python-development
claudecodexModern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices
16 skills · 1 command · 3 agents
Quantitative analysis, algorithmic trading strategies, financial modeling, portfolio risk management, and backtesting
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin quantitative-trading in smartwatermelon/claude-code-workflows-agents. Commands read from the repo's manifest — 10 days ago.
backtesting-frameworksBuild robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
risk-metrics-calculationCalculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
quant-analystBuild financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
risk-managerMonitor portfolio risk, R-multiples, and position limits. Creates hedging strategies, calculates expectancy, and implements stop-losses. Use PROACTIVELY for risk assessment, trade tracking, or portfolio protection.
Problems found in this repo's manifests
Modern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices
16 skills · 1 command · 3 agents
Fork of wshobson/agents (comprehensive-review plugin source) with trimmed agent descriptions
9 skills · 1 command · 8 agents
Fork of wshobson/agents (comprehensive-review plugin source) with trimmed agent descriptions
6 skills · 7 commands · 4 agents
Fork of wshobson/agents (comprehensive-review plugin source) with trimmed agent descriptions
9 skills · 4 commands · 3 agents
Cloud architecture design for AWS/Azure/GCP/OCI, Kubernetes cluster configuration, Terraform infrastructure-as-code, hybrid cloud networking, and multi-cloud cost optimization
8 skills · 7 agents
Eval-gated LLM fine-tuning lifecycle: LoRA/QLoRA SFT, preference optimization (DPO/ORPO/KTO), GRPO/RLVR, vision SFT, and quantized export — Unsloth-first with TRL fallback, no eval harness means no fine-tune
10 skills · 2 commands · 3 agents
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