Plugins

2 plugins

@brycewang-stanford

Twelve-skill bundle covering the Science manuscript lifecycle: workflow router, scope/significance fit, advance framing, abstract + one-sentence summary, main-text writing, display items, statistics & reproducibility, data/materials/code availability, reference style, cover letter, submission preflight, and reviewer rebuttal.

2 skills · plugin

@brycewang-stanford

Twelve-skill bundle covering the PNAS manuscript lifecycle: workflow router, scope/significance fit, submission-track selection (Direct vs Contributed), the ≤120-word Significance Statement, ≤250-word abstract, main-text writing with in-text Materials and Methods + classification, display items, statistics & reproducibility, data/code availability, numbered reference style, submission preflight, a

9 skills · plugin

Results for “reproducibility”

15 skills

Mlops And Infra

Enforces ML infrastructure, experiment tracking, reproducibility, model packaging, CI/CD, monitoring, and infrastructure-as-code standards at principal-engineer level.

Enforces rigorous ML modeling, feature engineering, training, and evaluation standards at principal-engineer level.

More results

Discovers and executes bioinformatics tools from the Galaxy ecosystem via natural language, with multi-signal scoring, workflow templates, and reproducibility bundles.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

Nemo Rl Auto Research

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

Pytorch Patterns

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Pytorch Patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Agent Evaluation

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Scores genetic compatibility between all male-female pairings in a Genomebook generation, ranking optimal mating pairs based on heterozygosity, trait complementarity, and disease risk.

Orch Fix Defect

Reproduce a bug as a failing regression test, fix until green, review, and commit via a delegated pipeline.

vvieira010-pixel

Retrieval Practice Generator

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

Manages personal local Agent Skill files by detecting reusable material, searching for similar skills, and creating or updating SKILL.md folders after user approval.

`analysis-agent`/`task-agent`/`review-agent`: primary-Skill-selected for logs, metrics, traces, alerts, SLI/SLO, or diagnostics; never task owner; skip without signal impact.

Assesses genome, transcriptome, and protein completeness with BUSCO v6, automatically resolving the correct lineage from an organism description and generating reproducible reports.