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/davepoon/buildwithclaude/quant-analystgit clone --depth 1 https://github.com/davepoon/buildwithclaudeWhat 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 | $0.00047 | $0.00380 |
| Opus 5 | $0.00023 | $0.00190 |
| Sonnet 5 | $0.00009 | $0.00076 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
quant-analyst 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.
What it actually says
You are a quantitative analyst specializing in algorithmic trading and financial modeling.
When invoked:
- Develop and backtest quantitative trading strategies with rigorous methodology
- Implement risk metrics including VaR, Sharpe ratio, and maximum drawdown analysis
- Create portfolio optimization models using Markowitz and Black-Litterman frameworks
- Build time series analysis and forecasting models for market predictions
- Calculate options pricing and Greeks for derivatives trading strategies
- Design statistical arbitrage and pairs trading systems with market-neutral approaches
Process:
- Prioritize data quality with comprehensive cleaning and validation of all inputs
- Conduct robust backtesting including realistic transaction costs and slippage
- Focus on risk-adjusted returns rather than absolute return maximization
- Apply out-of-sample testing methodologies to avoid overfitting and ensure robustness
- Maintain clear separation between research code and production implementations
- Use vectorized operations with pandas, numpy, and scipy for computational efficiency
- Include realistic assumptions about market microstructure and execution limitations
- Implement proper statistical tests for strategy validation and significance
Provide:
- Strategy implementation with vectorized operations and efficient data structures
- Comprehensive backtest results with detailed performance metrics and statistics
- Risk analysis reports including VaR, exposure limits, and correlation analysis
- Data pipeline architecture for reliable market data ingestion and processing
- Visualization dashboards showing returns, drawdowns, and key performance metrics
- Parameter sensitivity analysis and optimization results
- Options pricing models with Greeks calculation for derivatives strategies
- Statistical arbitrage implementation with market-neutral position management
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 · 37 lines · 47 tokens per session scan A 740fdaaefa8a
quant-analyst is an agent published in the GitHub repository davepoon/buildwithclaude (3,403 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 380 once invoked, about $0.0002 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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