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Found 1,896 Skills
Translate TradingView PineScript strategies into vectorized Python strategies suitable for Optuna optimization and walk-forward analysis.
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
Academic backtesting framework for quantitative research. ~30 risk and performance ratios, 10 classes of indicators, event-driven engine with 6+ strategies, MPT optimizer, forward-looking simulation with Johnson SU + t-Copula, walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). All flat Python + numpy.
Pricing completo de opciones europeas y americanas. 9 metodos: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (vol estocastica, sonrisa via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM) y P(Profit). Disenado para backtesting: cada funcion es flat Python vectorizado con numpy (sin abstracciones), usa math.erfc (no scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us, Binomial N=500 5.6 ms. CLI con 15 modos mas validate y bench. Time complexity O(1) para todos los closed-form.
Write comprehensive code documentation including JSDoc, Python docstrings, inline comments, function documentation, and API comments. Use when documenting code, writing docstrings, or creating inline documentation.
Practical Python craftsmanship guidance based on One Python Craftsman. Use when writing, refactoring, or reviewing Python code for naming, branching, data structures, functions, exceptions, loops, decorators, imports, file I/O, edge cases, and modern syntax choices. If the skills set includes friendly-python, suggest invoking it for better Python outcomes.
Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, or implementing the dialectic chat endpoint for AI agents.
Decompose high-level objectives into atomic implementation tasks for Python/React projects. Use when breaking down large features, multi-file changes, or tasks requiring more than 3 steps. Produces independently-verifiable tasks with done-conditions, file paths, complexity estimates, and explicit ordering. Creates persistent task files (task_plan.md, progress.md) to track state across context windows. Does NOT cover high-level planning (use project-planner) or architecture decisions (use system-architecture).
Guides the agent through running and configuring ASGI servers (Uvicorn, Granian, Hypercorn) for Python web applications. Triggered when users say "run a FastAPI app", "configure uvicorn", "set up ASGI server", "deploy with uvicorn", "configure workers", "set up SSL/TLS", "run development server", "configure hot reload", or mention ASGI server, production deployment, server configuration, uvicorn, granian, or hypercorn.
Guides the agent through Python project management with uv, the fast Rust-based package and project manager. Triggered when users say "create a Python project", "init a Python project with uv", "add a dependency", "manage Python packages", "sync dependencies", "lock dependencies", "run a Python script", "set up pyproject.toml", or mention uv, package management, virtual environments, or Python project initialization.
Best practices for managing development environments including Python venv and conda. Always check environment status before installations and confirm with user before proceeding.
Create robust Python automation with full logging and safety checks. Use when tasks need complex data processing, authenticated API work, conditional file operations, or error handling beyond simple shell commands.