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 sec-13f-analysisgit 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/sec-13f-analysis)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/sec-13f-analysis"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/sec-13f-analysis.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00031 | $0.00541 |
| Opus 5 | $0.00015 | $0.00270 |
| Sonnet 5 | $0.00006 | $0.00108 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
sec-13f-analysis 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 3d 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.
What it actually says
SEC 13-F Analysis Skill
Overview
SEC Form 13-F datasets consist of TSV files downloaded from EDGAR. Each quarter has the same file structure.
Key Files
| File | Purpose |
|---|---|
COVERPAGE.tsv |
Fund identity: FILINGMANAGER_NAME, ACCESSION_NUMBER, REPORTCALENDARORQUARTER |
SUMMARYPAGE.tsv |
Aggregated stats: TABLEVALUETOTAL (AUM in thousands), TABLEENTRYTOTAL (number of holdings) |
INFOTABLE.tsv |
Individual holdings: CUSIP, NAMEOFISSUER, VALUE (thousands), SSHPRNAMT (shares) |
SUBMISSION.tsv |
Filer metadata |
Common Tasks
Load data with pandas
import pandas as pd
q3_dir = "/root/2025-q3"
coverpage = pd.read_csv(f"{q3_dir}/COVERPAGE.tsv", sep="\t", dtype=str)
summarypage = pd.read_csv(f"{q3_dir}/SUMMARYPAGE.tsv", sep="\t", dtype=str)
infotable = pd.read_csv(f"{q3_dir}/INFOTABLE.tsv", sep="\t", dtype=str)
Get AUM for a fund (by accession_number)
row = summarypage[summarypage["ACCESSION_NUMBER"] == accession_number]
aum_thousands = int(row["TABLEVALUETOTAL"].iloc[0])
aum_dollars = aum_thousands * 1000
Get holdings count
holdings = infotable[infotable["ACCESSION_NUMBER"] == accession_number]
num_holdings = len(holdings)
# Or use TABLEENTRYTOTAL from SUMMARYPAGE for the reported count
Get holdings detail
holdings = infotable[infotable["ACCESSION_NUMBER"] == accession_number].copy()
holdings["VALUE"] = pd.to_numeric(holdings["VALUE"], errors="coerce")
holdings["SSHPRNAMT"] = pd.to_numeric(holdings["SSHPRNAMT"], errors="coerce")
Notes
VALUEin INFOTABLE is in thousands of USDTABLEVALUETOTALin SUMMARYPAGE is also in thousands of USDTABLEENTRYTOTALis the number of positions reported
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.
- 3d ago First seen · 56 lines · 31 tokens per session scan A 7ad06635e1d8
sec-13f-analysis is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 541 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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