Present comparisons, trends, and progress as interactive cards, tables, and charts inside Codeg chat. Create layouts with tabs, checklists, and simple forms that update displayed state and validate user input.
Plan experiments before collecting data by choosing how to assign samples, combine conditions, and order runs. Generate reproducible allocation schedules and experimental layouts while checking for design flaws that could make results difficult to interpret.
Assess how well scientific evidence supports a claim. Examine study design, statistical methods, bias, and alternative explanations, then explain supported conclusions, uncertainties, and improvements using established frameworks for judging evidence quality.
Review scientific manuscripts and grant proposals using structured checklists. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize findings into a balanced report with major concerns, minor comments, and specific suggestions for revision.
Assign independent bugs or test failures to separate agents that work at the same time. Define each agent’s scope and expected report, then check for conflicting changes and run the full test suite before integrating fixes.
Estimate study sample sizes and statistical power, the chance of detecting a specified effect. Calculate detectable effects for a fixed sample, compare assumptions with power curves, and document adjustments for dropout, multiple comparisons, or clustered observations.
Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.
Carry out a written implementation plan in a separate session, reviewing it before starting and tracking each task through verification. The workflow pauses for blockers and hands completed work to a development branch finishing process.
Request a focused code review after implementation or before merging. Give a separate reviewer agent the requirements, change summary, and commit range, then address findings according to their severity before continuing.
Find academic papers, citation links, and openly available full text using suitable sources among ten literature APIs. Results include search parameters, identifiers, and access dates so readers can repeat the lookup and understand gaps in coverage.
Explore research ideas through conversation, question assumptions, and connect approaches from different fields. Develop a broad set of possibilities, then assess promising directions and outline next steps such as pilot experiments or literature searches.
Evaluate code review feedback against the actual code, supported environments, and existing behavior. Clarify uncertain requests, respond with technical evidence, and implement valid changes one at a time with tests for each fix.
Finish completed development work by checking the full test suite and presenting integration choices: merge locally, create a Pull Request, or keep the branch. It carries out the selected path and handles workspace cleanup accordingly.
Turn specifications into detailed implementation plans before coding. Break work into testable tasks with exact file paths, code, test commands, expected results, and interfaces so another developer or agent can carry out the work.
Turn development ideas into clear designs through guided discussion. The skill clarifies goals, requirements, and constraints, then adapts the process to a feasibility investigation, a small existing-code change, or a larger architectural design.
Create, revise, and check agent skills by testing how agents behave before and after receiving the instructions. Use observed failures to improve SKILL.md, clarify usage conditions, and close gaps before deployment.
Establish a routine for checking and invoking relevant skills before answering or taking action. It prioritizes skills that define the workflow, then guides the agent toward task-specific skills and any checklists they contain.
Turn written descriptions into scientific diagrams, including neural network architectures, biological pathways, and research flowcharts. Nano Banana 2 generates images, while Gemini 3.1 Pro Preview reviews them and guides regeneration when needed.
Prepare research data figures for journal submission with consistent layouts, error bars showing uncertainty, and accessible colors. Adjust figure dimensions, typography, resolution, and export formats using matplotlib, seaborn, and plotly.
Analyze research data with guidance on choosing statistical tests, checking their assumptions, and measuring effect sizes, meaning the magnitude of differences or relationships. It also supports sample size planning, Bayesian analysis, and APA-style reports.
Prepare a separate workspace for feature development using platform tools or git worktree, which gives a branch its own working directory. It checks existing isolation, prepares dependencies, and runs tests before implementation begins.
Build testable scientific hypotheses from observations or preliminary data. Compare competing explanations against published evidence, define measurable predictions, and propose experiments, then organize the findings into a research report with diagrams and detailed supporting material.
Carry out an implementation plan by assigning independent tasks to separate agents. The workflow includes specification checks, code quality reviews, fixes, a persistent progress record, and a final review of the entire branch.
Investigate bugs, failing tests, and unexpected behavior before proposing changes. Follow four stages to gather evidence, compare working examples, test a specific cause, and verify a focused fix with a test that reproduces the problem.
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