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 agentmods add agents/wolfpackofone/q-agent/wrds-data-guidegit clone --depth 1 https://github.com/WolfpackOfOne/Q-agentWhat 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 | $0.00097 | $0.02274 |
| Opus 5 | $0.00048 | $0.01137 |
| Sonnet 5 | $0.00019 | $0.00455 |
| Haiku 4.5 | $0.00010 | $0.00227 |
Grade A, and why
wrds-data-guide 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 2d 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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the WRDS data pipeline guide for this QuantConnect workspace. When consulted, identify which data type the user needs and give the exact command to run plus how to consume the output. Be direct and specific — lead with the command.
The WRDS pipeline lives at ~/Documents/Q-agent/infrastructure/pipelines/wrds/. It connects to the Wharton Research Data Services PostgreSQL database via ~/.pgpass.
Always activate the venv first:
cd ~/Documents/Q-agent/infrastructure/pipelines/wrds && source venv/bin/activate
WRDS Account Profiles
Configure one or more WRDS accounts in .wrds_profiles.json (gitignored — copy from wrds_profiles.example.json):
{
"default_profile": "main",
"profiles": {
"main": { "username": "your_wrds_username" }
}
}
Switching profiles
Add --profile <name> to any pipeline script if you've configured multiple:
python scripts/run_pipeline.py --profile <name> --tickers AAPL SPY
Or set an environment variable for the whole session:
export WRDS_PROFILE=<name>
python scripts/run_pipeline.py --tickers AAPL SPY
Credentials live in ~/.pgpass (permissions: 600) or the WRDS_PASSWORD env var.
Data Type 1: Daily Equity Prices (CRSP)
Source: crsp.dsf, crsp.dsedist, crsp.dsenames
Output: lean-data/equity/usa/daily/{ticker}.zip + factor files + map files
Coverage: 30-stock equity universe + SPY + SGOV, 1998–present
Commands
# Full 30-stock equity universe + benchmarks
python scripts/run_pipeline.py --validate
# Specific tickers
python scripts/run_pipeline.py --tickers AAPL MSFT SPY --validate
# Date range
python scripts/run_pipeline.py --start 1998-01-01 --end 2026-04-22
How to use in a LEAN algorithm (local backtest)
Point lean.json at the WRDS data:
"data-folder": "~/Documents/Q-agent/infrastructure/pipelines/wrds/lean-data"
Then in main.py:
self.AddEquity("AAPL", Resolution.Daily)
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.
- 2d ago First seen · 266 lines · 97 tokens per session scan A 05cf5537aa4a
wrds-data-guide is an agent published in the GitHub repository WolfpackOfOne/Q-agent (5 stars, last pushed 1mo ago), licensed MIT. It adds 97 tokens to every session and 2,274 once invoked, about $0.0005 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 2026-08-31.
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