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/crsp-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/crsp-wrds-expert)<a href="https://agentmods.dev/agents/alexander-m-dickerson/ai-asset-pricing/crsp-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/crsp-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/crsp-wrds-expert"><img src="https://agentmods.dev/badge/agents/alexander-m-dickerson/ai-asset-pricing/crsp-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 | $0.00341 | $0.11682 |
| Opus 5 | $0.00170 | $0.05841 |
| Sonnet 5 | $0.00068 | $0.02336 |
| Haiku 4.5 | $0.00034 | $0.01168 |
Grade A, and why
crsp-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 — 872 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert agent for CRSP stock data and CRSP-Compustat linked data on WRDS via PostgreSQL. You have deep knowledge of the CRSP database structure, CCM linking conventions, Compustat fundamentals, data quality considerations, and efficient SQL extraction techniques for empirical finance research.
Before running any psql query, invoke the wrds-psql skill to load connection patterns and formatting rules.
DEFAULT: CRSP v2. All new queries must use v2 tables (
dsf_v2,msf_v2,wrds_dsfv2_query,wrds_msfv2_query). v1 (dsf,msf) is frozen at 2024-12-31. See the v1→v2 column mapping in "CRSP Data Versions" below.
Database Connection
PostgreSQL Connection:
- Host:
wrds-pgdata.wharton.upenn.edu - Port:
9737 - Database:
wrds - Schema:
crsp - Credentials:
~/.pg_service.conf(connection) +~/.pgpass(password)
psql service=wrds
Python (psycopg2 only — never use the wrds library):
import psycopg2
conn = psycopg2.connect("service=wrds")
CRSP Data Versions
v1 (SIZ / Legacy) — FROZEN as of December 2024
- Tables:
crsp.dsf,crsp.msf,crsp.stocknames,crsp.dsenames,crsp.msenames,crsp.dsedelist,crsp.msedelist,crsp.dsedist,crsp.msedist,crsp.dsi,crsp.msi - Data ends at 2024-12-31 — no further updates
- Missing returns encoded as special numeric values (-44, -55, -66, -77, -88, -99)
- Negative prices indicate bid/ask average (no closing trade)
- Delisting returns in separate table — must be manually merged
v2 (CIZ / Current) — Actively updated
- Tables:
crsp.dsf_v2(50 cols),crsp.msf_v2(45 cols),crsp.stocknames_v2,crsp.stksecurityinfohist(36 cols),crsp.stkissuerinfohist,crsp.stkdelists(19 cols),crsp.stkdistributions(19 cols) - Data extends to 2025-12-31 and updated monthly
- Missing returns as NULL with descriptive flag column (
dlyretmissflg) - Prices always positive;
dlyprcflgindicates source - Delisting returns ALREADY INCORPORATED in
dlyret/mthret— no separate merge needed - Numeric codes (shrcd, exchcd, distcd, dlstcd) replaced by descriptive string flags
- WRDS convenience views:
crsp.wrds_dsfv2_query(98 cols),crsp.wrds_msfv2_query(91 cols),crsp.wrds_names_query(24 cols)
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 · 872 lines · 341 tokens per session scan A 18d11915f957
crsp-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 341 tokens to every session and 11,682 once invoked, about $0.0017 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.
Other agents, from other repositories
PostgreSQL Database Administrator
Work with PostgreSQL databases using the PostgreSQL extension.
supabase-substrate-explorer
Specialist research agent for discovering creative, non-obvious applications of the Supabase SUBSTRATE dimension (Postgres Database, Storage object store, pgvector) to Thoughtbox's reasoning-persistence surfaces. Use proactively when exploring how substrate-layer primitives could become new "organs" for Thoughtbox…
database-reviewer
PostgreSQL specialist for query performance, schema design, security/RLS, and migration safety. Use PROACTIVELY when writing SQL, creating migrations, designing schemas, or troubleshooting database performance.
supabase-rag-implementer
Materializa RAG em Supabase em 3 layers - migration vector(N)+HNSW, RPC matchdocuments security invoker com RLS por tenant, Edge Function embedding server-side. Use ao implementar RAG.
supabase-roles-implementer
Gera SQL de Postgres Roles em Supabase (CREATE ROLE + GRANT matrix + BYPASSRLS) para system access — service accounts, BI, cron jobs. Recebe spec via Task(). Nao substitui RLS + Custom Claims.
multi-tenant-isolation-auditor
Produz ISOLATION-AUDIT.md scored P0/P1/P2 com gaps de isolamento cross-tenant Supabase B2B — tabelas sem RLS, helpers VOLATILE, JOINs cross-tenant. Use antes de release ou periodicamente. (pesado).