Benchmarking

Benchmarking skills and workflows surfaced by the site skill importer.

8 skills
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qiskit

by K-Dense-AI

qiskit is an IBM quantum computing skill for building circuits, choosing backends, transpiling for hardware, and running jobs on simulators or IBM Quantum devices. It is a strong fit for qiskit usage in chemistry, optimization, and machine learning, especially when you need practical install-and-run guidance rather than a theory-only qiskit guide.

Scientific
Favorites 0GitHub 21.3k
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huggingface-best

by huggingface

The huggingface-best skill helps you find the best model for a task by checking Hugging Face benchmark leaderboards and filtering by device limits and model size. Use it for model recommendations in coding, reasoning, chat, OCR, RAG, speech, vision, or multimodal work when you need a practical shortlist, not a generic model list.

Model Evaluation
Favorites 0GitHub 10.4k
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libafl

by trailofbits

The libafl skill helps you plan and build modular fuzzers with LibAFL for custom targets, mutation strategies, and security audit workflows. Use this libafl guide to move from target details to a practical harness, feedback model, and run plan with fewer assumptions.

Security Audit
Favorites 0GitHub 5k
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skill-optimizer

by mcollina

skill-optimizer helps authors improve AI skills for activation, clarity, and cross-model reliability. Use it for Skill Authoring when a skill is written but not reliably followed, when triggers are weak, regressions appear, or context cost needs trimming. It supports benchmark loops, release gates, and tighter usage fidelity.

Skill Authoring
Favorites 0GitHub 1.8k
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pytdc

by K-Dense-AI

pytdc is a skill for Therapeutics Data Commons, giving AI-ready drug discovery datasets and benchmarks for ADME, toxicity, DTI, DDI, generation, scaffold splits, and pharmacological prediction.

Data Analysis
Favorites 0GitHub 0
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pytorch-lightning

by K-Dense-AI

pytorch-lightning skill for organizing PyTorch projects with LightningModules and Trainers. Use this pytorch-lightning guide for install, training, validation, logging, checkpointing, and distributed execution across multi-GPU or TPU workflows.

Backend Development
Favorites 0GitHub 0
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pymoo

by K-Dense-AI

pymoo is a Python skill for single- and multi-objective optimization, Pareto fronts, constrained problems, and benchmark tests. Use this pymoo guide to choose algorithms like NSGA-II, NSGA-III, and MOEA/D, follow the install and usage workflow, and apply pymoo for Data Analysis when multiple metrics must be balanced.

Data Analysis
Favorites 0GitHub 0
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finance-metrics-quickref

by deanpeters

finance-metrics-quickref is a fast lookup skill for SaaS finance metrics, formulas, and benchmarks. Use it for quick metric definitions, formula checks, and benchmark reminders during product, finance, GTM, or board review work.

Finance
Favorites 0GitHub 0
Benchmarking