Investigates existing code before feature work, bug fixes, or refactoring. Finds similar implementations, reusable helpers, naming rules, and data handling patterns so proposed changes follow the project's established conventions.
Review Markdown documentation changes against the Metabase writing style guide. Identify meaningful problems in tone, structure, links, formatting, and examples, then provide numbered feedback with suggested fixes in conversation or GitHub review comments.
Turn development errors and debugging attempts into a structured Chinese report covering causes, fixes, and prevention steps. Create a Git commit that references the report, and update an index for later review.
Turn feature ideas and behavior changes into agreed designs before implementation. Clarify goals through one question at a time, compare alternative approaches, review the design in sections, and save a design document before moving to implementation planning.
Draft case reports, diagnostic reports, clinical trial reports, and patient records using document-specific structures and templates. The skill guides completeness checks, patient de-identification, and documentation aligned with named clinical reporting guidelines and regulatory frameworks.
Clean up AI-written code by reviewing branch changes against main. Remove unnecessary comments, excessive defensive checks, and casts that bypass type issues, while preserving legitimate changes and matching the surrounding code style.
Turn analytical findings into reports and presentations that explain what the numbers mean and what to do next. Structure the narrative, choose clear visual comparisons, highlight key findings, and communicate uncertainty to audiences without technical expertise.
Learn how to organize the instructions, conversation history, documents, and tool results an AI agent receives. Explains context limits and loading information as needed, helping you review agent designs and investigate behavior caused by poorly managed context.
Create charts from existing CSV files, tables, or query results to show trends, distributions, comparisons, and relationships. The skill guides chart selection and axis choices for everyday analysis, presentation slides, and simple dashboards.
Read, create, and edit .docx documents with python-docx, then render pages for visual inspection. Organize headings, tables, and lists, and check spacing, alignment, pagination, and readability before delivering the finished document.
Organize content ideas, contacts, meeting history, and goals in structured files. Use saved writing preferences and past posts to draft consistent content, prepare meeting briefs, and review weekly progress across publishing and relationships.
Build and run genomics analyses on DNAnexus, a cloud platform for biomedical data. Covers analysis apps, file organization, multi-step workflows, job monitoring, and automation with the dxpy Python library.
Study the mathematics behind computer science through a structured learning path and practice problems. Covers logic, proofs, sets, functions, counting, and number theory, connecting these foundations to algorithm correctness and computational thinking.
Analyze time series, observations ordered over time, using aeon in Python. Build classification, prediction, and clustering workflows, detect unusual patterns or changes, and compare methods through an interface compatible with scikit-learn.
Review code or pull requests for likely bugs, readability, maintainability, and risks to existing behavior. Supporting scripts and reference checklists help analyze code quality, identify missing tests, and organize findings into a review report.
Helps process astronomical data in Python by converting coordinates, units, and time scales, working with FITS observation files, matching object catalogs, and calculating distances using cosmological models.
Create original generative art with p5.js, using code to produce patterns and motion. Receive a written artistic concept and an interactive HTML viewer with adjustable parameters, reproducible random variations, seed navigation, and PNG downloads.
Build automated data checks with Great Expectations and dbt to detect missing values, duplicates, invalid ranges, and stale records. Define data contracts, shared agreements on data structure and quality, and summarize validation results across tables.
Plan experiments and quasi-experiments before analyzing data. Define treatment and comparison groups, choose outcomes, assess design assumptions, and specify follow-up validation when results are weak or contradictory, including rules for continuing, revising, or stopping.
Work with DNA, RNA, and protein sequences in Python. Read and convert biological files, compare sequences, search for similar sequences with BLAST, retrieve NCBI records, analyze protein structures, and build or visualize evolutionary trees.
Retrieve population, economic, health, and environmental statistics through the Data Commons Python API. Resolve place names into query identifiers, compare regions, explore historical trends, and convert results into Pandas tables for further analysis.
Cancel the local compatibility-mode state used by Ralph loops in Codex. The bundled PowerShell script removes active state and reports the current iteration, or reports that no active loop state was found.
Learn programming through tutorials tailored to your experience, goals, and actual project code. Keep a shared learning history across projects, record questions, and use quizzes with spaced review to revisit concepts based on demonstrated understanding.
Compare viable design approaches through different perspectives by routing to vibe’s Dialectic Mode. Receive a concise account of shared conclusions, disagreements and their reasons, overlooked issues, and a recommended combined approach.
Is this your account? Link GitHub to bring it onto your creator page (Japanese)