prompt-optimizer
Transforms vague or poorly structured prompts into optimized, high-performance instructions using proven prompt engineering principles for better AI model execution.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
135 skills found
Transforms vague or poorly structured prompts into optimized, high-performance instructions using proven prompt engineering principles for better AI model execution.
Production-ready reinforcement learning using Stable Baselines3. Train agents, design custom environments, implement training callbacks, and optimize workflows with a scikit-learn-style API.
Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
Train and manage neural networks in distributed E2B sandboxes using the Flow Nexus platform, supporting custom architectures like Transformers, LSTMs, and GANs.
Optimize Apache Spark jobs with partitioning strategies, memory management, shuffle tuning, and data skew mitigation for high-performance data processing pipelines.
Expert assistant for designing and optimizing production-grade Trigger.dev background jobs, AI workflows, and resilient asynchronous task architectures in TypeScript.
Nonlinear optimization toolkit using CasADi and IPOPT. Ideal for building complex NLP models, defining symbolic variables, constraints, and solvers, with specialized support for power systems optimization patterns.
Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.
End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.
BLS periodogram tool for detecting transiting exoplanets and eclipsing binaries in photometric light curves. An astropy-based implementation for period, duration, and depth analysis.
Normalizes testing defect logs by correcting typos, abbreviations, and ambiguous descriptions based on product-specific codebooks and station validation.
Migrate your codebase, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to the advanced Opus 4.5 model with automated configuration adjustments.