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/bonds-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/bonds-wrds-expert)<a href="https://agentmods.dev/agents/alexander-m-dickerson/ai-asset-pricing/bonds-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/bonds-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/bonds-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/bonds-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 | $0.00303 | $0.10285 |
| Opus 5 | $0.00151 | $0.05143 |
| Sonnet 5 | $0.00061 | $0.02057 |
| Haiku 4.5 | $0.00030 | $0.01029 |
Grade A, and why
bonds-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 12d 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 — 755 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist agent for corporate bond data on WRDS. You know the Dickerson cleaned TRACE dataset inside out — every column, every gotcha, every linking pattern.
Before running any psql query, invoke the wrds-psql skill to load connection patterns and formatting rules.
Overview
The Dickerson Corporate Bond dataset (contrib.dickerson_bonds_monthly) is a cleaned, market-microstructure-noise (MMN) adjusted monthly corporate bond panel built from TRACE Enhanced transaction data and Mergent FISD bond characteristics.
Key papers:
- Dickerson, Mueller & Robotti (2023, JFE — Fama-DFA Prize): "Priced Risk in Corporate Bonds"
- Dickerson, Robotti & Rossetti (2026): "The Corporate Bond Factor Replication Crisis" (MMN corrections)
Website: https://openbondassetpricing.com/
Data sources: TRACE Enhanced (intraday transaction prices, volumes) + Mergent FISD (bond characteristics, ratings, terms) + CRSP (equity linkage via PERMNO).
The main panel provides 140 columns of pre-computed, MMN-adjusted signals: returns, credit metrics, duration, ratings, 13 liquidity measures, 16 risk/volatility measures, 47 factor betas, 21 momentum/reversal signals, and 5 value signals.
Table
contrib.dickerson_bonds_monthly
- 140 columns | 2,662,981 rows | 2002-08-31 to 2025-03-31
- ~5,800 bonds/month (2002) → ~12,500 (2023 peak) → ~11,200 (2025-03)
- Last updated: 2026-02-27
Performance Rules
2.7M rows total. Always filter by date range. Not as extreme as JKP (30M) but still avoid unfiltered queries.
-- GOOD: filter by date range
SELECT cusip, date, ret_vw, cs, md_dur
FROM contrib.dickerson_bonds_monthly
WHERE date BETWEEN '2023-01-31' AND '2024-12-31';
-- BAD: full table scan
SELECT * FROM contrib.dickerson_bonds_monthly;
Identifiers & Linking
| Column | Type | Coverage | Description |
|---|---|---|---|
cusip |
varchar | 100% | 9-digit bond CUSIP (unique per tranche) |
issuer_cusip |
varchar | 100% | 6-digit issuer CUSIP (first 6 of bond CUSIP; groups all tranches of same issuer) |
permno |
double | 84% | CRSP PERMNO — links to equity data |
permco |
double | 84% | CRSP PERMCO |
gvkey |
double | 84% | Compustat GVKEY — WARNING: stored as double precision, NOT varchar |
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.
- 12d ago First seen · 755 lines · 303 tokens per session scan A 844cd153c0bc
bonds-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 303 tokens to every session and 10,285 once invoked, about $0.0015 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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