Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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.
