WRDS Data Access Skill | Agent Skills

What this skill carries — grep references/ for any subject the names below miss: !d=${CLAUDE_SKILL_DIR}; command -v skill-toc >/dev/null 2>&1 && exec skill-toc "$d"; s=$HOME/.claude/skills/plugin-utils/bin/skill-toc; [ -x "$s" ] && exec "$s" "$d"; echo "(skill-toc unavailable: references and scripts are NOT listed here — install the plugin-utils plugin, or start a new session so its bin/ reaches PATH)"

Building a proxy-voting panel? Use the npx-ownership-panel skill, not this one. It owns risk.voteanalysis_npx (238M rows / 329 GB), the ISS->CRSP fund crosswalk, and the four-leg SGE pipeline that produces the analysis-ready panel. This skill covers WRDS access patterns generally.

WRDS Login Node Enforcement
Query Enforcement
SAS ETL Enforcement
Quick Reference: Table Names
Connection
Critical Filters
Parameterized Queries
Additional Resources

WRDS Login Node Enforcement

IRON LAW: NEVER RUN COMPUTE ON THE WRDS LOGIN NODE

<EXTREMELY-IMPORTANT> The WRDS login node is shared infrastructure. Running parsers, bulk file reads, SAS jobs, or any process taking >30 seconds on the login node will get the account flagged.

ALWAYS write an SGE submission script and submit via qsub. No exceptions.

ssh wrds 'cat files.tsv | ./parser > output.tsv' → WRONG. Use qsub.
ssh wrds 'nohup ./process &' → WRONG. Still the login node. Use qsub.
ssh wrds 'python3 bulk_process.py' → WRONG. Use qsub.
qsub -t 1-20 submit.sh → CORRECT.

The login node is for: qsub, qstat, qdel, scp, ls, head, short psql queries.

Submission patterns and working array jobs: references/edgar.md (§ SGE index build), scripts/sec_index/submit_array.sh, scripts/parse_13f/sge/submit_shards.sh, and ../npx-ownership-panel/scripts/run_pipeline.sh. </EXTREMELY-IMPORTANT>

Running compute on the login node is NOT HELPFUL — it gets the user's account flagged, the job killed, and the work lost. You run on the login node because qsub feels like overhead. The overhead is 5 minutes of script writing. The downside is account suspension and a rerun from scratch.

Login Node & Infrastructure Facts

Tests go through the scheduler too: qsub -t 1-1 submit.sh. The login-node "quick test" is the run that flags the account — one file becomes 100K when the command changes, and 173K filings over NFS is not 30 seconds.
The quorum parser does not run on the login node and never did — it runs via submit_quorum.sh. Citing it as login-node precedent is an unverified claim presented as fact.
The wrds_clean_filings path convention is cik_int.zfill(10)[:6]/{cik_int}/{accession}.txt (see references/edgar.md). Hand-rolled path logic gets this wrong.
scan_covers profiles handle header extraction, body parsing, and custom extractors (Custom field type) — "this parser is different enough to need its own binary" has not yet been true once. For SGML header fields (state of incorporation, business address, SIC, FYE, former name, every role block), query the pre-parsed wrdssec_all.wrds_forms_reg first (see references/edgar.md); scan only for what it lacks.
A pixi/conda env under /scratch is not durable. A grid job that ran fine in August 2026 came back rc=127 weeks later: the env its submit script hard-coded had been swept, and no interpreter on WRDS had polars any more. Either rebuild the env as a step of the job, or keep the heavy pull on the grid and do the dataframe work locally. Do not hard-code an env path and assume it survives.

Red Flags — STOP Immediately If You're About To:

Write ssh wrds '... | ./binary > output' → STOP. That's login-node compute. Write a submit script.
Write ssh wrds 'nohup ... &' → STOP. nohup doesn't change the node. Use qsub.
Write ssh wrds 'python3 ...' for anything that reads >10 files → STOP. Use qsub.
Skip reading references/edgar.md before building a new WRDS file parser → STOP. The path conventions, SGE patterns, and existing parsers are already documented. Read them first.
Create a new standalone Go binary for EDGAR extraction → STOP. scripts/scan_covers/ is a generic profile-based framework. Add a profiles_*.go file, not a new binary. The framework handles SGE sharding, path construction, concurrency, and form-type filtering.
Build a new Go/Python parser without checking scripts/scan_covers/ → STOP. This framework exists precisely so you don't reinvent extraction infrastructure. Every standalone parser is technical debt that should have been a profile.

IRON LAW: USE SCAN_COVERS, NOT STANDALONE BINARIES

<EXTREMELY-IMPORTANT> Before writing ANY new EDGAR filing extractor:

Read scripts/scan_covers/ — generic profile-based Go framework with SGE, concurrency, path handling
Add a profiles_*.go file — not a standalone binary. The Profile struct supports pattern-based fields AND custom extractors (set FullBody: true for body-text searches like prospectus 485 filings — see profiles_proxy_advisors.go)
Read references/edgar.md — path conventions, existing profiles, SGE submission patterns

Building a standalone parser when scan_covers exists is NOT HELPFUL — it reinvents infrastructure that already handles SGE sharding, NFS concurrency, path construction, form-type filtering, and error handling. You built a 300-line standalone Go binary, ran it on the login node, got the path convention wrong, and spent 5 iterations fixing it. Adding a 60-line profile to scan_covers would have worked on the first try.

Every standalone EDGAR parser is technical debt. The scan_covers framework exists to eliminate this class of mistake.

Exactly two sanctioned exceptions: scripts/parse_13f/ and scripts/parse_npx/. scan_covers FullBody reads the whole file into one buffer per worker and its Field model reduces a regex to one value per column, so a record table — the 13F infoTable, the N-PX proxyTable whose nested voteRecords run to tens of thousands in a single filing — is neither; those two stream the record table instead, emitting one row per record: parse_13f with a hand-rolled information-table scanner, parse_npx with xml.Decoder. Cover-page or header extraction is still a profile, with no exception. Their TSV-to-parquet output contract is scripts/edgar_parquet/ (see references/edgar.md). </EXTREMELY-IMPORTANT>

WRDS Data Access

WRDS (Wharton Research Data Services) provides academic research data via PostgreSQL at wrds-pgdata.wharton.upenn.edu:9737.

Query Enforcement

IRON LAW: NO QUERY WITHOUT FILTER VALIDATION FIRST

Before executing ANY WRDS query, you MUST:

IDENTIFY what filters are required for this dataset
VALIDATE the query includes those filters
VERIFY parameterized queries (never string formatting)
EXECUTE the query
INSPECT a sample of results before claiming success

This is not negotiable. Skipping sample inspection is NOT HELPFUL — the user builds analysis on data with undetected quality problems.

Red Flags

Running a query without checking the Critical Filters section → standard filters apply even when the user doesn't mention them, and even for test queries.
Pulling everything to filter in pandas later → filter at the database level first.
Guessing a table name from the request → check the Quick Reference section for exact names.
Claiming success before sample inspection → inspect .head()/.sample() first; query success ≠ data quality.

Query Validation Checklist

Before EVERY query execution:

For Compustat queries (comp.funda, comp.fundq):

[ ] Includes indfmt = 'INDL'
[ ] Includes datafmt = 'STD'
[ ] Includes popsrc = 'D'
[ ] Includes consol = 'C'
[ ] Uses parameterized queries for variables
[ ] Date range is explicitly specified

For CRSP v2 queries (crsp.dsf_v2, crsp.msf_v2):

[ ] Post-query filter: sharetype == 'NS'
[ ] Post-query filter: securitytype == 'EQTY'
[ ] Post-query filter: securitysubtype == 'COM'
[ ] Post-query filter: usincflg == 'Y'
[ ] Post-query filter: issuertype.isin(['ACOR', 'CORP'])
[ ] Uses parameterized queries

For Form 4 queries (tr_insiders.table1):

[ ] Transaction type filter specified (acqdisp)
[ ] Transaction codes specified (trancode)
[ ] Date range is explicitly specified
[ ] Uses parameterized queries

For ALL queries:

[ ] Sample inspection with .head() or .sample() BEFORE claiming success
[ ] Row count verification (is result size reasonable?)
[ ] NULL value check on critical columns
[ ] Date range validation (does min/max match expectations?)

SAS ETL Enforcement

IRON LAW: NO SAS CODE WITHOUT PERFORMANCE VALIDATION FIRST

<EXTREMELY-IMPORTANT> Before writing or executing ANY SAS code on WRDS, you MUST validate performance patterns. This is not negotiable.

MERGE STRATEGY — Is hash or sort-merge appropriate? Justify the choice.
WHERE CLAUSES — Are all date/string filters index-friendly? No functions on indexed columns.
PARALLELISM — Can this job run as an SGE array? Year-by-year is always parallelizable.
SQL OPTIMIZATION — For PROC SQL: pass-through opportunity? Indexed join columns?

Writing SAS code that forces full table scans when indexes exist is NOT HELPFUL — the user's job runs 100x slower than necessary and may timeout. </EXTREMELY-IMPORTANT>

SAS Code Validation Checklist

Before EVERY SAS program execution:

For probing inputs (do this FIRST — metadata only, seconds):

[ ] PROC CONTENTS data=lib.x varnum on every input — variables, types, lengths, formats
[ ] Index section of the CONTENTS listing read — does the WHERE column actually have an index?
[ ] Key lengths compared across datasets to be merged (mismatched $6/$8 gvkey = silent zero matches)
[ ] PROC SQL; select memname, nobs from dictionary.tables where libname='LIB'; — row counts before committing to the job
[ ] PROC PRINT data=lib.x(obs=20); var ...; — values look like the docs claim (always obs=, always var)
[ ] PROC DATASETS library=scratch; — inventory intermediates; delete there, not via a rewriting DATA step

For merges/joins:

[ ] Small lookup + large fact table → hash object (not PROC SORT + DATA merge)
[ ] Hash uses defineKey/defineData/defineDone pattern correctly
[ ] h.output() uses double quotes for macro resolution (not single quotes)
[ ] call missing() initializes hash data variables for non-matches
[ ] Both tables >50M rows → sort-merge is justified (document why)

For WHERE clauses (CRITICAL):

[ ] NO year(date), month(date), datepart(dt) wrapping indexed columns
[ ] Date filters use BETWEEN "01jan&year."d AND "31dec&year."d range pattern
[ ] String filters avoid upcase(), substr() on indexed columns
[ ] Compound date filters collapsed to single range (not year() = X AND quarter() = Y)

For batch processing:

[ ] Multi-year jobs use SGE array (#$ -t start-end) not sequential loop
[ ] Year passed via -sysparm (not -set or %sysget)
[ ] Per-year log files (not single shared log)
[ ] Memory allocation appropriate for workload (#$ -l m_mem_free=4G minimum)
[ ] Single-year benchmark run completed before full array submission

For PROC SQL:

[ ] Join columns are not wrapped in functions
[ ] calculated keyword used for computed column references in HAVING
[ ] Pass-through SQL considered for direct WRDS PostgreSQL queries
[ ] No redundant subqueries that could be hash lookups

For macros:

[ ] Macro variables terminated with period (&year. not &year)
[ ] Double quotes used where macro resolution is needed
[ ] options mprint mlogic symbolgen used during development

SAS Performance Facts

Hash lookup joins are ~10x faster than PROC SORT + MERGE and need no sorting; PROC SQL still sorts for joins. The hash is 5 extra lines — choosing sort-merge for a lookup join makes the user's job slower for your convenience.
year(date) (or any function) on an indexed column forces a full table scan over millions of rows; BETWEEN with date literals uses the index.
Sequential multi-year jobs run ~18x slower than the SGE array (18 years × 3 minutes = 54 minutes sequential vs 3 minutes parallel) — "I'll parallelize later" is anti-efficient on its own terms.
Single quotes in h.output(dataset: '...') block macro resolution — the output dataset name comes out wrong. Always double quotes.
%sysget is unreliable under SGE — it may return blank silently. Pass the year via -sysparm + &sysparm..

SAS Red Flags - STOP Immediately If You're About To:

Write where year(date) = anything → STOP. Use BETWEEN with date literals.
Write proc sort; data; merge for a lookup join → STOP. Use hash object.
Write a %do year = start %to end loop → STOP. Use SGE array job.
Use single quotes in h.output(dataset: '...') → STOP. Use double quotes.
Submit a full array job without testing one year first → STOP. Benchmark first.
Use -set or %sysget for SGE task parameters → STOP. Use -sysparm.

SAS Reference

See references/sas-etl.md for complete patterns:

Probing data and metadata (PROC CONTENTS, PROC DATASETS, PROC PRINT, dictionary.tables)
Hash object merge (basic, multidata, accumulator)
Index-friendly WHERE clause quick reference table
SGE array job templates with memory and logging
PROC SQL pass-through and optimization
Macro quoting and debugging

Quick Reference: Table Names

| Dataset | Schema | Key Tables | |---------|--------|------------| | Compustat | comp | company, funda, fundq, secd | | ExecuComp | comp_execucomp | anncomp | | CRSP | crsp | dsf, msf, stocknames, ccmxpf_lnkhist | | CRSP v2 | crsp | dsf_v2, msf_v2, stocknames_v2 | | Form 4 Insiders | tr_insiders | table1, header, company | | ISS Incentive Lab | iss_incentive_lab | comppeer, sumcomp, participantfy | | Capital IQ | ciq (views), ciq_pplintel, ciq_common | wrds_professional (board/professional panel), ciqcompanyrel (company-to-company), wrds_compensation. Account-split: ciq_pplintel and boardex_na are on opposite WRDS accounts; ciq_transactions is denied on both. See references/capiq.md | | BoardEx | boardex_na (edwin_hu only) | na_wrds_org_composition (directors + senior managers — filter seniority), na_wrds_company_names, na_dir_profile_details (usualname = nickname). History starts 1999, ~20k mostly-large companies. See references/boardex.md | | WRDS People Link | wrdsapps_plink_exec_ciq, _exec_boardex, _exec_trinsider, _trinsider_ciq | pairwise PERSON id links (execid ↔ directorid ↔ CIQ personid ↔ TR personid). plink_boardex_ciq denied unless the account holds both. See references/people-linking.md | | Audit Analytics activism | audit_corp_legal (edwin_hu only) | feed31_shareholder_activism — one row per SC 13D/13D-A: subject CIK, filer, accession, stake, purpose keys. Query it before parsing 13D covers. No proxy-fight/settlement fields. See references/audit-analytics.md | | IBES | tr_ibes | det_epsus, statsum_epsus | | Form D / Reg D | wrdssec | wrds_vc_formd (parsed, 2000–2020); index: wrdssec_all.forms (all CIKs) or wrds_forms (filer only) — default to forms, see references/wrds-forms-tables.md | | SEC EDGAR | wrdssec_all | forms (raw index, all CIKs per filing — default), wrds_forms (filer-only view), wciklink_cusip | | SEC Search | wrds_sec_search | filing_<form> (full text + indexed filing_tsv), registrant — server-side full-text search; never select filing in bulk. See references/sec-text-search.md | | EDGAR | edgar | filings, filing_docs | | Fama-French | ff | factors_monthly, factors_daily | | LSEG/Datastream | tr_ds | ds2constmth, ds2indexlist | | FJC (Federal Judicial Center) | fjc | civil, criminal, bankruptcy, appeals | | FJC Linking | fjc_linking | wrds_civil_link, wrds_criminal_link | | SDC New Issues (IPO/SEO/Debt) | tr_sdc_ni | wrds_ni_details — equity + debt offerings | | SDC Mergers & Acquisitions | tr_sdc_ma | wrds_ma_details — M&A transactions | | TAQ Legacy | taq | mast_YYYY, wrds_iid_YYYY — second-level (1993–2006) | | TAQ Millisecond | taqmsec | mastm_YYYY, wrds_iid_YYYY, ctm_YYYYMM, complete_nbbo_YYYYMMDD | | Thomson S12 (Mutual Fund Holdings) | tfn (SAS) / tr_mutualfunds (PG) | s12 — 13F/N-CSR fund holdings | | Thomson S34 (13-F Institutional) | tfn (SAS) / tr_13f (PG) | s34 — 13-F institutional holdings | | FISD / Mergent (Corporate Bonds) | fisd_fisd | fisd_mergedissue, fisd_mergedissuer — corporate/agency/Treasury; NOT the muni source (issuer_type='M' munis are incidental) | | Municipal trades (MSRB RTRS) | msrb | msrb (trades + inline CUSIP master: coupon, maturity), msrb_lookup; also msrb_all, msrbsamp. Primary muni source. See references/muni-bonds.md | | Municipal new issues (SDC) | tr_sdc_municipals | deal-level: ratings, GO/rev, bank-qualified, callable, size, sector — but SELECT is permission-denied on this subscription (not licensed); msrb is the only readable muni schema. See references/muni-bonds.md | | PitchBook | pitchbk_companies_deals, pitchbk_investors_funds_lps, pitchbk_fund_returns | deal, company, fund, wrds_fund_returns — dealsize in USD millions |

Connection

Initialize PostgreSQL connection to WRDS:

import psycopg2

conn = psycopg2.connect(
    host='wrds-pgdata.wharton.upenn.edu',
    port=9737,
    database='wrds',
    sslmode='require'
    # Credentials from ~/.pgpass
)

Configure authentication via ~/.pgpass with chmod 600:

wrds-pgdata.wharton.upenn.edu:9737:wrds:USERNAME:PASSWORD

Connect via SSH tunnel:

ssh wrds

This uses ~/.ssh/wrds_rsa for authentication.

Critical Filters

Compustat Standard Filters

Always include for clean fundamental data:

WHERE indfmt = 'INDL'
  AND datafmt = 'STD'
  AND popsrc = 'D'
  AND consol = 'C'

CRSP v2 Common Stock Filter

Equivalent to legacy shrcd IN (10, 11):

df = df.loc[
    (df.sharetype == 'NS') &
    (df.securitytype == 'EQTY') &
    (df.securitysubtype == 'COM') &
    (df.usincflg == 'Y') &
    (df.issuertype.isin(['ACOR', 'CORP']))
]

Form 4 Transaction Types

WHERE acqdisp = 'D'  -- Dispositions
  AND trancode IN ('S', 'D', 'G', 'F')  -- Sales, Dispositions, Gifts, Tax

Parameterized Queries

Always use parameterized queries (never string formatting):

Use scalar parameter binding for single values:

cursor.execute("""
    SELECT gvkey, conm FROM comp.company WHERE gvkey = %s
""", (gvkey,))

Use ANY() for list parameters:

cursor.execute("""
    SELECT * FROM comp.funda WHERE gvkey = ANY(%s)
""", (gvkey_list,))

Additional Resources

Reference Files

Detailed query patterns and table documentation:

references/compustat.md - Compustat tables, ExecuComp, financial variables
references/crsp.md - CRSP legacy (SIZ) stock data and CCM linking
${CLAUDE_SKILL_DIR}/../../skills/crsp-v2/SKILL.md - CRSP CIZ / v2 format (required for any data after 2024-12-31)
references/insider-form4.md - Thomson Reuters Form 4, rolecodes, insider types
references/iss-compensation.md - ISS Incentive Lab, peer companies, compensation
references/formd.md - Form D / Reg D (canonical): two sources (WRDS wrds_vc_formd + SEC EDGAR TSV/XML), grain & keys, denormalization gotcha, exemption + industry codes, post-2020 gap, validated benchmarks
references/boardex.md - BoardEx: 1999 coverage start and ~20k-company universe, wrds_org_composition is directors PLUS senior managers, sentinel dates, feed 4.2 succession ids, the reused-ticker linking trap, measured recall vs proxy statements
references/people-linking.md - WRDS People Link: the pairwise person-id tables, which are readable per account, and the Execucomp chain when BoardEx↔CIQ is denied
references/edgar.md - SEC EDGAR filings, URL construction, DCN vs accession numbers
references/connection.md - Connection pooling, caching, error handling
references/taq.md - TAQ: master files, IID, raw tick processing (NBBO, VWAP, closing auctions), CRSP–TAQ merge, era transition (legacy vs millisecond)
references/sas-etl.md - SAS metadata probing (PROC CONTENTS/DATASETS/PRINT), hash objects, index-friendly WHERE, SGE array jobs, PROC SQL optimization
references/postgres-vs-sas.md - Decision guide: when to use PostgreSQL vs SAS for WRDS ETL (benchmarks, constraints, hybrid pattern)
references/fjc.md - FJC Integrated Database: civil/criminal case data, NOS codes, securities litigation queries, firm linking
references/sdc-issuances.md - SDC New Issues: IPOs, SEOs, 144A equity, debt offerings — schema discovery, cleaning filters, CRSP/Compustat linking
references/fisd-bonds.md - FISD/Mergent: corporate bond issuances, IG vs HY, 144A vs registered, rating classification, TRACE linking
references/sdc-ma.md - SDC M&A: deal counts, PE/LBO vs strategic buyer, deal status codes, public vs private target
references/fund-formation.md - Fund formation: Form D (pooled investment funds), EDGAR N-2 (closed-end fund IPOs), Form ADV (RIA registrations)
references/capiq.md - Capital IQ: the eddyhu/edwin_hu account split (ciq_pplintel vs boardex_na, both plink tables denied), wrds_professional board panel + the boardflag/sponsorflag traps, ciqcompanyrel relationship types, the keyed sponsor→portco→director→sponsor-employment join
references/pitchbook.md - PitchBook: schema architecture, dealsize/fundsize in USD millions, dealdate outliers, CIK crosswalk, fund performance (wrds_fund_returns), PE/VC/fund formation patterns
references/proxy-advisors.md - Proxy-advisor customer identification: 485BPOS/485APOS body scan for ISS/Glass Lewis/Egan-Jones name variants; CRSP MFDB lift to mgmt_cd × year; validates against chongshu published CSV
references/linkage.md - Cross-dataset linkage map: which identifiers are spines, the load-bearing link tables (CCM, wciklink, dswslink, MFDB), a "how do I join X to Y" table, and which vendor ids never cross
references/audit-analytics.md - Audit Analytics feed31 shareholder activism: filing-level 13D/13D-A with subject CIK, filer, accession and purpose keys (edwin_hu only); campaign construction, what it lacks vs SharkRepellent, CUSIP link caveat
references/blockholders.md - 13D/13G blockholder panel: Volkova replication, position %, the four mutually-exclusive holder flags
references/execucomp.md - ExecuComp: CEO anncomp, legacy codirfin vs current directorcomp, firm-year aggregation
references/iss-directors.md - ISS Directors: risk.directors + risk.rmdirectors, type harmonization, 1996 gender backfill, S&P 1500 filter
references/iss-voting.md - ISS Voting Analytics: vavoteresults, voteanalysis_npx, base-conditional turnout/forpct, agenda codes
references/tfn-ownership.md - Thomson 13-F (S34) institutional ownership and S12 mutual-fund holdings via MFLINKS, passive/index classification, and Known Data Defects (D1-D9: split mis-adjustment, post-2013 coverage collapse, 2017Q4 S12 feed change, 13F value unit break, and two that are yours not the vendor's — D8 silent Int8 date overflow, D9 ownership above 100%). Read the defects section before trusting any split-era or post-2013 quarter.
- Detectors: scripts/ownership_dq.py (14 detectors, S12 and S34) — run these against any holdings panel before analysis. Tests: tests/ownership_dq_test.py (105 assertions, stdlib only).
- Run detect_calendar_bucket_gap on every reference/dimension table at build time, not just on the output panel. It is the one detector that catches a root cause rather than a symptom: a reference table missing a whole calendar bucket makes every downstream join fall back to a default, silently, and the result looks like a vendor defect (see D8).
references/lpc-dealscan.md - LPC DealScan: legacy vs 2021+ flat schema, borrower ids, the gvkey link and its grain caveats
references/muni-bonds.md - Municipal bonds: MSRB RTRS trades, SDC municipals
references/wrds-forms-tables.md - wrdssec_all.wrds_forms and friends: filing metadata tables and their columns
references/sec-text-search.md - wrds_sec_search: phrase/prefix full-text search over filing text in SQL (tsvector + ts_headline snippets, registrant join), plus the bow_YYYY word-count tables

Example Files

Working code from real projects:

examples/form4_disposals.py - Insider trading analysis (from SVB project)
examples/wrds_connector.py - Connection pooling pattern
examples/formd_regd.ipynb - Form D / Reg D: dedup validation, SEC TSV download, exemption trend charts
examples/sdc_issuances_eda.ipynb - SDC New Issues: annual IPO/SEO/debt counts, 144A share, IG vs HY breakdown
examples/sdc_ma_eda.ipynb - SDC M&A: annual deal counts, PE/LBO vs strategic, public vs private target trends
examples/fund_formation_eda.ipynb - Fund formation: Form D 3C.1/3C.7 counts, EDGAR N-2 closed-end fund IPOs, Form ADV RIA registrations
examples/pitchbook_eda.ipynb - PitchBook: PE deal activity, VC rounds by stage, fund formation by vintage, IRR/TVPI by strategy
npx-ownership-panel SKILL (promoted out of this skill's examples) - the full meeting-level proxy-voting x ownership panel: ISS N-PX fund votes reduced to (item x block) cells on the grid, joined to 13-F institutional and MF holdings. One bash command, verified end to end on 2026-07-25. Also carries the ISS->CRSP fund crosswalk. Use it for any N-PX or fund-level voting work.
examples/blockholders_pipeline/ - 13D/13G → Volkova blockholder panel, end-to-end Python. redo_bridge.py is the reference implementation of TR personid → SEC rptOwnerCik name bridging (97.4% hit rate).
examples/form4_pipeline/ - Two parallel Form 3/4/5 pipelines: the annualized SAS ownership panel and the XML owner bridge built from the raw filings.
examples/proxy_advisors_pipeline/ - 485BPOS/485APOS scan for ISS / Glass Lewis / Egan-Jones customer relationships via the scan_covers Go framework + SGE.
examples/fjc_eda.ipynb - FJC Integrated Database: securities cases (nos = 850), filing trends, court distribution
examples/lpc_dealscan_eda.ipynb (paired script: examples/lpc_dealscan_eda.py) - LPC DealScan: ~171K US facilities 1990-2020 (the normalized facility table; queries are capped at 2020-12-31), volume by year, loan type and purpose mix
examples/voting_ownership_eda.py - Standalone Python/PostgreSQL EDA of the same ISS-votes + ownership merge. For production work use the npx-ownership-panel skill, which is the SGE-ready, verified-end-to-end version of this analysis.

Scripts

scripts/test_connection.py - Validate WRDS connectivity
scripts/inventory_schemas.py - Inventory every accessible WRDS PostgreSQL schema, its tables, and row counts — run this before guessing at a table name
scripts/scan_covers/ - Generic profile-based Go framework for EDGAR extraction (SGE sharding, NFS concurrency, path construction, form-type filtering). Add a profiles_*.go, never a new standalone binary — see the Iron Law above.
scripts/parse_13f/, scripts/scan_headers/, scripts/sec_index/ - Companion EDGAR tooling: 13F table parsing, SEC header scanning, index building
scripts/dual_class/ - Dual-class common stock from 10-K text (WRDS clean filings or EDGAR): deterministic passage extractor + Gemini-on-Vertex classifier (Flex ≤10, gated Batch), plus --backend jev|gemini|hybrid; calibrated on 270 hand labels + 180 second-labeller filings (hybrid precision 0.992 / recall 1.000) — see references/dual-class.md

Local Sample Notebooks

WRDS-provided samples at ~/resources/wrds-code-samples/:

ResearchApps/CCM2025.ipynb - Modern CRSP-Compustat merge
ResearchApps/ff3_crspCIZ.ipynb - Fama-French factor construction
comp/sas/execcomp_ceo_screen.sas - ExecuComp patterns

Date Awareness

When querying historical data, leverage current date context for dynamic range calculations.

Current date is automatically available via datetime.now(). Apply this to:

Data range validation (e.g., "get data for last 5 years")
Fiscal year calculations
Event study windows

Implement dynamic date ranges in queries:

from datetime import datetime, timedelta

# Query last 5 years of data
end_date = datetime.now()
start_date = end_date - timedelta(days=5*365)

query = """
SELECT * FROM comp.funda
WHERE datadate BETWEEN %s AND %s
"""
df = pd.read_sql(query, conn, params=(start_date, end_date))

Always incorporate current date awareness in date-dependent queries to ensure results remain fresh across time.

Agent Skills: WRDS Data Access

Install this agent skill to your local

Skill Files

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