2.7K skills · sorted by relevance
Introduce new users to OpenWork with explanations suited to their technical background. Cover file summaries, document creation, and organization, plus setup guidance for models, skills, plugins, and connections to external tools.
Create model architecture diagrams and method illustrations for academic papers using Gemini. Codex plans the figure and reviews generated images for correct connections, content, layout, and readability, guiding repeated revisions toward a clearer final figure.
Use Outlines to make language models produce data in a defined format. The skill covers extracting information into JSON, restricting classification labels, and applying Pydantic models, which define fields and data types, with local model backends.
Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.
Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.
Compare major AI coding tools and consult practical guidance on prompts, project instructions, debugging, workflows, and costs. Use the reference to choose suitable tools and identify why an AI-assisted coding task is stuck.
Guide code changes in iPolloWork by reusing existing implementations, limiting new files and dependencies, and keeping responsibilities clear. Review runtime costs, run focused checks, and audit the final changes for duplication and maintainability issues.
Review domain code for classes that hold data while business logic lives elsewhere. Using domain-driven design patterns, diagnose problems and refactor objects to keep business rules, state changes, and validation together, with before-and-after diffs.
Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Choose a single instance or queue workers, configure secrets and Python task runners, and follow procedures for updates, backups, and recovery.
Get started with Ouroboros through an interactive introduction that checks setup readiness and asks about your goals. Learn the core commands, then move into a project interview, a tutorial, or the help reference.
Turn mathematical modeling reports into DrawIO diagrams showing solution steps, model structures, and data processing flows. Produce editable source files, export PDFs for papers when DrawIO is available, and record diagram choices and export results.
Implements mathematical models from a design report as reproducible code, runs calculations, and checks results. It records methods and validation findings in a results report and produces data-based PDF charts for papers.
Evaluate Claude Code agents, skills, and commands with weighted quality criteria. Get scores, readiness grades, template comparisons, and practical improvement suggestions in Markdown or JSON reports to guide reviews before release.
Assess your Claude Code knowledge through multiple-choice questions with immediate feedback. Choose a quick check or a comprehensive assessment to identify gaps and receive a prioritized learning path, with practice projects included in the comprehensive mode.
Reproduce and verify Weave Router behavior locally with Docker Compose and Claude CLI. Test model routing, response conversion, and streaming against real APIs or a mock service, then inspect logs without changing global Claude Code settings.
Test chatbots and AI agents for injected instructions, data leaks, and unauthorized access across accounts. It emphasizes reproducible evidence, verified external requests, and real data access rather than trusting the model's claims.
Check an academic paper’s tables, numerical claims, and modeling choices against R scripts and outputs. Produce discrepancy reports, a record linking claims to code, and automated tests that rerun the analysis.
Review code that has not yet been checked by forming security hypotheses and testing them with analysis tools. The skill records evidence, checks whether affected code is reachable, searches for related patterns, and preserves review progress across sessions.
Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.
Develop healthcare prediction models from electronic health records and physiological signals. Use PyHealth to prepare data, train models for tasks such as mortality or readmission prediction, and evaluate accuracy, fairness, and how predictions can be explained.
Try NeMo Relay through a CLI wrapper, a supported integration, or direct Python, Node.js, or Rust instrumentation. Choose a minimal first run and verify recorded tool and model activity before considering one additional plugin.
Find Earth2Studio models, data sources, and examples for weather or climate tasks. Filters options by region, forecast type, and GPU memory, then checks whether data sources supply the variables required by each model using current documentation and source files.
Fine-tune a pretrained KERMT model on a CSV of molecular structures and labeled targets. The workflow checks inputs, prepares features, launches training in a container, and saves trained models, logs, and test results.
Write, review, and migrate DALI data processing code using dynamic mode, which calls operators directly from ordinary Python. Handle readers, batches, GPU processing, PyTorch conversion, random state, and checkpoints with the conventions specific to this API.