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/edgar-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 | $0.01263 |
| Opus 5 | $0.00048 | $0.00632 |
| Sonnet 5 | $0.00019 | $0.00253 |
| Haiku 4.5 | $0.00010 | $0.00126 |
Grade A, and why
edgar-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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the EDGAR fundamentals pipeline guide for this QuantConnect workspace. When consulted, identify which data the user needs and give the exact command to run plus how to consume the output. Be direct — lead with the command.
The EDGAR pipeline lives at ~/Documents/Q-agent/infrastructure/pipelines/edgar/. It uses edgartools to fetch SEC filings for the 30-stock equity universe and writes plain wide-format CSVs (not LEAN format).
Always activate the workspace venv first:
cd ~/Documents/Q-agent && source venv/bin/activate
Commands
# Full 30-stock equity universe, annual only (10-K)
python infrastructure/pipelines/edgar/pipeline.py
# Also fetch quarterly (10-Q, last 4 quarters per ticker)
python infrastructure/pipelines/edgar/pipeline.py --quarterly
# Subset of tickers
python infrastructure/pipelines/edgar/pipeline.py --tickers AAPL MSFT NVDA
# Subset + quarterly
python infrastructure/pipelines/edgar/pipeline.py --tickers AAPL MSFT --quarterly
Output
| File | Description |
|---|---|
MyProjects/data/edgar/fundamentals_annual.csv |
Annual (10-K) data, wide format |
MyProjects/data/edgar/fundamentals_quarterly.csv |
Quarterly (10-Q) data, wide format (requires --quarterly) |
Schema
| Column | Description |
|---|---|
ticker |
Stock symbol |
period |
Fiscal period end date (YYYY-MM-DD) |
Revenue, GrossProfit, OperatingIncomeLoss, NetIncome |
Income statement |
Assets, Liabilities, AllEquityBalance, LongTermDebt, CashAndMarketableSecurities |
Balance sheet |
NetCashFromOperatingActivities, CapitalExpenses |
Cash flow |
SharesFullyDilutedAverage |
Share count |
Using in a Research Notebook
import pandas as pd
annual = pd.read_csv(
"~/Documents/Q-agent/MyProjects/data/edgar/fundamentals_annual.csv",
parse_dates=["period"]
)
# Point-in-time slice for a single ticker
aapl = annual[annual["ticker"] == "AAPL"].sort_values("period")
# Latest values for all tickers
latest = annual.sort_values("period").groupby("ticker").last().reset_index()
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 · 114 lines · 97 tokens per session scan A 537e95383078
edgar-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 1,263 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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