Mirror-first discovery for Urban Institute Education Data: maps a research question to the mirror dataset FILES that answer it — canonical path, entity grain, year coverage, key variables, and join keys — for CCD, IPEDS, CRDC, College Scorecard, SAIPE, EDFacts, FSA, MEPS, NHGIS, PSEO, NACUBO, NCCS, EADA, and Campus Safety. Discovery output is the mirror path (plus grain, years, variables, caveats), the currency planning and acquisition consume. A pure readable-reference skill for read-only discovery agents — no code, no live HTTP probes; all facts keyed to pinned mirror vintage 0.26.1. Load BEFORE education-data-query — routing (which file, what grain) here, retrieval (fetch code, mirror config) there.
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Discovers which Urban Institute Education Data mirror files answer a research question and returns them as acquisition-ready routing facts: the canonical mirror path, what one row represents (grain), year coverage, curated key variables, and join keys. This is the discovery half of the education-data workflow — it owns question→file routing judgment, grain semantics, variable/code meaning, and cross-source joins. It does not own fetch mechanics: retrieval code, mirror configuration, codebook-URL construction, and the curated path catalog live in education-data-query. Load this skill first (routing), then education-data-query (retrieval). Every inventory fact here — years, variables, coded values — is keyed to mirror vintage 0.26.1 (pinned); the discovery path is pure markdown reading, with no instruction to fetch, probe, or execute anything.
DAAF's education pipeline never touches Urban's live REST API. All acquisition is mirror-based: a revision-pinned HuggingFace parquet mirror (Portal vintage 0.26.1, revision 0ad00ce0e232c96b0642459e4e7326607a8d26aa), with an unpinned urban_csv bulk-CSV fallback that uses the identical canonical paths. Acquisition consumes canonical mirror paths of the form {source}/{filename} (extensionless — the mirror layer appends .parquet or .csv). So discovery's job is to emit those paths, not URL routes. Portal endpoint routes appear here only as small citation/maintenance annotations, never as the thing you hand downstream.
The live REST API moves quarterly (endpoints rename, variables drift); a prior version of this skill hand-documented that API and went badly stale. The mirror is a frozen, reproducible snapshot — so the facts below are stable for vintage 0.26.1 and can be read as reference rather than re-probed.
Standing caveat (vintage + fallback). All year coverage, variables, and coded values below are exact for the pinned HuggingFace mirror at vintage 0.26.1. The
urban_csvfallback is unpinned/current-Portal, so if a fetch falls through to it, vintage guarantees no longer hold exactly. Cite Portal version 0.26.1 for data fetched from the pinned mirror.
| File | Purpose | When to Read |
|---|---|---|
references/schools-datasets.md | K-12 school-level mirror families (CCD, CRDC, EDFacts, MEPS, NHGIS) | Researching individual schools |
references/districts-datasets.md | District/LEA-level mirror families (CCD, SAIPE, EDFacts) | Researching school districts |
references/colleges-datasets.md | College-level mirror families (IPEDS, Scorecard, FSA, PSEO, EADA, NACUBO, NCCS, NHGIS, Campus Safety) | Researching higher education |
references/variable-dictionary-{source}.md | Complete per-source variable inventory (2,235 per-source variables, deduplicated from 2,994 endpoint-variable rows; verbatim labels/types/filter flags/coded values) | Confirming a real variable name or its coded values |
references/variable-codes.md | Curated, analytically load-bearing coded-value schemes with cross-source populated-subset notes | Interpreting or filtering categorical codes |
references/maintenance-live-api.md | Live-API catalog surfaces + regeneration mechanics — maintenance sessions only | Rebuilding the mirror/dictionaries at a new vintage |
The 14
variable-dictionary-{source}.mdfiles are mechanically generated from the Portal varlist at vintage 0.26.1. The routing files curate 5-12 key variables per family and point to the full dictionary entry for the rest. Never invent a variable name — if it is not in the dictionary, it is not in the mirror.
What entity is one row of my analysis?
├─ An individual K-12 school (ncessch) → schools level
│ └─ references/schools-datasets.md → pick a CCD/CRDC/EDFacts/MEPS/NHGIS family
├─ A school district / LEA (leaid) → school-districts level
│ └─ references/districts-datasets.md → pick a CCD/SAIPE/EDFacts family
└─ A college / university (unitid) → college-university level
└─ references/colleges-datasets.md → pick an IPEDS/Scorecard/FSA/PSEO/EADA/NACUBO/NCCS/csafety family
Research topic?
├─ K-12 enrollment / demographics → ccd/schools_ccd_enrollment_{year} (or ccd/schools_ccd_lea_enrollment_{year} at district level)
├─ School/district directory & staffing → ccd/schools_ccd_directory | ccd/school-districts_lea_directory
├─ District finance → ccd/districts_ccd_finance
├─ District poverty → saipe/districts_saipe
├─ Civil rights / discipline / course access → crdc/schools_crdc_* (see schools-datasets.md — one file per topic)
├─ K-12 assessments / grad rates → edfacts/{schools,districts}_edfacts_assessments_{year} | _grad_rates_{year}
├─ School poverty measure → meps/schools_meps
├─ College directory / characteristics → ipeds/colleges_ipeds_directory | _institutional-characteristics
├─ College enrollment → ipeds/colleges_ipeds_fall-enrollment-race_{year} | _enrollment-fte | _headcount
├─ College completions (by CIP) → ipeds/colleges_ipeds_completions-2digcip_{year} | -6digcip_{year}
├─ College graduation rates → ipeds/colleges_ipeds_grad-rates | -200pct | -pell
├─ College finance / endowment → ipeds/colleges_ipeds_finance | nacubo/colleges_nacubo_endow | nccs/colleges_nccs_all
├─ Student aid / loans → ipeds/colleges_ipeds_sfa_* | fsa/colleges_fsa_grants | _loans | _campus_based_volume
├─ Post-college earnings → scorecard/colleges_scorecard_earnings | pseo/colleges_pseo_{year}
├─ Athletics equity → eada/colleges_eada_inst_characteristics
├─ Campus crime (hate crimes) → csafety/colleges_csafety_hate_crimes
└─ Census geography crosswalk → nhgis/{schools,colleges}_nhgis_geog_{census-year}
Need a variable?
├─ Curated key variables → the family entry in the *-datasets.md routing file
├─ Every variable in a family → references/variable-dictionary-{source}.md (§ that family)
└─ What a categorical code means → references/variable-codes.md
| Level | Key Sources | Primary ID | ID Format |
|---|---|---|---|
| schools | CCD, CRDC, EDFacts, MEPS, NHGIS | ncessch | 12-char zero-padded string |
| school-districts | CCD, SAIPE, EDFacts | leaid | 7-char zero-padded string |
| college-university | IPEDS, Scorecard, FSA, PSEO, EADA, NACUBO, NCCS, NHGIS, Campus Safety | unitid | 6-digit integer |
Zero-padded IDs:
ncessch(12) andleaid(7) are zero-padded numeric strings — leading zeros are load-bearing (FIPS 01-09 states).education-data-queryhandles the force-string/pad-and-assert read recipe; discovery just needs to name the join key.
| Join across | Key | Example use |
|---|---|---|
| School sources (CCD ↔ CRDC ↔ EDFacts ↔ MEPS ↔ NHGIS) | ncessch (+ year) | Enrollment + discipline + poverty |
| District sources (CCD ↔ SAIPE ↔ EDFacts) | leaid (+ year) | Directory + poverty + outcomes |
| College sources (IPEDS ↔ Scorecard ↔ FSA ↔ PSEO ↔ EADA ↔ NACUBO ↔ NCCS ↔ csafety) | unitid (+ year) | Characteristics + earnings + aid |
| School → district rollup | leaid present on school files | Aggregate schools to their LEA |
| Any level → census geography | ncessch/unitid via NHGIS crosswalk | Link to tract/CBSA/region |
Match on year when joining — coverage windows differ across families (see below), so align years before merging.
Verified ranges from the endpoint-file bridge and route reconciliation. Non-contiguous families are flagged; see the routing files for exact year lists.
| Family (representative) | Coverage | Note |
|---|---|---|
| CCD directory / enrollment | 1986-2024 | Annual |
| CCD district finance | 1991, 1994-2020 | Non-contiguous early years |
| CRDC (most topics) | 2011, 2013, 2015, 2017, 2020, 2021 | Biennial; chronic-absenteeism reaches 2022 |
| EDFacts assessments | 2009-2018, 2020 | 2019 absent (COVID testing waivers) |
| EDFacts grad rates | 2010-2019 | — |
| IPEDS directory / inst-characteristics | 1980, 1984-2024 | — |
| IPEDS finance | 1979, 1983-2017 | Mirror ends 2017 (API reaches further back, differently packaged) |
| IPEDS grad-rates (150%) | 1996-2023 | Wholesale-revalued in v0.26.1 — re-run any pre-2026q3 analysis |
| IPEDS completions (2-/6-dig CIP) | 1991-2023 / 1983-2023 | Yearly shards |
| Scorecard earnings | 2003-2014, 2018 | Sparse; other Scorecard families 1996-2020 |
| SAIPE | 1995, 1997, 1999-2024 | Poverty pct on a 0-1 scale despite _pct |
| FSA | 1999-2021 | 90/10 from 2014; campus-based from 2001 |
| MEPS | 2009-2022 | 2023 empty on 2026-08-06 probe |
| NHGIS (schools / colleges) | 1986-2023 / 1980-2023 | Crosswalk file-years; census vintages 1990-2020 |
| PSEO | 2001-2021 | Yearly shards |
| NACUBO / NCCS / EADA / Campus Safety | 2012-2022 / 1993-2016 / 2002-2021 / 2005-2021 | — |
A mirror file's grain is the set of columns that uniquely identify a row. Reading it correctly is the difference between a clean join and a silent fan-out.
year (e.g., one row per ncessch × year in CCD directory)..../enrollment/{year}/{grade}/race/sex/); in the mirror file the same dimensions are columns (grade, race, sex) and each combination is its own row. So CCD enrollment is one row per ncessch × year × grade × race × sex. To get a school total, filter to the 99/total code on each dimension rather than summing (summing double-counts the totals rows).disability and lep dimensions in addition to race/sex; a discipline file is one row per school × year × the disaggregation the file name advertises.{year} in the path (fetch each year, concatenate). "Single" families pack all years into one file (filter on year locally). The routing files mark each family Single or Yearly.99 = Total and negative sentinels for missing (-1/-2/-3/-99) — see variable-codes.md. A "total" row and its component rows coexist in the same file.Discovery output feeds planning, then acquisition. For each dataset a research question needs, emit:
{source}/{filename}, extensionless, with {year} placeholder where the family is Yearly. This is the primary currency.Portal endpoint routes are optional citation annotations only — never the hand-off artifact. Do not instruct downstream to probe, fetch, or call an API; naming the path and years is sufficient for education-data-query to construct the fetch.
| Skill | Purpose | When |
|---|---|---|
education-data-query | Fetch code, mirror config, codebook URLs, path catalog | After identifying paths/years/variables here |
education-data-context | Portal-wide interpretation (missing codes, grade encoding, licensing, joins) | Before analyzing retrieved data |
education-data-source-{ccd,ipeds,crdc,scorecard,saipe,edfacts,fsa,meps,nhgis,nccs,nacubo,eada,campus-safety,pseo} | Deep source context: methodology, limitations, historical changes | When you need collection methodology or source-specific caveats beyond routing |
Load a education-data-source-* skill when a question needs collection methodology, known limitations, or historical coding changes for a specific source. Each source skill carries a skill-last-updated provenance date — flag stale ones to the orchestrator.
Question: "Relationship between school poverty and AP course offerings in California high schools?"
ncessch × year.crdc/schools_crdc_apib_enroll (Single; 2011-2021) — AP/IB enrollment. Key vars: enrl_ap, enrl_ap_math, enrl_ap_science, enrl_ib, enrl_gifted_talented. Grain adds race/sex/disability/lep columns — filter to totals for a school-level measure.meps/schools_meps (Single; 2009-2022) — modeled poverty. Key var: meps_poverty_pct.ncessch + year; overlap window 2011-2021.fips == 6 (California), high schools via CCD school_level == 3 (join ccd/schools_ccd_directory).ncessch join key, and the CRDC disaggregation-totals caveat. Then load education-data-query to fetch.crdc/schools_crdc_discipline_k12_{year}, not /schools/crdc/discipline/{year}/....99) coexist with component rows — filter, don't sum.variable-dictionary-{source}.md, it is not in the mirror.year across families before joining; several have gaps (EDFacts 2019, IPEDS finance ends 2017).urban_csv fallback is current-Portal.还没有评价。欢迎分享你的使用感受。
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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.
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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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