Build Claude Code plugin hooks: automated checks triggered by tool use or session events. Get guidance on prompt and command hooks for validating actions, checking task completion, loading context, and testing configurations.
Configure a terminal status display for Diverga research projects. Show the project name, research stage, checkpoint progress, and memory health, choose among four display presets, and set up or repair the Claude Code integration.
Compare qualitative research designs for studying experiences, stories, cultures, or developing theory. Receive three options matched to the research question and available resources, plus a plan for participant selection, data collection, analysis, and quality checks.
Coordinate multiple AI agents in Diverga for parallel research, reviews, and methodology comparisons. Manage team creation, progress, and results, or run VS Arena debates where different research perspectives critique recommendations before the user chooses an approach.
Coordinate a seven-stage literature review, from defining a research question to collecting and screening papers, building a searchable knowledge database, and producing PRISMA documentation. Specialized agents handle execution, with human approval at key decisions.
Diagnose a Diverga installation by checking plugin registration, skill and cache synchronization, hooks, configuration, API key setup, and research project state. Receive pass, warning, or error results with suggested repair commands, without modifying files.
Design studies that combine quantitative data, such as surveys, with qualitative evidence, such as interviews. It recommends a suitable design and outlines study phases, participant selection, timing, and ways to combine findings into an integrated interpretation.
Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.
Plan interviews and focus groups for qualitative research, which explores people’s experiences and views. Develop question guides, follow-up prompts, discussion plans, transcription conventions, and participant review procedures suited to the study.
Guide team Git work with consistent commit messages, branch naming, and review practices. Follow concrete steps for everyday development, urgent fixes, releases, merge conflicts, and version tags, including guidance on when to use merge or rebase.
Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.
Coordinate meta-analysis, which combines results across studies, from data extraction to reporting. Validate effect sizes, check for duplicates and implausible values, and assess how individual studies or publication bias affect the combined result.
Find and compare academic journals using a research abstract, field, and publication priorities. The skill combines topic fit, citation metrics, open access options, and practical considerations into a comparison report, sequential submission plan, and cover letter template.
Add datasets, models, and other components to machine learning projects using consistent registration and creation patterns. The skill guides file placement, configuration, and automatic module loading so new components fit the project's established architecture.
Coordinate research from question development through literature searches, analysis, and publication with specialized agents. Supports quantitative, qualitative, and mixed methods studies, plus systematic reviews, with human approval at key decisions about research direction and methods.
Learn from top Kaggle solutions across text, images, time series, tabular data, and multimodal tasks that combine data types. Explore technical analyses, model settings, reusable code templates, and practical lessons to inform machine learning projects.
Combine interview themes and numerical findings into comparison tables and integrated conclusions. The skill helps select an integration approach, explain agreements and contradictions, and assess whether conclusions are supported by both kinds of evidence.
Review code changes and pull requests for correctness, security, performance, and test quality. The skill helps prioritize issues, write specific and constructive feedback, and create shared review checklists and standards for a development team.
Assess study quality and risk of bias, meaning systematic distortions in research results. Choose assessment tools suited to the study and review purpose, document judgment rationales, and summarize evidence certainty and recommendations for use in further analysis.
Investigate code errors through a structured process using reproduction steps, logs, and testable explanations. The workflow guides fault isolation, fixes, and follow-up checks, with common error patterns for Python, JavaScript/TypeScript, and Bash/Zsh.
Extract readable Markdown from web pages with Defuddle CLI, removing navigation, ads, and other clutter to reduce token usage. Save the extracted content to a file or retrieve individual details such as the title, description, and domain.
Apply a compact checklist to everyday code changes. Read existing files first, keep edits focused, check types and security, verify execution, remove debugging leftovers, and summarize what changed and its impact.
Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.
Revise formulaic academic prose through staged vocabulary and structure edits while checking that citations, statistics, and technical terms remain intact. Choose sections and editing intensity, compare changes, and review progress before accepting the revised manuscript.
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