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 eric861129/SKILLS_All-in-one --skill hedgefundmonitorgit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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/eric861129/skills_all-in-one/hedgefundmonitor)<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/hedgefundmonitor"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/hedgefundmonitor/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/eric861129/skills_all-in-one/hedgefundmonitor"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/hedgefundmonitor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00123 | $0.01707 |
| Opus 5 | $0.00062 | $0.00853 |
| Sonnet 5 | $0.00025 | $0.00341 |
| Haiku 4.5 | $0.00012 | $0.00171 |
Grade A, and why
hedgefundmonitor scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(f"{BASE}/series/dataset") This is a copy
100% identical to hedgefundmonitor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OFR Hedge Fund Monitor API
Free, open REST API from the U.S. Office of Financial Research (OFR) providing aggregated hedge fund time series data. No API key or registration required.
Base URL: https://data.financialresearch.gov/hf/v1
Quick Start
import requests
import pandas as pd
BASE = "https://data.financialresearch.gov/hf/v1"
# List all available datasets
resp = requests.get(f"{BASE}/series/dataset")
datasets = resp.json()
# Returns: {"ficc": {...}, "fpf": {...}, "scoos": {...}, "tff": {...}}
# Search for series by keyword
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"})
results = resp.json()
# Each result: {mnemonic, dataset, field, value, type}
# Fetch a single time series
resp = requests.get(f"{BASE}/series/timeseries", params={
"mnemonic": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN",
"start_date": "2015-01-01"
})
series = resp.json() # [[date, value], ...]
df = pd.DataFrame(series, columns=["date", "value"])
df["date"] = pd.to_datetime(df["date"])
Authentication
None required. The API is fully open and free.
Datasets
| Key | Dataset | Update Frequency |
|---|---|---|
fpf |
SEC Form PF — aggregated stats from qualifying hedge fund filings | Quarterly |
tff |
CFTC Traders in Financial Futures — futures market positioning | Monthly |
scoos |
FRB Senior Credit Officer Opinion Survey on Dealer Financing Terms | Quarterly |
ficc |
FICC Sponsored Repo Service Volumes | Monthly |
Data Categories
The HFM organizes data into six categories (each downloadable as CSV):
- size — Hedge fund industry size (AUM, count of funds, net/gross assets)
- leverage — Leverage ratios, borrowing, gross notional exposure
- counterparties — Counterparty concentration, prime broker lending
- liquidity — Financing maturity, investor redemption terms, portfolio liquidity
- complexity — Open positions, strategy distribution, asset class exposure
- risk_management — Stress test results (CDS, equity, rates, FX scenarios)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 129 lines · 123 tokens per session scan A 075de626ca4d
hedgefundmonitor is a skill published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 4mo ago), licensed MIT. It adds 123 tokens to every session and 1,707 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to hedgefundmonitor, differing in 0 lines, and is treated as a copy.
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