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 skills add mitchellbernstein/openquant --skill openquant-tradinggit clone --depth 1 https://github.com/mitchellbernstein/openquantWrote 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/skills/mitchellbernstein/openquant/openquant-trading)<a href="https://agentmods.dev/skills/mitchellbernstein/openquant/openquant-trading"><img src="https://agentmods.dev/badge/skills/mitchellbernstein/openquant/openquant-trading/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/skills/mitchellbernstein/openquant/openquant-trading"><img src="https://agentmods.dev/badge/skills/mitchellbernstein/openquant/openquant-trading.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 | $0.00025 | $0.02128 |
| Opus 5.5 | $0.00010 | $0.00851 |
| Sonnet 5.5 | $0.00005 | $0.00426 |
| Haiku 4.5 | $0.00003 | $0.00213 |
Grade A, and why
openquant-trading 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 1mo 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenQuant Trading Skill
When This Skill Activates
Activate this skill when the user:
- Asks about a stock or ticker (e.g., "What do you think of AAPL?")
- Wants trading signals or strategy recommendations
- Mentions insider trading, risk, VaR, or position sizing
- Wants to check their portfolio or execute a trade
- Asks about backtesting or strategy validation
- Uses terms like "analyze", "signal", "backtest", "Kelly", "insider buy"
Prerequisites
The OpenQuant MCP server must be running. Start it:
pip install openquant-cli[mcp]
openquant-mcp
MCP endpoint: http://localhost:8000/sse (SSE transport)
Server name: openquant
Common Workflows
Workflow 1: Analyze a Stock
Use when: User asks about a ticker or wants a stock opinion.
- Call
openquant_analyze(ticker="SYMBOL", days=90) - Review the result: current_price, insider score/label, risk level, VaR, max drawdown
- If insider score is interesting (|score| > 25), call
openquant_insider_scan(ticker="SYMBOL")for detail - Summarize for the user: price, insider sentiment, risk level, and whether it's worth deeper investigation
- Do NOT execute any trades — this is research mode
Workflow 2: Get a Trading Signal
Use when: User wants a buy/sell recommendation for a ticker.
- Call
openquant_strategy_list()if user doesn't specify a strategy - Call
openquant_strategy_run(strategy_name="STRATEGY", ticker="SYMBOL") - Review: action (BUY/SELL/HOLD), confidence (0-100), entry_price, stop_loss, take_profit, position_size_pct, reasoning
- If confidence < 40: tell the user the signal is weak, suggest monitoring
- If confidence >= 40: present the signal with all details but do NOT execute without confirmation
Workflow 3: Validate a Strategy
Use when: User wants to know if a strategy works before using it.
- Call
openquant_backtest(strategy_name="STRATEGY", ticker="SYMBOL", days=252) - Review: total_trades, win_rate, total_return, max_drawdown, sharpe_ratio, avg_holding_days
- Flag concerns: win_rate < 50%, max_drawdown > 20%, sharpe < 0.5
- Present the backtest results with interpretation
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.
- 1mo ago First seen · 171 lines · 25 tokens per session scan A 711a3351ece1
openquant-trading is a skill published in the GitHub repository mitchellbernstein/openquant (2 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 2,128 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
risk-management
VKKM Aegis — Claude Plugin Master Prompt (v5.0.0 — Advanced Finance Edition).
vkkm-aegis
VKKM Aegis — Claude Plugin Master Prompt (v5.0.0 — Advanced Finance Edition).
escalation-report
/vkkm:escalation-report — Risk Escalation Memo Writer.
counterparty-profile
/vkkm:counterparty-profile — Counterparty Risk Profiler.
credit-risk
Command Spec: /vkkm:credit-risk.
kri-dashboard
Command Spec: /vkkm:kri-dashboard.