Use when "query WRDS", "pull SEC filings", "access Compustat/CRSP/ExecuComp/Capital IQ", "Form 4 insider data", "13F institutional ownership (Thomson)", "13D/13G blockholders", "ISS governance/compensation/voting/directors", "proxy advisor recommendations", "TAQ intraday/NBBO", "SDC M&A or new issues", "DealScan syndicated loans", "PitchBook PE/VC deals", "FISD corporate bonds", "municipal bonds / muni trades / MSRB RTRS / SDC municipals", "Form D/ADV", "fund formation", "FJC court data", "linking datasets / join keys (gvkey-permno via CCM, cik-gvkey via wciklink, DealScan-Compustat)", or any WRDS PostgreSQL query or SAS ETL on the WRDS grid (qsub/qsas/SGE).
user-invocable
false
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.
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.
Before writing ANY new EDGAR filing extractor:
Before writing or executing ANY SAS code on WRDS, you MUST validate performance patterns. This is not negotiable.
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_array.sh, and ../npx-ownership-panel/scripts/run_pipeline.sh.
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.
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
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)
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.
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
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.
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
wrds_compensation
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
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
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/edgar.md - SEC EDGAR filings, URL construction, DCN vs accession numbers
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/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/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/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 (79 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
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/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/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
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
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.
