| Variable | Description | Type |
|---|---|---|
ncessch | NCES school ID | Int64 |
leaid | NCES district ID | Int64 |
tract | Census tract number | Int64 |
block_group | Block group number (1-9; 0 = unassigned) | Int64 |
geoid_block | Full block FIPS identifier | Int64 |
census_region | Census Bureau region (1-4, 9) | Int64 |
census_division | Census Bureau division (1-9) | Int64 |
cbsa | CBSA code (2000+ census files only) | Int64 |
cbsa_type | Metropolitan (1) or Micropolitan (2) | Int64 |
cbsa_city | Principal city indicator (0=No, 1=Yes; 2000+ only). See note below. | Int64 |
geocode_accuracy | Geocode confidence (1=High, 2=Medium, 3=Low, 4=Did not geocode, -2=N/A) | Float64 |
geocode_accuracy_detailed | Geocode match type (1-12) | Int64 |
class_code | FIPS place class code | Int64 |
lower_chamber_type | State legislative district lower chamber type (1-8; census 2010 only). See variable-catalog.md for code mapping. | Int64 |
geo_latitude / geo_longitude | Geocoded coordinates | Float64 |
latitude / longitude | CCD-reported coordinates (many nulls in early years) | Float64 |
fips | State FIPS code | Int64 |
puma | Public Use Microdata Area (2000+ census files only) | Int64 |
Colleges NHGIS datasets have 38 columns (2020 census). Different identifier set from schools.
| Variable | Description | Type |
|---|---|---|
unitid | IPEDS institution ID | Int64 |
opeid | Office of Postsecondary Education ID | String |
tract | Census tract number | Int64 |
block_group | Block group number (1-9) | Int64 |
geoid_block | Full block FIPS identifier | Int64 |
census_region | Census Bureau region (1-4, 9) | Int64 |
census_division | Census Bureau division (1-9) | Int64 |
cbsa | CBSA code | Int64 |
cbsa_type | Metropolitan (1) or Micropolitan (2) | Int64 |
cbsa_city | Principal city indicator (0=No, 1=Yes; 2000+ only) | Int64 |
geocode_accuracy | Geocode match score (Int64 in colleges, Float64 in schools) | Int64 |
county_fips | County FIPS code | Int64 |
county_name | County name | String |
state_abbr | State abbreviation | String |
| Code | Meaning | When Used |
|---|---|---|
-2 | Not geocoded | geocode_accuracy field in Portal data |
-1 | Missing/not reported | General missing data indicator (e.g., latitude, county_code) |
0 | Unassigned | block_group (rare, ~4 rows in schools) |
null | Not available | Variable not applicable to this record; many columns heavily null in early years |
Schema Difference: Schools NHGIS 2020 files (47 columns) have a different schema than colleges NHGIS 2020 files (38 columns). Schools data includes school-specific identifiers (
ncessch,leaid,school_name, mailing/location address fields) while colleges data includes institution-specific identifiers (unitid,opeid,inst_name,county_name). Both entity types have block-level geographic precision. Earlier census years have fewer columns (e.g., Schools 1990 has 35 columns — no CBSA or legislative district fields). Do not assume identical column structures when working across entities or census years.
Datasets for NHGIS are available via the mirror system. See datasets-reference.md for canonical paths, mirrors.yaml for mirror configuration, and fetch-patterns.md for fetch code patterns.
| Dataset | Type | Years | Path | Codebook |
|---|---|---|---|---|
| Schools Census 1990 | Single | 1986-2023 | nhgis/schools_nhgis_geog_1990 | nhgis/codebook_schools_nhgis_census1990 |
| Schools Census 2000 | Single | 1986-2023 | nhgis/schools_nhgis_geog_2000 | nhgis/codebook_schools_nhgis_census2000 |
| Schools Census 2010 | Single | 1986-2023 | nhgis/schools_nhgis_geog_2010 | nhgis/codebook_schools_nhgis_census2010 |
| Schools Census 2020 | Single | 1986-2023 | nhgis/schools_nhgis_geog_2020 | nhgis/codebook_schools_nhgis_census2020 |
| Colleges Census 1990 | Single | 1980-2023 | nhgis/colleges_nhgis_geog_1990 | nhgis/codebook_colleges_nhgis_census1990 |
| Colleges Census 2000 | Single | 1980-2023 | nhgis/colleges_nhgis_geog_2000 | nhgis/codebook_colleges_nhgis_census2000 |
| Colleges Census 2010 | Single | 1980-2023 | nhgis/colleges_nhgis_geog_2010 | nhgis/codebook_colleges_nhgis_census2010 |
| Colleges Census 2020 | Single | 1980-2023 | nhgis/colleges_nhgis_geog_2020 | nhgis/codebook_colleges_nhgis_census2020 |
Codebooks are .xls files co-located with data in all mirrors. Use get_codebook_url() from fetch-patterns.md to construct download URLs.
Truth Hierarchy: When interpreting variable values, apply this priority:
- Actual data file (what you observe in the parquet/CSV) — this IS the truth
- Live codebook (.xls in mirror) — authoritative documentation, may lag
- This skill documentation — convenient summary, may drift from codebook
If this documentation contradicts the codebook, trust the codebook. If the codebook contradicts observed data, trust the data and investigate.
import polars as pl
# Filter to a specific school
school_census = df.filter(pl.col("ncessch") == 10000201704)
# Filter to metropolitan areas only (cbsa_type only in 2000+ census files)
metro = df.filter(pl.col("cbsa_type") == 1)
# Filter to a specific census region (South)
south = df.filter(pl.col("census_region") == 3)
# Filter to a specific year
recent = df.filter(pl.col("year") == 2023)
library(dplyr)
# Filter to a specific school
school_census <- df |> filter(ncessch == 10000201704)
# Filter to metropolitan areas only (cbsa_type only in 2000+ census files)
metro <- df |> filter(cbsa_type == 1)
# Filter to a specific census region (South)
south <- df |> filter(census_region == 3)
# Filter to a specific year
recent <- df |> filter(year == 2023)
Direct-access note: The Portal provides pre-processed school/college-to-census-geography links. For custom census analysis (tract-level demographics, time series, boundary files), browse the NHGIS Data Finder without signing in; a free IPUMS NHGIS account and login are required to submit, manage, or download extracts, and API requests additionally need an API key. See
./references/data-access.md. Direct NHGIS data is governed by separate IPUMS terms and citation requirements.
| Pitfall | Issue | Solution |
|---|---|---|
| Boundary changes | Tracts split/merged between censuses break longitudinal analysis | Use crosswalks or geographically standardized tables |
| ACS margins of error | Small-area estimates have high uncertainty | Check MOE; aggregate areas if needed |
| Block data limitations | Only 100% count variables available (no income/poverty) | Use block groups for sample data (ACS) |
| GISJOIN vs GEOID | Different ID formats cause join failures | Use GISJOIN for NHGIS joins, GEOID for Census Bureau joins |
| 2020 Census noise | Differential privacy added noise to small-area counts | Check for negative values; prefer ACS for detailed characteristics |
| Schools vs colleges schema | Different column counts (47 vs 38 for 2020) and identifier sets | Check schema before joining; do not assume identical structures |
| Census year schema drift | Earlier census files have fewer columns (e.g., 1990 lacks CBSA/legislative fields) | Check available columns per census year before relying on them |
| geocode_accuracy type | Float64 in schools, Int64 in colleges | Cast to consistent type before cross-entity comparison |
| Using string codes | Portal data uses integer encodings, not string labels | Always verify codes against codebook (see encoding warning above) |
| Source | Relationship | When to Use |
|---|---|---|
education-data-source-ccd | School identifiers for linking | Join school data to census geography via ncessch |
education-data-source-saipe | District-level poverty | Use SAIPE for district poverty; NHGIS for tract/block group poverty |
education-data-source-meps | School-level poverty | MEPS provides school-level poverty estimates; NHGIS provides community context |
education-data-source-ipeds | College identifiers for linking | Join college data to census geography via unitid |
education-data-explorer | Parent discovery skill | Routing questions to mirror dataset files |
education-data-query | Data fetching | Downloading parquet/CSV files |
| Topic | Reference File |
|---|---|
| Census tract definition | ./references/geographic-units.md |
| Block group definition | ./references/geographic-units.md |
| School district boundaries | ./references/geographic-units.md |
| School-to-tract linking | ./references/school-geography-links.md |
| SABINS attendance areas | ./references/school-geography-links.md |
| NCES EDGE files | ./references/school-geography-links.md |
| Time series tables | ./references/time-series.md |
| Geographic standardization | ./references/time-series.md |
| Geographic crosswalks | ./references/time-series.md |
| Population variables | ./references/variable-catalog.md |
| Income/poverty variables | ./references/variable-catalog.md |
| Education variables | ./references/variable-catalog.md |
| Tract boundary changes | ./references/boundary-changes.md |
| 2022 Connecticut changes | ./references/boundary-changes.md |
| TIGER/Line versions | ./references/boundary-changes.md |
| Direct NHGIS access | ./references/data-access.md |
| ipumspy Python package | ./references/data-access.md |
| Data Finder workflow | ./references/data-access.md |
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Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
中文简介正在准备中。当前显示原文说明。
Researcher-facing analytic triangulation ("creative verification") for stress-testing provisional findings and designing pre-specified challenges before an analysis runs. Turns a researcher's concern or focal claim into competing explanations, bounded discriminating tests, audited diagnostic artifacts, and evidence-calibrated interpretations in a hypothesis/evidence ledger — behavioral, output-level verification replacing line-by-line code review. Covers 12 challenge families organized by Shadish-Cook-Campbell validity type, named sensitivity statistics, pre-committed interpretation rules, anti-fishing guardrails, and interactive diagnostic-artifact QA. Use in Ad Hoc Collaboration to verify, challenge, or stress-test a result, or to design discriminating tests before an analysis runs. Not code-change verification (verify skill / code review) and not final adjudication or re-execution (data-verifier, Reproducibility Verification) — prospective, hypothesis-driven challenge design of analytic claims.
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In-situ smoke testing that verifies a LIVE DAAF deployment functions end-to-end in whatever provider route it is configured for. Auto-detects the active install route (anthropic-subscription, openrouter, chatgpt-subscription shim, openai-api shim) and runs tiered probes: Tier 0 free preflight (route/env coherence, hooks, statuslines, shim health); Tier 1 one live round-trip (~cents); Tier 2 a six-probe functional battery (dispatch, coding, web, skill loading, isolation-strip, nested-dispatch deny); Tier D a zero-cost deterministic battery (bats, Pester, lint, safety-hook tests). Use after install, after a rebuild or update, after any provider/config change (new API key, model remap, shim change), for pre-release validation, or when asked "is my install working" or to "smoke test the deployment/config." Requires the DAAF_DEV=1 dev image; a contributor tool, not end-user-facing. NOT DAAFBench (model behavioral-adherence evaluation under benchmarks/) and NOT scripts/smoke_tests/ (R/Python library-package smoke tests).
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Operational framework for the DAAF orchestrator. Defines engagement modes, confirmation protocol, subagent dispatch, context budget, and reference-loading. Loaded exclusively by the orchestrator — not for subagents or user questions.
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Data science methodology and method-selection routing for quantitative research. Covers EDA, data validation, descriptive analysis, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial analysis, network analysis, and visualization design. Contains the canonical method-to-library routing tree, routed by execution language — Python: statsmodels (OLS/GLM/time series), pyfixest (FE/DiD), linearmodels (RE/GMM/SUR), svy (complex surveys), scikit-learn (clustering/prediction ML), geopandas (spatial), igraph (network analysis); R: r-stats (OLS/GLM/time series), fixest (FE/DiD), plm (panel/RE/IV), survey-r (complex surveys), tidymodels (ML), sf-terra (spatial), igraph-r (network analysis). Load the routed tool-specific skill before giving tool-specific advice or writing code — library skills encode environment constraints and curated caveats absent from general knowledge.
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Interpretation guidance for Urban Institute Education Data Portal datasets. The Portal is a curation layer over federal data: lowercase variable names, integer-encoded categoricals, standardized missing codes (-1 missing, -2 not applicable, -3 suppressed). Covers year definitions, grade encoding (grade=-1 is Pre-K, not missing), suppression rates, ODC-By licensing, and cross-source join identifiers. Load before analyzing any Portal data. Routes to source-specific deep-dive skills for individual datasets.
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