Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cxcscmu/SkillLearnBench --skill sec13f-data-formatgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cxcscmu/skilllearnbench/sec13f-data-format)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/sec13f-data-format"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/sec13f-data-format/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/sec13f-data-format"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/sec13f-data-format.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.01281 |
| Opus 5.5 | $0.00010 | $0.00512 |
| Sonnet 5.5 | $0.00005 | $0.00256 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
sec13f-data-format scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEC 13-F Data Format and Structure
Overview
SEC 13-F filings provide quarterly snapshots of large institutional investment holdings. The data is distributed as tab-separated value (TSV) files organized by reporting quarters.
Key Data Files
COVERPAGE.tsv
Purpose: Fund/Manager information and filing metadata Key Columns:
ACCESSION_NUMBER: Unique identifier for the filing (use to link with holdings)FILINGMANAGER_NAME: Name of the fund or investment managerREPORTCALENDARORQUARTER: Reporting date (e.g., "30-JUN-2025")DATEREPORTED: When the filing was reportedREPORTTYPE: Type of 13-F report
Usage Example:
import pandas as pd
# Load fund information
coverpage = pd.read_csv('/root/2025-q3/COVERPAGE.tsv', sep='\t')
# Find a specific fund by name
fund_info = coverpage[coverpage['FILINGMANAGER_NAME'].str.contains('Renaissance', case=False, na=False)]
accession_number = fund_info['ACCESSION_NUMBER'].values[0]
INFOTABLE.tsv
Purpose: Detailed holdings data for all funds Key Columns:
ACCESSION_NUMBER: Links to the fund (from COVERPAGE)NAMEOFISSUER: Company name of the stockCUSIP: Committee on Uniform Security Identification Procedures code (unique stock identifier)VALUE: Market value of holdings in thousands (USD)SSHPRNAMT: Number of shares held (integer)SSHPRNAMTTYPE: Share amount type (usually "SH" for shares)
Usage Example:
# Load all holdings
holdings = pd.read_csv('/root/2025-q3/INFOTABLE.tsv', sep='\t')
# Get holdings for a specific fund
fund_holdings = holdings[holdings['ACCESSION_NUMBER'] == 'specific_accession_number']
# Get count of stocks held
stock_count = len(fund_holdings)
# Find a specific stock
palantir = holdings[holdings['NAMEOFISSUER'].str.contains('PALANTIR', case=False, na=False)]
SUMMARYPAGE.tsv
Purpose: Summary-level information per fund Key Columns:
ACCESSION_NUMBER: Fund identifierTABLE_OF_CONTENTS: Summary metadata- Other aggregate information
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 148 lines · 25 tokens per session scan A 9947748547f0
sec13f-data-format is a skill published in the GitHub repository cxcscmu/SkillLearnBench (91 stars, last pushed 22d ago), licensed MIT. It adds 25 tokens to every session and 1,281 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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