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Research industrial AI topics such as predictive maintenance and anomaly detection. Compare papers by methods and publication venues, distinguish preprints from reviewed work, identify conflicting findings and research gaps, or build an evidence-based survey draft.

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• Researching recent predictive maintenance literature
• Comparing industrial anomaly detection methods and evaluations
• Identifying research gaps in manufacturing AI

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.

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• Finding journals that match a research topic
• Comparing candidate journals side by side
• Reviewing open access options and publication fees

Organize interview transcripts and other qualitative material into codes, or meaning labels, and broader themes. Develop coding guidelines, compare researchers’ coding decisions, and document whether additional data still introduces new findings.

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• Developing a codebook for interview analysis
• Organizing codes into themes
• Checking agreement between researchers

Build web pages, application interfaces, and components with a clear visual direction suited to their purpose and audience. It turns design choices about typography, color, layout, and motion into working frontend code with consistent visual details.

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• Building a landing page with a clear visual direction
• Designing and implementing a dashboard
• Creating styled React components

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.

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• Adding a registered Dataset class
• Creating a model driven by configuration
• Wiring registration and creation logic in a new module

Review research ethics, participant protection, and potential bias from planning through publication. The skill examines questionable research practices and study trustworthiness, then produces a structured report with evidence, risk levels, and prioritized recommendations.

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• Review consent documents before collecting data
• Screen analyses for questionable research practices
• Assess trustworthiness in qualitative research

Plan quantitative research by comparing experiments and surveys against research goals and resource limits. Get design recommendations, sample size calculations, participant recruitment strategies, and experimental materials, with an assessment of factors that could distort results.

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• Compare experimental and survey designs
• Estimate the number of participants needed
• Plan participant sampling and recruitment

Keep research progress, decisions, and pending checkpoints across sessions. Store project context locally, recover essential details when returning, and track why decisions changed so research work can resume with a clear next step.

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• Resuming research after a break
• Reviewing decisions and their rationale
• Checking pending research milestones

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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• Revising formulaic wording in an academic draft
• Editing only the discussion and conclusion
• Checking that citations and statistics survive revisions

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.

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• Validate effect sizes extracted from research papers
• Check duplicate studies and dependent results
• Assess how individual studies affect pooled results

Check academic references for existence, accurate publication details, and support for cited claims. Provides a writing-time verification workflow using WebSearch and Google Scholar, including BibTeX retrieval and clear handling of references that cannot be verified.

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• Checking references suggested by AI
• Matching authors, titles, years, and publication venues
• Confirming that a paper supports a cited claim

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.

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• Refining research questions and choosing a theoretical framework
• Planning a systematic review and approving screening criteria
• Designing a qualitative study using interviews

View a dashboard for a social science research environment with 24 specialized agents. Check configuration, project stage, human review settings, and connection readiness for paper retrieval services and other research tools.

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• Check research environment configuration
• Review project stage and human checkpoints
• Find missing API settings

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.

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• Monitor research progress in the terminal
• Show detailed checkpoint completion status
• Focus the display on memory and context health

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.

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• Implement a new feature with a consistent coding checklist
• Keep changes to existing code focused on the requested task
• Check types and security while writing code

Refine AI-assisted academic drafts into natural scholarly prose while preserving citations, statistics, and methodological details. Choose how extensively to revise and which sections to edit, then review a report comparing the original and revised text.

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• Polishing an AI-assisted academic draft
• Making minimal edits before journal submission
• Revising only the abstract and discussion

Choose quantitative or qualitative methods that fit a research question and dataset. Compare alternatives, check assumptions, generate analysis code, interpret results, and examine sensitivity, meaning how conclusions change under different analytical choices.

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• Choosing methods for a research question
• Generating analysis code in R or Python
• Planning theme identification in interview data

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.

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• Appraising studies included in a systematic review
• Checking study quality before a meta-analysis
• Assessing evidence certainty with GRADE

Extract statistical values from research PDFs for meta-analysis, which combines results across studies. Track each value’s source and confidence, prioritize review, and manage human corrections and final approval in a shared data structure.

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• Extracting statistics from research PDFs
• Prioritizing uncertain or conflicting values for review
• Tracking human corrections and final approval

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.

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• Investigating an error using logs and reproduction steps
• Finding why code broke after a recent change
• Troubleshooting common shell script errors

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.

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• Compare survey results with interview themes
• Develop survey items from interview findings
• Explain conflicting qualitative and quantitative findings

Co-write proposals, technical specifications, and decision documents through context gathering, section-by-section drafting, and revision. A fresh AI reader then tests the document to reveal unclear explanations, missing context, and contradictions before it is shared.

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• Drafting a proposal through discussion
• Organizing the structure of a technical specification
• Writing a document that explains a decision

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.

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• Write commit messages using Conventional Commits
• Organize branches and Pull Request reviews
• Resolve conflicts during merge or rebase

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.

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• Comparing research methods from different perspectives
• Reviewing a methodology section from several angles
• Retrieving papers from multiple databases in parallel