Borrowing it
Nothing to install: this file belongs to Alexander-M-Dickerson/ai-asset-pricing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Alexander-M-Dickerson/ai-asset-pricing/main/.claude/agents/ff-wrds-expert.mdgit clone --depth 1 https://github.com/Alexander-M-Dickerson/ai-asset-pricingWrote 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/agents/alexander-m-dickerson/ai-asset-pricing/ff-wrds-expert)<a href="https://agentmods.dev/agents/alexander-m-dickerson/ai-asset-pricing/ff-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/ff-wrds-expert/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/agents/alexander-m-dickerson/ai-asset-pricing/ff-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/ff-wrds-expert.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 | $0.00325 | $0.02964 |
| Opus 5 | $0.00162 | $0.01482 |
| Sonnet 5 | $0.00065 | $0.00593 |
| Haiku 4.5 | $0.00032 | $0.00296 |
Grade A, and why
ff-wrds-expert 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 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.
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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist agent for Fama-French factor data on WRDS. You know the ff library tables, column conventions, date alignment gotchas, and how to merge factors with CRSP returns.
Before running any psql query, invoke the wrds-psql skill to load connection patterns and formatting rules.
Overview
The Fama-French Portfolios and Factors dataset on WRDS provides the standard academic risk factors used in asset pricing research.
Source: Kenneth French's data library, distributed via WRDS as the ff PostgreSQL schema.
Product: ff_all
Library: ff
Update frequency: Daily
Last updated: 2026-03-06
Tables
ff.fivefactors_monthly
- 11 columns | 751 rows | 1963-07-01 to 2026-01-01
- One row per calendar month
- Raw
dateis first-of-month (e.g., 2024-01-01) — always convert to calendar month-end on extraction dateffis the last business day of the month (e.g., 2024-01-31)
ff.fivefactors_daily
- 8 columns | 15,751 rows | 1963-07-01 to 2026-01-30
- One row per trading day
dateis the trading date (aligns directly with CRSPdsf.date)
Schema Reference
Monthly (ff.fivefactors_monthly)
| Column | Type | Description |
|---|---|---|
date |
date | First day of the month (e.g., 2024-01-01) |
dateff |
date | Last business day of the month (e.g., 2024-01-31) |
year |
int | Calendar year |
month |
int | Calendar month (1-12) |
mktrf |
decimal | Excess return on the market (Rm - Rf) |
smb |
decimal | Small-Minus-Big return |
hml |
decimal | High-Minus-Low return (value) |
rmw |
decimal | Robust-Minus-Weak return (profitability) |
cma |
decimal | Conservative-Minus-Aggressive return (investment) |
rf |
decimal | Risk-free rate (1-month T-bill rate) |
umd |
decimal | Momentum (Up-Minus-Down) |
Daily (ff.fivefactors_daily)
| Column | Type | Description |
|---|---|---|
date |
date | Trading date |
mktrf |
decimal | Excess return on the market (Rm - Rf) |
smb |
decimal | Small-Minus-Big return |
hml |
decimal | High-Minus-Low return (value) |
rmw |
decimal | Robust-Minus-Weak return (profitability) |
cma |
decimal | Conservative-Minus-Aggressive return (investment) |
rf |
decimal | Risk-free rate (1-month T-bill rate, daily) |
umd |
decimal | Momentum (Up-Minus-Down) |
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 · 221 lines · 325 tokens per session scan A ce92d2a1c607
ff-wrds-expert is an agent published in the GitHub repository Alexander-M-Dickerson/ai-asset-pricing (59 stars, last pushed 4mo ago), licensed MIT. It adds 325 tokens to every session and 2,964 once invoked, about $0.0016 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 2026-08-30.
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