13f analyzer

Skill cxcscmu/SkillLearnBench/skills/b4-skill-creator-claude-opus-4-6/financial-analysis/13f-analyzer

[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.

Install

npx -y skills add cxcscmu/SkillLearnBench --skill 13f-analyzer

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What its author says it does

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Perform data analysis on SEC 13-F filing datasets (TSV format) to obtain insights about fund activities such as number of holdings, AUM, and change of holdings between two quarters. Use this skill whenever analyzing hedge fund portfolios, comparing quarterly holdings, or working with SEC EDGAR 13F data files.

SKILL.md

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13F Filing Analyzer

Dataset Structure

SEC 13F data comes as TSV files in quarterly folders (e.g., /root/2025-q2/, /root/2025-q3/):

File	Purpose	Key Columns
`COVERPAGE.tsv`	Filer metadata	`ACCESSION_NUMBER`, `FILINGMANAGER_NAME`, `REPORTCALENDARORQUARTER`
`INFOTABLE.tsv`	Individual holdings	`ACCESSION_NUMBER`, `NAMEOFISSUER`, `CUSIP`, `VALUE` (in thousands USD), `SSHPRNAMT` (shares)
`SUMMARYPAGE.tsv`	Filing summary	`ACCESSION_NUMBER`, `TABLEENTRYTOTAL` (count of holdings), `TABLEVALUETOTAL` (AUM in thousands)

Common Analysis Patterns

Get Fund's Accession Number

Search COVERPAGE.tsv by FILINGMANAGER_NAME to get the fund's ACCESSION_NUMBER for a given quarter.

Get AUM

Use the accession number to look up TABLEVALUETOTAL in SUMMARYPAGE.tsv. The value is in thousands of USD.

Get Number of Holdings

Use TABLEENTRYTOTAL from SUMMARYPAGE.tsv.

Compare Holdings Between Quarters

Get accession numbers for both quarters from COVERPAGE
Filter INFOTABLE by each accession number
Join on CUSIP and compute differences in VALUE or SSHPRNAMT
Rank by dollar value change (VALUE column, in thousands)

Find Top Holders of a Stock

Find the stock's CUSIP from INFOTABLE (search by NAMEOFISSUER)
Filter all INFOTABLE rows matching that CUSIP
Join with COVERPAGE on ACCESSION_NUMBER to get fund names
Rank by VALUE (in thousands)

Implementation Notes

Use Python with pandas for efficient TSV processing
TSV files use tab separator, read with pd.read_csv(path, sep='\t')
INFOTABLE is large (~3M rows); filter early to reduce memory usage
VALUE column is in thousands of USD
ACCESSION_NUMBER is the join key across all tables
When comparing quarters, some funds may have multiple filings (amendments); prefer non-amendment filings (ISAMENDMENT is empty or 'N')